{"paper_id":"301a2e99-2bd3-49a7-b4da-983aedd66499","body_text":"A bibliometric review on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A bibliometric review on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning Florieza Mangubat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7370235/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a comprehensive bibliometric analysis of the evolving role of Artificial Intelligence (AI) in healthcare, with a specific focus on diagnostics, drug discovery, personalized medicine, and treatment planning. Drawing upon data from the Scopus database between 2021 and 2025, the research examines 48 scholarly publications sourced from 47 journals, conference proceedings, and book chapters. The analysis aims to uncover prevailing research trends, collaboration patterns, thematic developments, and key concerns surrounding the integration of AI into critical healthcare domains. Findings reveal a significant surge in scientific production over the past five years, with an annual growth rate exceeding 120%, indicating heightened global interest in AI-driven healthcare solutions. Despite the rising volume of publications, the average number of citations per article showed a declining trend, highlighting the saturation of the field and a shift from foundational to more applied research. Thematic mapping and keyword analysis identified core research clusters centered on AI technologies such as machine learning, deep learning, and natural language processing applied to oncology diagnostics, clinical decision-making, and precision medicine. Emerging ethical themes, such as data privacy, algorithmic bias, transparency, and explainable AI, also surfaced, reflecting the growing interdisciplinary engagement. Geographically, countries such as India, the United States, Australia, and the United Kingdom lead in publication output, although international collaboration remains uneven, with many contributions being single-country efforts. Notably, citation impact does not always align with productivity, as evidenced by countries such as the UK and Finland, which have demonstrated high citation rates despite lower publication volumes. Visualization tools, such as VOSviewer and Bibliometrix, revealed an increasingly dense and diversified research landscape, with intellectual structures that bridge technical AI development, ethical governance, and healthcare implementation. While AI’s integration into healthcare shows remarkable progress, the study identifies challenges in equitable collaboration, responsible innovation, and ensuring meaningful societal impact. The bibliometric insights offer valuable guidance for researchers, policymakers, and funders, emphasizing the need for interdisciplinary approaches, global cooperation, and ethical oversight to responsibly advance AI’s transformative potential in healthcare. Concerns on AI Drug discovery Personalized medicine Treatment planning Bibliometric review Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction Artificial Intelligence (AI) technologies have increasingly become integral to modern healthcare, supporting clinicians and researchers across critical domains. In diagnostics, AI-driven tools such as machine learning algorithms, computer vision, and natural language processing (NLP) are used to interpret medical imaging, pathology slides, and electronic health records (EHRs), enabling earlier and more accurate disease detection [ 1 , 2 , 3 , 4 ] AI models assist in diagnosing conditions ranging from cancers to rare diseases, often outperforming traditional diagnostic techniques in speed and precision [ 5 , 6 , 7 ]. In drug discovery, AI accelerates the identification of potential therapeutic compounds, predicts drug-target interactions, and streamlines the design of clinical trials [ 8 , 9 ]. Techniques such as deep learning and reinforcement learning enable researchers to analyze vast datasets of chemicals and biology, resulting in reduced development timelines and costs [ 10 , 11 ]. Personalized medicine benefits from AI’s ability to analyze genomic, proteomic, and clinical data to create individualized treatment plans [ 12 , 13 , 14 ]. AI models predict patient responses to treatments, enabling the development of optimized therapeutic strategies tailored to individual genetic profiles, disease subtypes, and lifestyle factors [ 15 ]. In treatment planning, AI aids in optimizing clinical decisions through predictive analytics, decision support systems, and robotic interventions [ 16 , 17 , 18 ]. AI algorithms support evidence-based recommendations in oncology, cardiology, and other specialties, helping healthcare providers devise treatment regimens that maximize efficacy while minimizing adverse effects [ 19 , 20 , 21 , 22 ]. AI is poised to reshape healthcare systems worldwide by enhancing diagnostic accuracy, accelerating drug development, personalizing treatment, and improving clinical workflows. AI addresses many of the traditional challenges in healthcare delivery. Its applications reduce diagnostic errors, shorten the time from research to treatment, and enable more equitable care through scalable digital tools [ 23 , 24 , 25 , 26 ]. It also plays a significant role in addressing workforce shortages by automating routine tasks, supporting clinical decision-making, and expanding access to specialist-level care in under-resourced settings [ 27 , 28 , 29 ]. Moreover, AI-driven predictive models improve population health management by identifying at-risk patients earlier, enabling preventive interventions [ 30 , 31 ]. However, the transformative potential of AI in healthcare is not without challenges [ 32 , 33 , 34 ]. Issues such as data privacy, algorithmic bias, regulatory uncertainty, and technological interoperability must be addressed to ensure the responsible adoption of AI [ 35 , 36 , 37 ]. Despite these concerns, AI remains central to the evolution of more efficient, accurate, and patient-centered healthcare systems. In the rapidly evolving field of artificial intelligence (AI) in healthcare, bibliometric analysis serves as a valuable tool for systematically mapping scientific progress [ 38 , 39 , 40 ]. By quantitatively analyzing published literature, bibliometric methods provide objective insights into research productivity, influence, and focus areas over time. This type of analysis helps uncover not only the volume of research but also its underlying patterns, such as emerging technologies, popular applications, and recurring concerns in diagnostics, drug discovery, personalized medicine, and treatment planning. Bibliometric analysis enables researchers and policymakers to identify thought leaders, influential institutions, and key journals that shape the discourse. It also highlights shifts in research focus, such as growing attention to ethical concerns or technological innovations. Ultimately, bibliometric studies offer evidence-based insights that inform research funding, policy decisions, and strategic planning for future innovations. Evaluating publication growth provides a clear picture of how scientific interest and investment in AI-assisted healthcare have expanded over time. Tracking publication trends helps assess whether research in specific subfields (e.g., AI in drug discovery versus diagnostics) is accelerating, stabilizing, or declining. Examining collaboration networks among authors, institutions, and countries reveals the structure and dynamics of scientific cooperation. Understanding these networks helps identify research hubs, potential collaborators, and the level of international and interdisciplinary integration within the field. Analyzing thematic areas through keyword co-occurrence and thematic mapping helps uncover the intellectual structure of the research domain. This process highlights dominant research themes (e.g., machine learning in oncology diagnostics), emerging topics (e.g., explainable AI in clinical decision-making), and areas that require attention. Furthermore, monitoring these themes is crucial for identifying research frontiers and predicting future directions. Collectively, evaluating these dimensions through bibliometric analysis enables a strategic overview of the field, supporting informed decision-making for researchers, funders, and policymakers. Thus, there is a need to systematically analyze the scientific output related to AI in the specified healthcare domains and to identify prevailing trends, emerging concerns, and research gaps. 2 Methodology Data Collection A bibliometric analysis was conducted, which began by collecting data on publications listed in Scopus. It is a bibliographic database that provides a comprehensive overview of global interdisciplinary scientific information, managed by Elsevier. The search in the database was performed using the advanced keywords TITLE-ABS-KEY (\"artificial intelligence\" OR \"AI\") AND (\"healthcare\" OR \"medical diagnostics\" OR \"drug discovery\" OR \"personalized medicine\" OR \"treatment planning\") AND (trends OR issues OR challenges OR concerns OR risks OR limitations OR barriers) AND (\"bibliometric analysis\" OR \"scientometric analysis\" OR \"systematic review\" OR \"literature analysis\") The entire search string is designed to retrieve review-type studies or bibliometric/scientometric analyses focused on AI applications in healthcare, specifically examining trends, challenges, and issues in fields such as diagnostics, drug discovery, personalized medicine, and treatment planning. In short, it filters for papers that discuss AI in healthcare, focusing on specific applications, addressing trends and critical issues, and using review or bibliometric methods to analyze existing research. To ensure comprehensive coverage, literature data were extracted from major academic databases such as Scopus, chosen for its extensive multidisciplinary coverage and detailed citation tracking capabilities. Filters were applied to publication years from 2021 to 2025, and document types were articles, book chapters, conference papers, and review papers. A systematic search strategy, employing carefully formulated Boolean search strings, was used to retrieve relevant publications. The search focused on combinations of keywords such as “Healthcare,” “Drug Discovery,” “Treatment Planning,” “Trends,” “Issues,” “Personalized Medicine,” “Treatment Planning,” and related terms. Bibliometric data were exported in standardized formats (CSV, BibTeX) for analysis. Tools utilized included VOSviewer for network visualization and mapping of co-authorship, co-citation, and keyword co-occurrence relationships. Bibliometrix package in RStudio for statistical analysis and thematic evolution tracking. Microsoft Excel for descriptive statistics and data cleaning. 3 Results and Discussion Overview of the data Table 1. General information on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning Table 1 provides a detailed interpretation of the general bibliometric information on trends, issues, and concerns related to AI in diagnostics, drug discovery, personalized medicine, and treatment planning over a 5-year period (2021-2025), indicating a recent and contemporary focus in research. Research data were collected from 47 distinct sources, including journals, books, and conference proceedings. A total of 48 documents were included in the analysis, reflecting a modest yet focused volume of research outputs in this field. This high growth rate (121.34%) indicates a rapid increase in interest and publication output concerning AI applications in healthcare diagnostics, drug discovery, personalized medicine, and treatment planning. On average, the documents are less than 1 year old (0.792 years), indicating that the majority of studies are highly recent, underscoring the evolving and cutting-edge nature of this field. Each document, on average, has received 22.5 citations, which suggests that the research in this field is gaining attention and recognition relatively quickly, considering the newness of the publications. Across all documents, there are 333 index keywords, reflecting a broad and diverse set of topics, methods, and technologies explored in the field. The authors themselves used 200 unique keywords, highlighting key themes and concepts directly prioritized by researchers in their work. The 48 documents were authored by 158 researchers, suggesting multi-author collaboration is common. Out of 48 papers, 11 were written by single authors, indicating that while collaboration is prevalent, individual research contributions still exist. On average, each document has just over three co-authors, suggesting moderate collaboration levels per study. A quarter (25%) of all documents involved collaboration between authors from different countries, indicating significant but not dominant international cooperation. The majority of publications (28) are journal articles, reflecting primary, peer-reviewed research outputs. A smaller portion (2) of research contributions is in book chapter format. Conference papers (10) form a significant portion, reflecting early-stage or rapidly disseminated research. Review papers (8) indicate efforts to synthesize and evaluate existing research, suggesting a maturing field with reflective assessments. This data indicates that AI's role in healthcare applications, including diagnostics, drug discovery, personalized medicine, and treatment planning, is a rapidly growing and evolving field of research. The field is characterized by recent, high-impact publications, moderate collaboration levels, and a mixture of article types, suggesting both foundational studies and synthesis works. The high growth rate and citation counts reflect rising academic and possibly industry interest, likely driven by AI’s potential to transform medical practice. The Annual Scientific Production chart provides a clear visualization of research output trends in AI applications for diagnostics, drug discovery, personalized medicine, and treatment planning from 2021 to 2025. Between 2020 and 2024, a strong upward trend is observed in scientific publications, reflecting a rapid growth in research interest and output in this specialized AI healthcare domain. A gradual increase in publications, reaching close to 100 articles in 2021. This indicates a growing recognition of AI’s potential in healthcare, although research is still in its early stages of development. A more significant rise, surpassing 150 articles in 2022. This year marks the early acceleration phase, possibly influenced by global health challenges (such as post-pandemic emphasis on digital health innovations). A sharp spike in publications to around 250–300 articles in 2023, showing clear momentum in research investments, academic focus, and industry interest. A dramatic surge occurs, with annual publications peaking above 500 articles in 2024. This suggests that AI’s role in healthcare has moved from exploration to widespread investigation and application. Improved AI models, increased funding, practical deployments, and pilot projects in clinical settings, along with growing demand for solutions in precision medicine, could drive sharp growth. The chart only shows data up to 2024, assuming continuity from prior trends; 2025 may stabilize near 2024 levels or potentially grow further, but this cannot be directly inferred from the chart. After the sharp peak in 2024, there is a very slight decline or plateau at the top. This might indicate a saturation point in publication volume, consolidation of research findings, and a shift from foundational studies to applied, commercial, or clinical implementation. The trend shows exponential growth that reflects rapid technological progress in AI models relevant to healthcare; the surge aligns with global healthcare digitalization trends, possibly influenced by real-world applications such as AI-assisted diagnostics and precision drug development, and the field is transitioning from conceptual exploration toward widespread, applied research, as evidenced by the high volume of recent outputs. The annual scientific production chart illustrates that AI applications in healthcare are experiencing a research boom, particularly from 2022 onwards, culminating in a potential peak or stabilization phase by 2024. This reflects an active and highly dynamic field poised for practical adoption, although future research directions may shift toward evaluating effectiveness, addressing ethical concerns, and implementing real-world applications. The chart illustrates a declining trend in average citations per year, despite a sharp rise in research output during the same period (as shown in Figure 1). This indicates that although more papers are being published, they are receiving fewer citations on average over time. During the year 2020–2021, citations per article were highest in the earliest years, averaging around 12 citations per paper. This suggests that early foundational or pioneering works in AI-assisted healthcare attracted significant attention and were frequently cited. A decline to around 8–9 citations per paper in 2022. This drop could reflect newer publications having had less time to accumulate citations and a rapid influx of publications diluting citation attention. A temporary increase to nearly 12 citations per paper in 2023 suggests that certain impactful studies or landmark publications may have appeared this year, driving citation rates back up temporarily. A sharp decline to approximately five citations per article in 2024. This is significant because a large volume of newer publications from 2024 (as shown in Figure 1) are likely too recent to have accumulated many citations, and the saturation of the research field may mean that individual studies receive less attention. The lowest point on the chart is expected to be reached in 2025, with approximately 1 citation per article. This is likely because articles published in 2025 are too new to have been widely cited yet. Initial publications (2020–2021) have the highest average number of citations, indicating their foundational importance and possibly novel contributions that have shaped subsequent research. As publication volume surged in 2023–2024, citation averages fell—likely because the field became crowded, research outputs became more specialized or incremental rather than groundbreaking, and recent papers have not had time to accumulate citations. Recent papers (2024–2025) naturally show low average citations due to the inherent lag between publication and citation accumulation. Articles from 2024 and 2025 may experience a rise in citations in the future as they mature within the literature ecosystem. Figure 2 illustrates a typical bibliometric pattern, where early papers dominate in citations, and newer publications, despite an increase in quantity, receive fewer citations initially. This suggests that while AI applications in healthcare are a growing research area, attention and influence remain concentrated in earlier, possibly more foundational studies. For researchers, this underlines the importance of publishing novel, high-impact work and the challenge of standing out amid a flood of recent publications. Figure 3, titled \"Most Relevant Sources (2021–2025),\" presents a horizontal bar chart identifying the top academic and research publications that have contributed to the literature on AI's role in diagnostics, drug discovery, personalized medicine, and treatment planning during this period. The figure shows that the X-axis (horizontal) represents the Number of Documents published by each source, and the Y-axis (vertical) lists the names of the sources/journals. Each blue dot represents 1 document; the number inside the black circle indicates the total document count for the most prominent source. \"Social Science and Medicine\" is the most prominent source with two documents. This reflects a notable trend: AI in healthcare is being explored not just technically but also in terms of social, ethical, and medical implications. This journal likely addresses the societal impacts, ethical dilemmas, and regulatory concerns surrounding the integration of AI into clinical workflows. Nine other sources each contributed 1 document. These include a mix of technical, medical, and legal publications, indicating a highly interdisciplinary interest in the topic such as Technical AI/CS journals: e.g., ACM Computing Surveys, ACM Proceedings, Artificial Intelligence Review, Applied Computational Intelligence and Soft Computing; Medical/Pharma journals: Chinese Journal of Pharmacovigilance, reflecting interest in drug safety and discovery using AI; Law and ethics: Brazilian Journal of International Law, possibly addressing global regulations and ethics in AI-driven healthcare and Research accountability: Accountability in Research likely discusses transparency, bias, and reproducibility issues in AI applications. Meanwhile, conferences such as AIES 2023 (Artificial Intelligence, Ethics, and Society) and the IEEE Uttar Pradesh Section International Conference 2024 suggest a recent momentum and academic focus on AI's implications in real-world clinical and ethical contexts. Interdisciplinarity is a dominant theme, meaning that AI in healthcare is not confined to computer science but spans medicine, ethics, law, and social sciences. Ethical and regulatory concerns are as prominent as technical advancements. This is evident from contributions by journals focused on accountability, law, and social sciences. Balanced academic interest, as there is no single journal that dominates the field (except one with just two papers), indicating that research is still diversifying and exploratory. New conferences and proceedings reflect the increased academic attention in recent years (2023-2024), particularly since the pandemic, as AI applications in healthcare have accelerated. Figure 3 illustrates that from 2021 to 2025, research on AI in diagnostics, drug discovery, personalized medicine, and treatment planning is gaining traction across multiple disciplines, raising complex societal and regulatory issues, beyond mere technical feasibility, and is still emerging, with contributions scattered across various sources rather than concentrated in a few core journals. This diverse pattern signals an evolving field that requires collaboration across medicine, computer science, ethics, and policy to integrate AI into healthcare systems in a responsible manner. This horizontal bar chart visualizes the number of documents published by corresponding authors from various countries in the field of AI in diagnostics, drug discovery, personalized medicine, and treatment planning. It also differentiates between Single Country Publications (SCP) and Multiple Country Publications (MCP), indicating whether the paper was written by authors from a single country or resulted from international collaboration. The top three contributors are India, with a total of four documents, all of which are Single Country Publications (SCPs), indicating vigorous domestic research activity but limited international collaboration. Next is Australia, with three documents, two SCPs, and one MCP, which demonstrates a mix of domestic research and global collaboration. The third group comprises Indonesia, Canada, China, and Colombia, with each country contributing 2–3 documents. Each has at least 1 MCP, suggesting these countries are engaging in cross-border collaborations more actively than India. There is a balanced international collaboration between Korea, Poland, the United Kingdom, and the USA, each of which has two documents, including both SCP and MCP (or just SCP). This reflects a moderate contribution with some international engagement. Countries with Only SCPs (No MCPs) include France, Iran, Italy, Portugal, Qatar, Romania, South Africa, Sweden, and the UAE. Each contributed one paper, all from within their own country. This likely reflects either Individual research efforts (perhaps by a single research group) or early-stage involvement in this domain without yet forming international collaborations. Finland has only one publication, and it is an MCP; this suggests that Finland's contribution is entirely internationally collaborative. India leads in volume but lags in collaboration, having the highest number of papers but no MCPs. Potentially due to strong internal research networks or barriers to international collaboration, such as funding, policy, or focus on domestic applications. Emergence of Australia and Canada as Hybrid Contributors for the significant contributions with both SCPs and MCPs. Indicates mature research environments that are both self-sufficient and internationally connected. There is a widespread but fragmented global involvement, as many countries contribute only 1 document, mostly SCPs. Suggests growing but scattered global interest in AI for healthcare, possibly fueled by isolated initiatives, funding from national programs, and one-time collaborations. There is a low rate of international collaboration because SCPs dominate most bars. While international interest is present, true cross-border research collaboration is still limited. Points to an opportunity to expand global cooperation, which is critical in healthcare innovation and AI ethics governance. Figure 4 shows that India is the top producer in volume, but it works primarily independently. Countries like Australia, Canada, and China show more balanced profiles, engaging in both domestic and international projects. International collaboration is still in its early stages, with many countries yet to engage in cross-border AI healthcare research. There is a clear opportunity to strengthen global networks for tackling healthcare challenges using AI through greater multi-country research partnerships. Figure 5 points out that darker blue shades indicate countries with higher levels of scientific production, lighter blue shades represent moderate output, and gray or very light-colored countries indicate minimal or no recorded scientific production (based on the dataset used). The countries shaded in dark blue are leading in scientific output, such as the United States, which is most prominent, likely the top producer; China also has very high production; India, the United Kingdom, Germany, and Japan have strong contributors, and Australia and Canada are also among the top producers. Countries shaded in medium blue indicate moderate levels of scientific publication, including Brazil, Russia, South Korea, Italy, France, Spain, Turkey, Iran, and South Africa. These countries have active scientific communities but contribute less than the top-tier nations. Countries in gray or very light blue, such as many African nations, parts of Central Asia, and a few countries in Central America and the Caribbean. These may have limited research infrastructure, lower investments in R&D, or data unavailability in the database used in Scopus. Trends observed signify that countries in North America, Western Europe, and East Asia are the global scientific powerhouses. South America and the Middle East show emerging scientific activity. Meanwhile, Africa and parts of Southeast Asia appear underrepresented. This map reflects global disparities in research funding, education, infrastructure, and international collaboration. It may guide international policy efforts to support underrepresented regions through partnerships and investments. The graph visualizes the number of scientific articles published from 2021 to 2025 by five countries: Australia, India, Indonesia, the United Kingdom, and the USA. The trend overview highlights that all five countries show positive trends in article production, although growth rates vary. India and Indonesia are expected to show the most rapid increase by 2025; Australia and the United Kingdom are projected to maintain steady growth, while the USA is expected to exhibit early growth and then plateau. As for country-by-country analysis, Australia (Red Line) starts with two articles in 2021, gradually increases each year, peaks at 11 articles in 2024, remains constant in 2025, and shows early and consistent leadership in article production. India (Olive Line) starts low at 1 article in 2021; gradually grows until 2023, with a sharp increase between 2024 (6 articles) and 2025 (13 articles), reaching its highest output in 2025, surpassing all other countries. Indonesia (Green Line) has no publications until 2024, followed by a sudden increase in 2025 to 7 articles, indicating a late start but an emerging presence in research output. United Kingdom (Blue Line) starts at 0 in 2021, rises steadily to 7 articles by 2023, plateaus at seven until 2024, then grows to 9 in 2025 and remains a steady and consistent contributor, with a late boost. The USA (Pink Line) starts at 1 article in 2021, remains flat until 2023, then gradually rises to 6 in 2024 and 9 in 2025, reflecting a modest but stable increase. Key observations include India showing the most dramatic growth, suggesting increased research funding or prioritization of publication. Australia had the earliest and strongest momentum, but growth slowed after 2024. Indonesia’s late entry suggests a newly developing research effort. The USA and UK, typically strong in research, show moderate performance here, possibly due to the scope or filters applied to the data in AI for healthcare. There is a growing diversification in global participation in scientific research. Emerging economies, such as India and Indonesia, are becoming key players. Research trends are not static; countries can leap forward rapidly with the proper support and infrastructure in place. Figure 6 presents a horizontal bar graph of the top 10 countries based on the number of citations their research articles have received. This graph highlights research impact, not just production. Citations reflect how influential or widely referenced a country’s scientific work is within the global research community. The United Kingdom dominates the chart with 442 citations, far exceeding those of all other countries, indicating exceptionally high-impact research, which suggests strong international visibility and possibly leading roles in collaborative studies. Australia and Finland have a very close citation count (87 and 86, respectively), indicating high-quality research output that is frequently cited, even though the total number of articles is fewer than those of top-producing countries like India or the USA. China (55) and Iran (38) show moderate impact, reflecting a growing influence, particularly in fields where citation rates are increasing (e.g., AI, healthcare). However, lower citation counts, especially in the USA, with only 1 citation, which is surprising. This may be due to the limited number of articles in the analyzed dataset, newer publications not yet cited, and a focus on a niche field where citation accumulation is slow. The USA and India were more productive in the earlier graph, but their work appears less cited here, showing that impact is not proportional to output. The UK’s significant lead could be attributed to influential review articles or foundational studies, participation in high-profile international projects, and publications in high-impact journals. Countries like Colombia, Iran, and Korea show promising citation levels despite limited global presence, historically indicating a rise in research relevance. This graph emphasizes the quality and international recognition of scientific work. While some countries may publish more, others (like the UK and Finland) are producing research that the global community relies upon and cites heavily. Figure 7 is a word cloud, which visually represents the frequency or importance of terms related to a specific topic. In this case, the topic appears to be the ethics and governance of Artificial Intelligence (AI). The larger and bolder the words, the more frequently they appear or the more critical they are in the analyzed text or dataset. The dominant terms are the most prominent, comprising artificial intelligence, ethical technology, systematic literature review, AI ethics, and responsible AI. These terms are the most central themes, likely indicating a strong focus on evaluating and guiding AI development and implementation from an ethical, technological, and review-based perspective. Other significant concepts include ethics, algorithmic bias, data privacy, AI governance, accountability, trustworthy AI, human bias, and transparency. These terms suggest concerns about bias, privacy, accountability, and transparency in AI systems, which are critical aspects of ethical AI. Contextual/supportive concepts involve decision-making, systematic review, healthcare, economic and social effects, generative AI, and governance. These highlight the applications of AI in fields like healthcare and policy, and the importance of systematic approaches to analyzing its impact. This word cloud strongly emphasizes the interplay between artificial intelligence and ethics, with key themes around responsibility, bias, privacy, governance, and systematic review processes. It is likely derived from academic or policy-related literature exploring how AI can be developed and deployed ethically. This image is a thematic map, commonly used in bibliometric or systematic literature reviews to visualize research themes based on two dimensions: the X-axis (Relevance degree/Centrality), which represents the importance or centrality of a theme within the overall field. Y-axis (Development degree / Density): How developed or specialized the theme is internally (i.e., the coherence of the research around it). Upper-Right: Motor Themes are considered to have high centrality and high density; these are well-developed and important to the field. Themes include artificial intelligence, ethical technology, and data privacy, which are core driving themes in the literature. They are both highly relevant and well-studied. The upper-left is the Niche Themes, which are characterized by low centrality and high density. These are well-developed but marginal, specialized, or isolated themes such as ethics, systematic review, and human. These are deeply researched but not central to the overall network; they are possibly more interdisciplinary or highly specialized in nature. Lower-Left: Emerging or Declining Themes, characterized by low centrality and density, often representing underdeveloped new areas or fading topics. Themes such as current, decentralized finance, ethical considerations, ethical artificial intelligence, and ethical implications are newer trends (like decentralized finance) or areas that are not yet fully explored or are losing traction. Lower-Right: Basic Themes, with high centrality and low density, with fundamental topics that are important but underdeveloped or general. Themes such as AI ethics, responsible AI, and systematic literature reviews are central to the field but require further development or are more conceptual/introductory in nature. The core of the research field revolves around AI, ethics, and data privacy, with strong development in ethical technology. Responsible AI and AI ethics are important, but they require further development, suggesting a growing interest, albeit still in their early stages. Specialized work is being done on ethics and systematic reviews, but they are not central to the broader research discussions. Emerging topics, such as decentralized finance and the ethical implications of AI, are on the periphery but may gain traction over time. This image is a Conceptual Structure Map generated using Multiple Correspondence Analysis (MCA) — a common technique in bibliometric and co-word analysis. The purpose is to reveal the underlying intellectual structure of a field by clustering and projecting keywords/themes in a reduced-dimensional space. Dim 1 (X-axis) and Dim 2 (Y-axis) represent the first two dimensions extracted from the MCA, which explain the variation in the co-occurrence of keywords across documents. Although they are not labeled semantically, their relative positions help identify conceptual proximities and thematic clusters. Top-left Cluster (Upper-Left Quadrant): Key terms: Responsible artificial intelligence, trustworthy AI, responsible AI, philosophical aspects, ai ethics, patient care, transparency; Theme: Ethical & Trustworthy AI in Social & Medical Contexts; Focus: Emphasizes ethical and humanistic approaches to AI—particularly in sensitive domains like healthcare and patient care, and includes a philosophical foundation; Conceptual Maturity: This cluster appears internally coherent, suggesting a developed subfield. Top-right Cluster (Upper-Right Quadrant): Key terms: Large language model, health care delivery, clinical practice guideline, algorithm bias, fairness, health policy; Theme: Applied AI in Healthcare and Fairness; Focus: Intersection of AI tools (e.g., LLMs) with healthcare systems, fairness, and health policy—real-world implementations and ethical oversight; Trend: This is a cutting-edge, rapidly evolving area with practical and policy implications. Center Cluster: Key terms: Artificial intelligence, systematic review, data privacy, accountability, ethics, bias, technology; Theme: Core AI Ethics Discourse; Focus: This central area represents the foundational discourse around AI, touching on bias, ethics, privacy, and systematic literature reviews; Interpretation: It acts as the intellectual “glue” that links more specialized subfields. Bottom-left Cluster (Lower-Left Quadrant): Key terms: Education, students, engineering education, ethical dilemma, research trends, education computing; Theme: AI Ethics in Education; Focus: Addresses the ethical challenges and use of AI in educational contexts, including higher education and computing education; Stage: Possibly underdeveloped or emerging. Bottom-right / Peripheral Themes: Key terms: International cooperation, law, public health, stakeholder, male; Theme: Policy and Global Dimensions; Focus: International law, cooperation, and stakeholder involvement in AI governance; Position: Somewhat peripheral, suggesting these topics are not tightly integrated with the core discourse but are important extensions. Overall, the field is broadly structured around Core ethical considerations in AI (center), Applied concerns in healthcare (upper right), Philosophical and humanistic ethics (upper left), Educational applications and concerns (lower left), and Global governance/policy (lower periphery). Furthermore, Dim 1 (X-axis) reflects a spectrum from technical/applied topics (right) to conceptual/ethical concerns (left), and Dim 2 (Y-axis) separates abstract/philosophical themes (top) from practical/policy themes (bottom). This is a Country Collaboration Map, commonly used in bibliometric or scientometric analysis to visualize international research partnerships. It illustrates how actively countries are involved in collaborations on a specific research topic, likely AI ethics or related fields, based on previous maps. Darker blue denotes higher research output and collaboration frequency, while lighter blue denotes lower collaboration activity; grey denotes no or negligible participation in the analyzed literature. The United States is the dominant collaborator, with the darkest blue shade, indicating the highest volume of collaborative publications, and serves as a central hub in global research collaborations. The United Kingdom is highly active in international collaboration, which is directly linked with the United States by a visible connection line, suggesting a strong bilateral research relationship. India is a significant contributor with high collaboration levels, which is likely to collaborate with both Western countries and neighboring Asian partners. China is also prominently involved; however, fewer visible direct collaboration lines might indicate more domestic publication or selective partnerships. Australia, Canada, Germany, France, and Italy show strong involvement, medium to dark blue. These countries have active roles in international research, likely collaborating with the US. Other notable collaborators, such as South Korea, Japan, Brazil, South Africa, Nigeria, Pakistan, Iran, Indonesia, and Argentina, exhibit moderate to low levels of collaboration. Their lighter blue indicates participation, though with fewer collaborations. Most collaborations are centered around the U.S. and the U.K., reflecting their roles as global leaders in academic and policy circles regarding AI and ethics. Europe shows intra-regional collaboration, particularly among Western European countries. Asia is active, with India, China, and South Korea playing significant roles, though intercontinental links are less visually prominent. Africa and Latin America are underrepresented but still present, suggesting emerging engagement in the field. The U.S. and U.K. are the dominant collaborative hubs in AI ethics research. Global collaboration is broad but uneven, with most activity concentrated in North America, Western Europe, and certain parts of the Asia-Pacific region. Developing regions are underrepresented, which may point to disparities in access, funding, or infrastructure in AI ethics research. This is a term co-occurrence network map generated using VOSviewer, a tool commonly used in bibliometric analysis to visualize relationships among keywords or terms extracted from a body of academic literature. The purpose of this network map is to illustrate the frequency with which terms co-occur in research publications and the degree of relatedness between different concepts within the scholarly discussion associated with AI in healthcare/ethics, based on the terms. Node Size (Font Size of Terms), such as “artificial intelligence”, “machine learning”, “deep learning”, “healthcare”, “humans”, and “personalized medicine” are the most dominant and widely discussed topics. It also indicates the frequency of occurrence of the term in the dataset. The Edges (Connecting Lines) show co-occurrence links: how frequently two terms appear together. The thicker/denser lines indicate stronger relationships. Terms such as artificial intelligence, machine learning, deep learning, natural language processing, and support vector machine are foundational AI techniques and appear as the core research axis in this domain. Terms such as healthcare, healthcare system, patient care, healthcare personnel, electronic medical record, and telemedicine demonstrate how AI is applied across the healthcare infrastructure and clinical workflows. Terms such as personalized medicine, predictive value, treatment planning, biomarkers, and cancer types (including breast, prostate, and lung) focus on AI’s use in diagnostics, genomics, and individualized treatment strategies. Terms such as systematic review, meta-analysis, data extraction, PRISMA, literature review, and Scopus indicate strong emphasis on evidence synthesis, methodology, and bibliometric analysis in this body of work. Terms like ethics, ethical technology, consensus, confidentiality, and ethical challenges, as well as concerns around AI applications, are part of the literature but remain more peripheral. Terms such as radiomics, oncology, biomarkers, chemotherapy, tumor microenvironment, and drug development refer to the use of AI in diagnostic imaging, drug discovery, and cancer care. Overall patterns suggest that the dense web of connections suggests a mature, interdisciplinary field with a strong conceptual core. AI is tightly coupled with both clinical applications and research methodologies, reflecting a robust interest in both practice and policy. Peripheral areas (ethics, specialty domains) suggest growing but still developing research subfields. 4 Conclusion This bibliometric analysis offers a comprehensive overview of recent scientific trends, issues, and collaborative patterns related to the application of Artificial Intelligence (AI) in healthcare, specifically in diagnostics, drug discovery, personalized medicine, and treatment planning. The rapid growth in publication volume from 2021 to 2025 reflects an intensifying global interest in the transformative potential of AI to address longstanding healthcare challenges, including diagnostic delays, treatment inefficiencies, and the need for individualized care strategies. Key themes identified in co-occurrence and conceptual maps confirm that artificial intelligence, machine learning, deep learning, and personalized medicine are central to the discourse, with applications spanning cancer diagnostics, radiomics, genomics, and predictive analytics. A thematic shift is evident, with earlier research focused on foundational AI methods, now expanding toward more applied, interdisciplinary, and ethically informed domains. Emerging topics such as explainable AI, algorithmic fairness, and data privacy signal a maturing research field that is grappling not only with technical innovation but also with governance, trust, and societal impact. The structural maps reveal a fragmented yet globally expanding research landscape. While countries such as the United States, the United Kingdom, India, China, and Australia dominate output, international collaboration remains uneven, suggesting opportunities for deeper cross-border and cross-disciplinary partnerships. Citation analyses highlight the outsized influence of early publications and the emerging impact of recent studies, especially those addressing both technological performance and ethical frameworks. Despite the impressive momentum, the field continues to face persistent challenges. Declining citation averages over time may indicate content saturation or the difficulty of producing novel contributions amidst a flood of publications. Moreover, ethical and governance-related research remains somewhat peripheral, even as calls for responsible AI grow louder. Low participation from developing regions reflects ongoing global disparities in research capacity, underscoring the need for inclusive strategies in AI deployment. Overall, the analysis confirms that AI is transitioning from conceptual promise to practical implementation in healthcare. For this shift to be sustainable, future research must emphasize robust validation, ethical oversight, equitable access, and interdisciplinary integration. Policymakers, funders, and researchers must collaborate to ensure that AI not only enhances healthcare outcomes but also does so in a manner that is transparent, accountable, and universally beneficial. Declarations Funding This study did not receive monetary funds. Clinical Trial Number Not Applicable. Consent to Publish Declaration Not applicable. Consent to Participate Declaration Not applicable. Ethics Declaration Not applicable. Author Contribution SINGLE AUTHOR ONLY References Hou, Y. J., Xie, J. B., Zhai, Y., & Lu, G. (2025). Evolution of artificial intelligence in healthcare: a 30-year bibliometric study. Frontiers in Medicine, 11. https://doi.org/10.3389/fmed.2024.1505692 Chowdhury, R. (2024). Intelligent systems for healthcare diagnostics and treatment. World Journal of Advanced Research and Reviews, 23(1), 007–015. https://doi.org/10.30574/wjarr.2024.23.1.2015 Liu, Y.-X., Zhu, C., Wu, Z.-X., Lu, L., & Yu, Y.-T. (2022). A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness. Annals of Translational Medicine, 10(16), 854. https://doi.org/10.21037/atm-22-913 Apriyanto, N. P., Alamsyah, N., Wijaya, O., Loniza, E., & Satriyandari, Y. (2024). Roles of AI in Healthcare. 289–294. https://doi.org/10.1109/icitcom62788.2024.10762060 Bhagat, I. A., Wankhede, K. G., Kopawar, N. A., & Sananse, D. A. (2024). Artificial Intelligence in Healthcare: A Review. International Journal of Scientific Research in Science, Engineering and Technology, 11(4), 133–138. https://doi.org/10.32628/ijsrset24114107 Gencer, A. (2024). Bibliometric analysis and research trends of artificial intelligence in lung cancer. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e24665 Rich, E., & Winston, P. (2024). AI in Healthcare. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-15285 Andrade-Arenas, L., & Yactayo-Arias, C. (2024). A bibliometric analysis of the advances of artificial intelligence in medicine. International Journal of Electrical and Computer Engineering. https://doi.org/10.11591/ijece.v14i3.pp3350-3361 Dhudum, R., Ganeshpurkar, A., & Pawar, A. (2024). Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications. https://doi.org/10.3390/ddc3010009 Guo, J., Tong, H. H. Y., & To, W. M. (2024). Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review. Mini-Reviews in Medicinal Chemistry. https://doi.org/10.2174/0113895575271267231123160503 Khandagale, P. (2024). Emerging trends in drug discovery: Harnessing artificial intelligence and machine learning for drug development. 12(03), 56–61. https://doi.org/10.31690/ipplanet.2024.v012i03.016 Datta, D., & De, A. (2023). Impact of Artificial Intelligence on Medical Literature. Acta Scientific Otolaryngology, 5(8), 09–12. https://doi.org/10.31080/asol.2023.05.0586 De Nicolò, V., & La Torre, D. (2024). Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022) (pp. 151–177). Elsevier BV. https://doi.org/10.1016/b978-0-443-13671-9.00004-1 Vijayan, V. K., Is, S., & John, J. (2024). An insight on the interventions of AI in healthcare—A bibliometric study. Journal of Autonomous Intelligence . https://doi.org/10.32629/jai.v7i4.1148 Özalp, S. (2024). AI and Personalized Treatment. Next Frontier. , 8 (1), 179. https://doi.org/10.62802/e6fyw805 Khanna, P. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. https://doi.org/10.3390/pharmaceutics16101328 Nihalani, G. (2024). Applications of Artificial Intelligence in Pharmaceutical Research: An Extensive Review. Bioequivalence & Bioavailability International Journal, 8(1), 1–7. https://doi.org/10.23880/beba-16000231 Shah, P. N., & Shah, R. P. (2023). Artificial Intelligence in the Paradigm Shift of Pharmaceutical Sciences: A Review. Nano Biomedicine and Engineering. https://doi.org/10.26599/nbe.2023.9290043 Hussain, W., Mabrok, M. A., Gao, H., Rabhi, F. A., & Rashed, E. A. (2024). Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digital Health , 10 . https://doi.org/10.1177/20552076241258757 Chawla, M. (2024). Artificial Intelligence in Drug Discovery: Transforming the Future of Medicine. Premier Journal of Science. , 2024 . https://doi.org/10.70389/pjs.100034 Glicksberg, B. S., & Klang, E. (2024). Unveiling Recent Trends in Biomedical Artificial Intelligence Research: Analysis of Top-Cited Papers. Applied Sciences . https://doi.org/10.3390/app14020785 Rastogi, M. (2023). The growth and potential of ai applications in medicine and healthcare. Indian Journal of Applied Research . https://doi.org/10.36106/ijar/7206074 Kabilan, K. (2024). AI in Drug Development: Speeding Up the Search for New Medicines – A Comprehensive Review . https://doi.org/10.14293/pr2199.001372.v1 Okolo, C. A., Olorunsogo, T., & Babawarun, O. (2024). A comprehensive review of AI applications in personalized medicine. International Journal of Science and Research Archive , 11 (1), 2544–2549. https://doi.org/10.30574/ijsra.2024.11.1.0338 Weerarathna, I. N. (2024). Artificial intelligence in healthcare (pp. 346–362). https://doi.org/10.58532/v3biai4p4ch2 Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial Intelligence in Health Care: Bibliometric Analysis. Journal of Medical Internet Research , 22 (7). https://doi.org/10.2196/18228 Bhattamisra, S. K., Banerjee, P., Gupta, P., Mayuren, J., Patra, S., & Candasamy, M. (2023). Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing , 7 (1), 10. https://doi.org/10.3390/bdcc7010010 Khanday, S., & Ahmed, M. (2024). Leveraging Artificial Intelligence for Enhanced Healthcare Diagnostics: Opportunities and Challenges. International Journal of Science and Research . https://doi.org/10.21275/sr24316150834 Mottaghi-Dastjerdi, N., & Soltany‐Rezaee‐Rad, M. (2024). Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review. Deleted Journal , 23 (1). https://doi.org/10.5812/ijpr-150510 Senthil, R., Thirunavukarasou, A., Somala, C. S., & Saravanan, K. M. (2024). Bibliometric analysis of artificial intelligence in healthcare research: trends and future directions. Future Healthcare Journal , 11 (3), 100182. https://doi.org/10.1016/j.fhj.2024.100182 Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Martínez Simón, J., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics , 16 (10), 1328. https://doi.org/10.3390/pharmaceutics16101328 Bhabad, S., Lamkhade, D., Koyate, S., Karanjkhele, K., Kale, V., & Doke, R. (2023). Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research. IP International Journal of Comprehensive and Advanced Pharmacology , 8 (4), 210–219. https://doi.org/10.18231/j.ijcaap.2023.034 Katwaroo, A. R., Adesh, V. S., Lowtan, A., & Umakanthan, S. (2023). The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgraduate Medical Journal . https://doi.org/10.1093/postmj/qgad135 Wu, L., Li, J., Guan, Q., & Duan, J. (2023). Global research trends of artificial intelligence applied in drug design: A bibliometric analysis based on Vosviewer and Citespace . https://doi.org/10.1145/3644116.3644324 Amirineni, S. (2024). The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications. World Journal of Advanced Research and Reviews , 23 (2), 2026–2030. https://doi.org/10.30574/wjarr.2024.23.2.2559 Purohit, S., & Kumar C. N., S. (2024). AI in Health Care: A Comprehensive Review . https://doi.org/10.52711/jnmr.2024.26 Zahoor, F., Abdullah, M., Tahir, M. W., & Islam, A. (2024). The 100 most influential papers in medical artificial intelligence; a bibliometric analysis. 74 (4), 752–761. https://doi.org/10.47391/jpma.10064 Basubrin, O. (2025). Current Status and Future of Artificial Intelligence in Medicine. Cureus . https://doi.org/10.7759/cureus.77561 Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024). Ai’s impact on personalized medicine: tailoring treatments for improved health outcomes . https://doi.org/10.51594/estj.v5i4.1040 Mondal, P. K., Hasan, M. F., Rusho, M. A., & Rahman, M. H. (2024). A Systematic Literature Review on the Application of Artificial Intelligence and Machine Learning in Personalized Medicine: Methodological Advances and Emerging Trends . https://doi.org/10.31219/osf.io/f3jsz Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7370235\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Systematic Review\",\"associatedPublications\":[],\"authors\":[{\"id\":519219524,\"identity\":\"c8616da0-680f-430a-8924-76423b8fab89\",\"order_by\":0,\"name\":\"Florieza Mangubat\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Cebu Technological University – Tuburan Campus\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Florieza\",\"middleName\":\"\",\"lastName\":\"Mangubat\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-14 06:08:26\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7370235/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7370235/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":92154350,\"identity\":\"bc9e2ecb-4a61-40bb-8b90-8cffd15d9546\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1851695,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ABIBLIOMETRICReview.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/d9df658f882b9a4e6427b99f.docx\"},{\"id\":92155330,\"identity\":\"0a046316-6944-411c-b360-3baaf4a14363\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4368,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"5fab971556164066b99a8fc0084a0d61.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/f249e35e63f3d9d0ef66d7a9.json\"},{\"id\":92155648,\"identity\":\"cc150d6b-f137-4788-b3ee-f6851f6046c4\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 09:03:50\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":115373,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"5fab971556164066b99a8fc0084a0d611enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/529bfb38fbbd66d8f0c759bb.xml\"},{\"id\":92155332,\"identity\":\"1dd48926-40fb-4eee-ac2a-64a7ae97ad09\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":63125,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/6efa58a77fb1a672c30fc404.png\"},{\"id\":92154356,\"identity\":\"0cf0833d-9089-4663-afff-301796cf885a\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":203070,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/9899978ff3c4831691c95505.png\"},{\"id\":92154353,\"identity\":\"835ee272-5648-4f47-a24d-b5a37821abb6\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":75719,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/db7fa40a58dd501727ba6170.png\"},{\"id\":92155334,\"identity\":\"d7c5f2e3-c54e-481d-9a18-db1df25e16fd\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1132692,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage12.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/0e93f4d3672db43b2abf11ca.jpeg\"},{\"id\":92155333,\"identity\":\"8fe31118-5cd1-4eca-ad4d-f68a668eac99\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"png\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":18646,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/44061e8854714822b67dbba4.png\"},{\"id\":92155336,\"identity\":\"08da940a-6386-4ea0-8175-cbd42eff69d4\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"png\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":96064,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/c02a292645a9b3c946bd5328.png\"},{\"id\":92154357,\"identity\":\"8fa4f5ba-243e-4df0-982d-c8ab00029f17\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":83928,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/19f7daafb0739a0c6dec3e22.png\"},{\"id\":92154365,\"identity\":\"286a5d0c-c777-455c-a7fa-9c40f7da3f5a\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"jpeg\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":225002,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/27d1dfaed7be6028363f7573.jpeg\"},{\"id\":92154362,\"identity\":\"1cca9529-1a7b-41da-baf1-19a645b4fb2b\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":64121,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/249f779bd34fb9bd7e70b59f.png\"},{\"id\":92154368,\"identity\":\"64cb2275-39be-4198-96e5-5f2ad5ca9b56\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":46119,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/b06f64b708fc96b45dfdcd69.png\"},{\"id\":92154363,\"identity\":\"c129500c-76b2-4b75-acc8-78f831a46c32\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":90351,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/1180ba4c8c227576abf0bf9c.png\"},{\"id\":92155338,\"identity\":\"1c276d61-7950-480a-a3bc-830e21250323\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"png\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":58407,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/21d03fa3b277f7a9a71f5058.png\"},{\"id\":92154374,\"identity\":\"3e602277-6ea9-4bfd-aa30-15a05c284d31\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:51\",\"extension\":\"png\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":56871,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/3a1487cdd6f601626e38e066.png\"},{\"id\":92154376,\"identity\":\"2a31c539-21c8-49fc-91e1-aa3d41c8ad42\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:51\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":51532,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/36fc66c9ad32b9eaae56a3fc.png\"},{\"id\":92154378,\"identity\":\"32dd1648-8bf8-4f4e-978a-c49349596566\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:51\",\"extension\":\"png\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":21744,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/7f1404e8d822d7428cdf2235.png\"},{\"id\":92154372,\"identity\":\"2e211b01-d47d-42a6-b992-428f20ab2be8\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":339664,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/819d595302751f2c7a6bdb97.png\"},{\"id\":92155651,\"identity\":\"936d97a9-7393-4808-b25c-bce336a14b78\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 09:03:51\",\"extension\":\"png\",\"order_by\":19,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4192,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/4e07531c3ac0dedb0b3d07e7.png\"},{\"id\":92155345,\"identity\":\"83670111-8cc2-4518-9730-cdad8c6dd90b\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:51\",\"extension\":\"png\",\"order_by\":20,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":88209,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/124093ffdd1d5ba122ea20fc.png\"},{\"id\":92154370,\"identity\":\"38456bdd-9577-4aa8-b4f1-816914d42df5\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":21,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":21281,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/4e1d98e2982290061b34e3a5.png\"},{\"id\":92154371,\"identity\":\"0f19783e-4dd6-45cf-8bcd-78dbe9706aab\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":22,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":79485,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/52b423fab4093e86929acb2e.png\"},{\"id\":92155340,\"identity\":\"2c3aedef-1fe5-4803-80ec-811044cf37a4\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:51\",\"extension\":\"png\",\"order_by\":23,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":15677,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/38b278235c7bbc6b6199671f.png\"},{\"id\":92154369,\"identity\":\"c96733f4-0144-4be6-9323-c6dda6404d45\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":24,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":8600,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/c98ef777f4573376f6bde7a8.png\"},{\"id\":92155343,\"identity\":\"86fa8c12-7426-4032-a150-e10e9c1c4c07\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:51\",\"extension\":\"png\",\"order_by\":25,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":16819,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/e23ca4894b75309345d0258b.png\"},{\"id\":92154373,\"identity\":\"950171b7-771d-46e8-a112-9415e5d907e1\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":26,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":18086,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/c7d002212d76ac40c2a8fd38.png\"},{\"id\":92154375,\"identity\":\"230c4677-5461-4f74-9b8d-d0632f6308b4\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:51\",\"extension\":\"xml\",\"order_by\":27,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":113853,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"5fab971556164066b99a8fc0084a0d611structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/95c0e01adb307ce57c7b86fc.xml\"},{\"id\":92155341,\"identity\":\"2f53ceae-fc71-4b4d-8e30-d66188ea4659\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:51\",\"extension\":\"html\",\"order_by\":28,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":124016,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/1b5fee2f0ddab9962dd934c6.html\"},{\"id\":92154342,\"identity\":\"6bb0a82b-ec80-4ede-bd3d-0bcd465f527b\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:49\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":31002,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnual scientific production from 2021-2025, the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/e8b35095bc37da687820bc08.png\"},{\"id\":92155329,\"identity\":\"a3e5cc23-2039-480d-9c3d-044e2995f01d\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:49\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":28725,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnual average citations from 2021 to 2025 on the trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/fdba7d6c48f86b3457bd86c5.png\"},{\"id\":92154343,\"identity\":\"696428d2-6720-4ec4-9f8c-3501ec33c879\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":72971,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMost relevant sources from 2021-2025, the trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/ef64aaabc9d8d7884ee14b54.png\"},{\"id\":92154351,\"identity\":\"e410ce65-fe22-4ae2-91d2-dbc52108297d\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57877,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorresponding authors’ countries in terms of collaboration type and research output from 2021 to 2025\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/8d35310d0c7737cac599d034.png\"},{\"id\":92154348,\"identity\":\"087b0070-5a2c-4be2-95ec-4f7d947e42ad\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":120026,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCountry scientific production\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/ddbce36c188c3c532ccc4d33.png\"},{\"id\":92154355,\"identity\":\"b3175cbc-47fc-4a36-b43f-ab99d8ebf2e8\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":60384,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCountry production over time from 2021 to 2025\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/289107e34739fa318e916ebc.png\"},{\"id\":92154354,\"identity\":\"7e8c5aa9-4931-46eb-865c-23666b21dc72\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":33559,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 6\\u003c/strong\\u003e. Top ten most cited countries on publications regarding trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/a2feb79897d8ddf991128533.png\"},{\"id\":92155649,\"identity\":\"ace5c4ed-14a0-4bcc-939f-deba8135b3d9\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 09:03:50\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":191174,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 7\\u003c/strong\\u003e. Word cloud composition on trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/47b07eead6ac1ca6468968c4.png\"},{\"id\":92155337,\"identity\":\"c3e762ee-a663-4ab0-a699-ee31669c5827\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:50\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":56843,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 8\\u003c/strong\\u003e. Keyword-based conceptual structure map on trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/1dd4e9e49a9f1d3bc6f865c1.png\"},{\"id\":92154359,\"identity\":\"4689f825-56e9-455c-8d57-611666af18ae\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":211931,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 9\\u003c/strong\\u003e. Conceptual structure map using Multiple Correspondence Analysis (MCA) on trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/49877568f4dd162f1f61082c.png\"},{\"id\":92155342,\"identity\":\"d7e7e37d-c1ce-4685-b191-606fd0e790bd\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:55:51\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":94347,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 9\\u003c/strong\\u003e. Scopus document distribution on trends, issues, and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/3d94a3e0eecc786182cfc738.png\"},{\"id\":92154367,\"identity\":\"a01f7001-00c1-448b-81ad-7558476ebd19\",\"added_by\":\"auto\",\"created_at\":\"2025-09-25 08:47:50\",\"extension\":\"png\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":577220,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 10\\u003c/strong\\u003e. Visualization of keywords based on occurrence time\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/4a5aede7eaa4a8c1f7822acb.png\"},{\"id\":94598878,\"identity\":\"4f784b25-05a0-45c1-bcf8-c841c41d7913\",\"added_by\":\"auto\",\"created_at\":\"2025-10-28 18:57:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1922370,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7370235/v1/06d3b84b-6769-489f-8d88-772d31d2763a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A bibliometric review on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eArtificial Intelligence (AI) technologies have increasingly become integral to modern healthcare, supporting clinicians and researchers across critical domains. In diagnostics, AI-driven tools such as machine learning algorithms, computer vision, and natural language processing (NLP) are used to interpret medical imaging, pathology slides, and electronic health records (EHRs), enabling earlier and more accurate disease detection [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] AI models assist in diagnosing conditions ranging from cancers to rare diseases, often outperforming traditional diagnostic techniques in speed and precision [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In drug discovery, AI accelerates the identification of potential therapeutic compounds, predicts drug-target interactions, and streamlines the design of clinical trials [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Techniques such as deep learning and reinforcement learning enable researchers to analyze vast datasets of chemicals and biology, resulting in reduced development timelines and costs [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Personalized medicine benefits from AI\\u0026rsquo;s ability to analyze genomic, proteomic, and clinical data to create individualized treatment plans [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. AI models predict patient responses to treatments, enabling the development of optimized therapeutic strategies tailored to individual genetic profiles, disease subtypes, and lifestyle factors [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In treatment planning, AI aids in optimizing clinical decisions through predictive analytics, decision support systems, and robotic interventions [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. AI algorithms support evidence-based recommendations in oncology, cardiology, and other specialties, helping healthcare providers devise treatment regimens that maximize efficacy while minimizing adverse effects [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAI is poised to reshape healthcare systems worldwide by enhancing diagnostic accuracy, accelerating drug development, personalizing treatment, and improving clinical workflows. AI addresses many of the traditional challenges in healthcare delivery. Its applications reduce diagnostic errors, shorten the time from research to treatment, and enable more equitable care through scalable digital tools [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. It also plays a significant role in addressing workforce shortages by automating routine tasks, supporting clinical decision-making, and expanding access to specialist-level care in under-resourced settings [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Moreover, AI-driven predictive models improve population health management by identifying at-risk patients earlier, enabling preventive interventions [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. However, the transformative potential of AI in healthcare is not without challenges [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Issues such as data privacy, algorithmic bias, regulatory uncertainty, and technological interoperability must be addressed to ensure the responsible adoption of AI [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Despite these concerns, AI remains central to the evolution of more efficient, accurate, and patient-centered healthcare systems.\\u003c/p\\u003e\\u003cp\\u003eIn the rapidly evolving field of artificial intelligence (AI) in healthcare, bibliometric analysis serves as a valuable tool for systematically mapping scientific progress [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. By quantitatively analyzing published literature, bibliometric methods provide objective insights into research productivity, influence, and focus areas over time. This type of analysis helps uncover not only the volume of research but also its underlying patterns, such as emerging technologies, popular applications, and recurring concerns in diagnostics, drug discovery, personalized medicine, and treatment planning. Bibliometric analysis enables researchers and policymakers to identify thought leaders, influential institutions, and key journals that shape the discourse. It also highlights shifts in research focus, such as growing attention to ethical concerns or technological innovations. Ultimately, bibliometric studies offer evidence-based insights that inform research funding, policy decisions, and strategic planning for future innovations. Evaluating publication growth provides a clear picture of how scientific interest and investment in AI-assisted healthcare have expanded over time. Tracking publication trends helps assess whether research in specific subfields (e.g., AI in drug discovery versus diagnostics) is accelerating, stabilizing, or declining. Examining collaboration networks among authors, institutions, and countries reveals the structure and dynamics of scientific cooperation. Understanding these networks helps identify research hubs, potential collaborators, and the level of international and interdisciplinary integration within the field.\\u003c/p\\u003e\\u003cp\\u003eAnalyzing thematic areas through keyword co-occurrence and thematic mapping helps uncover the intellectual structure of the research domain. This process highlights dominant research themes (e.g., machine learning in oncology diagnostics), emerging topics (e.g., explainable AI in clinical decision-making), and areas that require attention. Furthermore, monitoring these themes is crucial for identifying research frontiers and predicting future directions. Collectively, evaluating these dimensions through bibliometric analysis enables a strategic overview of the field, supporting informed decision-making for researchers, funders, and policymakers. Thus, there is a need to systematically analyze the scientific output related to AI in the specified healthcare domains and to identify prevailing trends, emerging concerns, and research gaps.\\u003c/p\\u003e\"},{\"header\":\"2 Methodology\",\"content\":\"\\u003cp\\u003eData Collection\\u003c/p\\u003e\\u003cp\\u003eA bibliometric analysis was conducted, which began by collecting data on publications listed in Scopus. It is a bibliographic database that provides a comprehensive overview of global interdisciplinary scientific information, managed by Elsevier. The search in the database was performed using the advanced keywords TITLE-ABS-KEY\\u003c/p\\u003e\\u003cp\\u003e(\\\"artificial intelligence\\\" OR \\\"AI\\\") AND\\u003c/p\\u003e\\u003cp\\u003e(\\\"healthcare\\\" OR \\\"medical diagnostics\\\" OR \\\"drug discovery\\\" OR \\\"personalized medicine\\\" OR \\\"treatment planning\\\") AND\\u003c/p\\u003e\\u003cp\\u003e(trends OR issues OR challenges OR concerns OR risks OR limitations OR barriers) AND\\u003c/p\\u003e\\u003cp\\u003e(\\\"bibliometric analysis\\\" OR \\\"scientometric analysis\\\" OR \\\"systematic review\\\" OR \\\"literature analysis\\\")\\u003c/p\\u003e\\u003cp\\u003eThe entire search string is designed to retrieve review-type studies or bibliometric/scientometric analyses focused on AI applications in healthcare, specifically examining trends, challenges, and issues in fields such as diagnostics, drug discovery, personalized medicine, and treatment planning. In short, it filters for papers that discuss AI in healthcare, focusing on specific applications, addressing trends and critical issues, and using review or bibliometric methods to analyze existing research. To ensure comprehensive coverage, literature data were extracted from major academic databases such as Scopus, chosen for its extensive multidisciplinary coverage and detailed citation tracking capabilities. Filters were applied to publication years from 2021 to 2025, and document types were articles, book chapters, conference papers, and review papers. A systematic search strategy, employing carefully formulated Boolean search strings, was used to retrieve relevant publications. The search focused on combinations of keywords such as \\u0026ldquo;Healthcare,\\u0026rdquo; \\u0026ldquo;Drug Discovery,\\u0026rdquo; \\u0026ldquo;Treatment Planning,\\u0026rdquo; \\u0026ldquo;Trends,\\u0026rdquo; \\u0026ldquo;Issues,\\u0026rdquo; \\u0026ldquo;Personalized Medicine,\\u0026rdquo; \\u0026ldquo;Treatment Planning,\\u0026rdquo; and related terms. Bibliometric data were exported in standardized formats (CSV, BibTeX) for analysis. Tools utilized included VOSviewer for network visualization and mapping of co-authorship, co-citation, and keyword co-occurrence relationships. Bibliometrix package in RStudio for statistical analysis and thematic evolution tracking. Microsoft Excel for descriptive statistics and data cleaning.\\u003c/p\\u003e\"},{\"header\":\"3 Results and Discussion\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eOverview of the data\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1.\\u003c/strong\\u003e General information on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,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\\\" width=\\\"562\\\" height=\\\"541\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1 provides a detailed interpretation of the general bibliometric information on trends, issues, and concerns related to AI in diagnostics, drug discovery, personalized medicine, and treatment planning over a 5-year period (2021-2025), indicating a recent and contemporary focus in research. Research data were collected from 47 distinct sources, including journals, books, and conference proceedings. A total of 48 documents were included in the analysis, reflecting a modest yet focused volume of research outputs in this field. This high growth rate (121.34%) indicates a rapid increase in interest and publication output concerning AI applications in healthcare diagnostics, drug discovery, personalized medicine, and treatment planning. On average, the documents are less than 1 year old (0.792 years), indicating that the majority of studies are highly recent, underscoring the evolving and cutting-edge nature of this field. Each document, on average, has received 22.5 citations, which suggests that the research in this field is gaining attention and recognition relatively quickly, considering the newness of the publications. Across all documents, there are 333 index keywords, reflecting a broad and diverse set of topics, methods, and technologies explored in the field. The authors themselves used 200 unique keywords, highlighting key themes and concepts directly prioritized by researchers in their work. The 48 documents were authored by 158 researchers, suggesting multi-author collaboration is common. Out of 48 papers, 11 were written by single authors, indicating that while collaboration is prevalent, individual research contributions still exist. On average, each document has just over three co-authors, suggesting moderate collaboration levels per study. A quarter (25%) of all documents involved collaboration between authors from different countries, indicating significant but not dominant international cooperation. The majority of publications (28) are journal articles, reflecting primary, peer-reviewed research outputs. A smaller portion (2) of research contributions is in book chapter format. Conference papers (10) form a significant portion, reflecting early-stage or rapidly disseminated research. Review papers (8) indicate efforts to synthesize and evaluate existing research, suggesting a maturing field with reflective assessments. This data indicates that AI's role in healthcare applications, including diagnostics, drug discovery, personalized medicine, and treatment planning, is a rapidly growing and evolving field of research. The field is characterized by recent, high-impact publications, moderate collaboration levels, and a mixture of article types, suggesting both foundational studies and synthesis works. The high growth rate and citation counts reflect rising academic and possibly industry interest, likely driven by AI’s potential to transform medical practice.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Annual Scientific Production chart provides a clear visualization of research output trends in AI applications for diagnostics, drug discovery, personalized medicine, and treatment planning from 2021 to 2025. Between 2020 and 2024, a strong upward trend is observed in scientific publications, reflecting a rapid growth in research interest and output in this specialized AI healthcare domain. A gradual increase in publications, reaching close to 100 articles in 2021. This indicates a growing recognition of AI’s potential in healthcare, although research is still in its early stages of development. A more significant rise, surpassing 150 articles in 2022. This year marks the early acceleration phase, possibly influenced by global health challenges (such as post-pandemic emphasis on digital health innovations). A sharp spike in publications to around 250–300 articles in 2023, showing clear momentum in research investments, academic focus, and industry interest. A dramatic surge occurs, with annual publications peaking above 500 articles in 2024. This suggests that AI’s role in healthcare has moved from exploration to widespread investigation and application. Improved AI models, increased funding, practical deployments, and pilot projects in clinical settings, along with growing demand for solutions in precision medicine, could drive sharp growth. The chart only shows data up to 2024, assuming continuity from prior trends; 2025 may stabilize near 2024 levels or potentially grow further, but this cannot be directly inferred from the chart. After the sharp peak in 2024, there is a very slight decline or plateau at the top. This might indicate a saturation point in publication volume, consolidation of research findings, and a shift from foundational studies to applied, commercial, or clinical implementation. The trend shows exponential growth that reflects rapid technological progress in AI models relevant to healthcare; the surge aligns with global healthcare digitalization trends, possibly influenced by real-world applications such as AI-assisted diagnostics and precision drug development, and the field is transitioning from conceptual exploration toward widespread, applied research, as evidenced by the high volume of recent outputs. The annual scientific production chart illustrates that AI applications in healthcare are experiencing a research boom, particularly from 2022 onwards, culminating in a potential peak or stabilization phase by 2024. This reflects an active and highly dynamic field poised for practical adoption, although future research directions may shift toward evaluating effectiveness, addressing ethical concerns, and implementing real-world applications.\\u003c/p\\u003e\\n\\u003cp\\u003eThe chart illustrates a declining trend in average citations per year, despite a sharp rise in research output during the same period (as shown in Figure 1). This indicates that although more papers are being published, they are receiving fewer citations on average over time. During the year 2020–2021, citations per article were highest in the earliest years, averaging around 12 citations per paper. This suggests that early foundational or pioneering works in AI-assisted healthcare attracted significant attention and were frequently cited. A decline to around 8–9 citations per paper in 2022. This drop could reflect newer publications having had less time to accumulate citations and a rapid influx of publications diluting citation attention. A temporary increase to nearly 12 citations per paper in 2023 suggests that certain impactful studies or landmark publications may have appeared this year, driving citation rates back up temporarily. A sharp decline to approximately five citations per article in 2024. This is significant because a large volume of newer publications from 2024 (as shown in Figure 1) are likely too recent to have accumulated many citations, and the saturation of the research field may mean that individual studies receive less attention. The lowest point on the chart is expected to be reached in 2025, with approximately 1 citation per article. This is likely because articles published in 2025 are too new to have been widely cited yet. Initial publications (2020–2021) have the highest average number of citations, indicating their foundational importance and possibly novel contributions that have shaped subsequent research. As publication volume surged in 2023–2024, citation averages fell—likely because the field became crowded, research outputs became more specialized or incremental rather than groundbreaking, and recent papers have not had time to accumulate citations. Recent papers (2024–2025) naturally show low average citations due to the inherent lag between publication and citation accumulation. Articles from 2024 and 2025 may experience a rise in citations in the future as they mature within the literature ecosystem. Figure 2 illustrates a typical bibliometric pattern, where early papers dominate in citations, and newer publications, despite an increase in quantity, receive fewer citations initially. This suggests that while AI applications in healthcare are a growing research area, attention and influence remain concentrated in earlier, possibly more foundational studies. For researchers, this underlines the importance of publishing novel, high-impact work and the challenge of standing out amid a flood of recent publications.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 3, titled \\\"Most Relevant Sources (2021–2025),\\\" presents a horizontal bar chart identifying the top academic and research publications that have contributed to the literature on AI's role in diagnostics, drug discovery, personalized medicine, and treatment planning during this period. The figure shows that the X-axis (horizontal) represents the Number of Documents published by each source, and the Y-axis (vertical) lists the names of the sources/journals. Each blue dot represents 1 document; the number inside the black circle indicates the total document count for the most prominent source. \\\"Social Science and Medicine\\\" is the most prominent source with two documents. This reflects a notable trend: AI in healthcare is being explored not just technically but also in terms of social, ethical, and medical implications. This journal likely addresses the societal impacts, ethical dilemmas, and regulatory concerns surrounding the integration of AI into clinical workflows. Nine other sources each contributed 1 document. These include a mix of technical, medical, and legal publications, indicating a highly interdisciplinary interest in the topic such as Technical AI/CS journals: e.g., ACM Computing Surveys, ACM Proceedings, Artificial Intelligence Review, Applied Computational Intelligence and Soft Computing; Medical/Pharma journals: Chinese Journal of Pharmacovigilance, reflecting interest in drug safety and discovery using AI; Law and ethics: Brazilian Journal of International Law, possibly addressing global regulations and ethics in AI-driven healthcare and Research accountability: Accountability in Research likely discusses transparency, bias, and reproducibility issues in AI applications. Meanwhile, conferences such as AIES 2023 (Artificial Intelligence, Ethics, and Society) and the IEEE Uttar Pradesh Section International Conference 2024 suggest a recent momentum and academic focus on AI's implications in real-world clinical and ethical contexts. Interdisciplinarity is a dominant theme, meaning that AI in healthcare is not confined to computer science but spans medicine, ethics, law, and social sciences. Ethical and regulatory concerns are as prominent as technical advancements. This is evident from contributions by journals focused on accountability, law, and social sciences. Balanced academic interest, as there is no single journal that dominates the field (except one with just two papers), indicating that research is still diversifying and exploratory. New conferences and proceedings reflect the increased academic attention in recent years (2023-2024), particularly since the pandemic, as AI applications in healthcare have accelerated. Figure 3 illustrates that from 2021 to 2025, research on AI in diagnostics, drug discovery, personalized medicine, and treatment planning is gaining traction across multiple disciplines, raising complex societal and regulatory issues, beyond mere technical feasibility, and is still emerging, with contributions scattered across various sources rather than concentrated in a few core journals. This diverse pattern signals an evolving field that requires collaboration across medicine, computer science, ethics, and policy to integrate AI into healthcare systems in a responsible manner.\\u003c/p\\u003e\\n\\u003cp\\u003eThis horizontal bar chart visualizes the number of documents published by corresponding authors from various countries in the field of AI in diagnostics, drug discovery, personalized medicine, and treatment planning. It also differentiates between Single Country Publications (SCP) and Multiple Country Publications (MCP), indicating whether the paper was written by authors from a single country or resulted from international collaboration. The top three contributors are India, with a total of four documents, all of which are Single Country Publications (SCPs), indicating vigorous domestic research activity but limited international collaboration. Next is Australia, with three documents, two SCPs, and one MCP, which demonstrates a mix of domestic research and global collaboration. The third group comprises Indonesia, Canada, China, and Colombia, with each country contributing 2–3 documents. Each has at least 1 MCP, suggesting these countries are engaging in cross-border collaborations more actively than India. There is a balanced international collaboration between Korea, Poland, the United Kingdom, and the USA, each of which has two documents, including both SCP and MCP (or just SCP). This reflects a moderate contribution with some international engagement. Countries with Only SCPs (No MCPs) include France, Iran, Italy, Portugal, Qatar, Romania, South Africa, Sweden, and the UAE. Each contributed one paper, all from within their own country. This likely reflects either Individual research efforts (perhaps by a single research group) or early-stage involvement in this domain without yet forming international collaborations. Finland has only one publication, and it is an MCP; this suggests that Finland's contribution is entirely internationally collaborative. India leads in volume but lags in collaboration, having the highest number of papers but no MCPs. Potentially due to strong internal research networks or barriers to international collaboration, such as funding, policy, or focus on domestic applications. Emergence of Australia and Canada as Hybrid Contributors for the significant contributions with both SCPs and MCPs. Indicates mature research environments that are both self-sufficient and internationally connected. There is a widespread but fragmented global involvement, as many countries contribute only 1 document, mostly SCPs. Suggests growing but scattered global interest in AI for healthcare, possibly fueled by isolated initiatives, funding from national programs, and one-time collaborations. There is a low rate of international collaboration because SCPs dominate most bars. While international interest is present, true cross-border research collaboration is still limited. Points to an opportunity to expand global cooperation, which is critical in healthcare innovation and AI ethics governance. Figure 4 shows that India is the top producer in volume, but it works primarily independently. Countries like Australia, Canada, and China show more balanced profiles, engaging in both domestic and international projects. International collaboration is still in its early stages, with many countries yet to engage in cross-border AI healthcare research. There is a clear opportunity to strengthen global networks for tackling healthcare challenges using AI through greater multi-country research partnerships.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 5 points out that darker blue shades indicate countries with higher levels of scientific production, lighter blue shades represent moderate output, and gray or very light-colored countries indicate minimal or no recorded scientific production (based on the dataset used). The countries shaded in dark blue are leading in scientific output, such as the United States, which is most prominent, likely the top producer; China also has very high production; India, the United Kingdom, Germany, and Japan have strong contributors, and Australia and Canada are also among the top producers. Countries shaded in medium blue indicate moderate levels of scientific publication, including Brazil, Russia, South Korea, Italy, France, Spain, Turkey, Iran, and South Africa. These countries have active scientific communities but contribute less than the top-tier nations. Countries in gray or very light blue, such as many African nations, parts of Central Asia, and a few countries in Central America and the Caribbean. These may have limited research infrastructure, lower investments in R\\u0026amp;D, or data unavailability in the database used in Scopus. Trends observed signify that countries in North America, Western Europe, and East Asia are the global scientific powerhouses. South America and the Middle East show emerging scientific activity. Meanwhile, Africa and parts of Southeast Asia appear underrepresented. This map reflects global disparities in research funding, education, infrastructure, and international collaboration. It may guide international policy efforts to support underrepresented regions through partnerships and investments.\\u003c/p\\u003e\\n\\u003cp\\u003eThe graph visualizes the number of scientific articles published from 2021 to 2025 by five countries: Australia, India, Indonesia, the United Kingdom, and the USA. The trend overview highlights that all five countries show positive trends in article production, although growth rates vary. India and Indonesia are expected to show the most rapid increase by 2025; Australia and the United Kingdom are projected to maintain steady growth, while the USA is expected to exhibit early growth and then plateau. As for country-by-country analysis, Australia (Red Line) starts with two articles in 2021, gradually increases each year, peaks at 11 articles in 2024, remains constant in 2025, and shows early and consistent leadership in article production. India (Olive Line) starts low at 1 article in 2021; gradually grows until 2023, with a sharp increase between 2024 (6 articles) and 2025 (13 articles), reaching its highest output in 2025, surpassing all other countries. Indonesia (Green Line) has no publications until 2024, followed by a sudden increase in 2025 to 7 articles, indicating a late start but an emerging presence in research output. United Kingdom (Blue Line) starts at 0 in 2021, rises steadily to 7 articles by 2023, plateaus at seven until 2024, then grows to 9 in 2025 and remains a steady and consistent contributor, with a late boost. The USA (Pink Line) starts at 1 article in 2021, remains flat until 2023, then gradually rises to 6 in 2024 and 9 in 2025, reflecting a modest but stable increase. Key observations include India showing the most dramatic growth, suggesting increased research funding or prioritization of publication. Australia had the earliest and strongest momentum, but growth slowed after 2024. Indonesia’s late entry suggests a newly developing research effort. The USA and UK, typically strong in research, show moderate performance here, possibly due to the scope or filters applied to the data in AI for healthcare. There is a growing diversification in global participation in scientific research. Emerging economies, such as India and Indonesia, are becoming key players. Research trends are not static; countries can leap forward rapidly with the proper support and infrastructure in place.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 6 presents a horizontal bar graph of the top 10 countries based on the number of citations their research articles have received. \\u0026nbsp;This graph highlights research impact, not just production. Citations reflect how influential or widely referenced a country’s scientific work is within the global research community. The United Kingdom dominates the chart with 442 citations, far exceeding those of all other countries, indicating exceptionally high-impact research, which suggests strong international visibility and possibly leading roles in collaborative studies. Australia and Finland have a very close citation count (87 and 86, respectively), indicating high-quality research output that is frequently cited, even though the total number of articles is fewer than those of top-producing countries like India or the USA. China (55) and Iran (38) show moderate impact, reflecting a growing influence, particularly in fields where citation rates are increasing (e.g., AI, healthcare). However, lower citation counts, especially in the USA, with only 1 citation, which is surprising. This may be due to the limited number of articles in the analyzed dataset, newer publications not yet cited, and a focus on a niche field where citation accumulation is slow. The USA and India were more productive in the earlier graph, but their work appears less cited here, showing that impact is not proportional to output. The UK’s significant lead could be attributed to influential review articles or foundational studies, participation in high-profile international projects, and publications in high-impact journals. Countries like Colombia, Iran, and Korea show promising citation levels despite limited global presence, historically indicating a rise in research relevance. This graph emphasizes the quality and international recognition of scientific work. While some countries may publish more, others (like the UK and Finland) are producing research that the global community relies upon and cites heavily.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 7 is a word cloud, which visually represents the frequency or importance of terms related to a specific topic. In this case, the topic appears to be the ethics and governance of Artificial Intelligence (AI). The larger and bolder the words, the more frequently they appear or the more critical they are in the analyzed text or dataset. The dominant terms are the most prominent, comprising artificial intelligence, ethical technology, systematic literature review, AI ethics, and responsible AI. These terms are the most central themes, likely indicating a strong focus on evaluating and guiding AI development and implementation from an ethical, technological, and review-based perspective. Other significant concepts include ethics, algorithmic bias, data privacy, AI governance, accountability, trustworthy AI, human bias, and transparency. These terms suggest concerns about bias, privacy, accountability, and transparency in AI systems, which are critical aspects of ethical AI. Contextual/supportive concepts involve decision-making, systematic review, healthcare, economic and social effects, generative AI, and governance. These highlight the applications of AI in fields like healthcare and policy, and the importance of systematic approaches to analyzing its impact. This word cloud strongly emphasizes the interplay between artificial intelligence and ethics, with key themes around responsibility, bias, privacy, governance, and systematic review processes. It is likely derived from academic or policy-related literature exploring how AI can be developed and deployed ethically.\\u003c/p\\u003e\\n\\u003cp\\u003eThis image is a thematic map, commonly used in bibliometric or systematic literature reviews to visualize research themes based on two dimensions: the X-axis (Relevance degree/Centrality), which represents the importance or centrality of a theme within the overall field. Y-axis (Development degree / Density): How developed or specialized the theme is internally (i.e., the coherence of the research around it). \\u0026nbsp;Upper-Right: Motor Themes are considered to have high centrality and high density; these are well-developed and important to the field. Themes include artificial intelligence, ethical technology, and data privacy, which are core driving themes in the literature. They are both highly relevant and well-studied. The upper-left is the Niche Themes, which are characterized by low centrality and high density. These are well-developed but marginal, specialized, or isolated themes such as ethics, systematic review, and human. These are deeply researched but not central to the overall network; they are possibly more interdisciplinary or highly specialized in nature. Lower-Left: Emerging or Declining Themes, characterized by low centrality and density, often representing underdeveloped new areas or fading topics. Themes such as current, decentralized finance, ethical considerations, ethical artificial intelligence, and ethical implications are newer trends (like decentralized finance) or areas that are not yet fully explored or are losing traction. \\u0026nbsp;Lower-Right: Basic Themes, with high centrality and low density, with fundamental topics that are important but underdeveloped or general. Themes such as AI ethics, responsible AI, and systematic literature reviews are central to the field but require further development or are more conceptual/introductory in nature. The core of the research field revolves around AI, ethics, and data privacy, with strong development in ethical technology. Responsible AI and AI ethics are important, but they require further development, suggesting a growing interest, albeit still in their early stages. Specialized work is being done on ethics and systematic reviews, but they are not central to the broader research discussions. Emerging topics, such as decentralized finance and the ethical implications of AI, are on the periphery but may gain traction over time.\\u003c/p\\u003e\\n\\u003cp\\u003eThis image is a Conceptual Structure Map generated using Multiple Correspondence Analysis (MCA) — a common technique in bibliometric and co-word analysis. The purpose is to reveal the underlying intellectual structure of a field by clustering and projecting keywords/themes in a reduced-dimensional space. Dim 1 (X-axis) and Dim 2 (Y-axis) represent the first two dimensions extracted from the MCA, which explain the variation in the co-occurrence of keywords across documents. Although they are not labeled semantically, their relative positions help identify conceptual proximities and thematic clusters. Top-left Cluster (Upper-Left Quadrant): Key terms: Responsible artificial intelligence, trustworthy AI, responsible AI, philosophical aspects, ai ethics, patient care, transparency; Theme: Ethical \\u0026amp; Trustworthy AI in Social \\u0026amp; Medical Contexts; Focus: Emphasizes ethical and humanistic approaches to AI—particularly in sensitive domains like healthcare and patient care, and includes a philosophical foundation; Conceptual Maturity: This cluster appears internally coherent, suggesting a developed subfield. \\u0026nbsp;Top-right Cluster (Upper-Right Quadrant): Key terms: Large language model, health care delivery, clinical practice guideline, algorithm bias, fairness, health policy; Theme: Applied AI in Healthcare and Fairness; Focus: Intersection of AI tools (e.g., LLMs) with healthcare systems, fairness, and health policy—real-world implementations and ethical oversight; Trend: This is a cutting-edge, rapidly evolving area with practical and policy implications. \\u0026nbsp;Center Cluster: Key terms: Artificial intelligence, systematic review, data privacy, accountability, ethics, bias, technology; Theme: Core AI Ethics Discourse; Focus: This central area represents the foundational discourse around AI, touching on bias, ethics, privacy, and systematic literature reviews; Interpretation: It acts as the intellectual “glue” that links more specialized subfields. Bottom-left Cluster (Lower-Left Quadrant): Key terms: Education, students, engineering education, ethical dilemma, research trends, education computing; Theme: AI Ethics in Education; Focus: Addresses the ethical challenges and use of AI in educational contexts, including higher education and computing education; Stage: Possibly underdeveloped or emerging. Bottom-right / Peripheral Themes: Key terms: International cooperation, law, public health, stakeholder, male; Theme: Policy and Global Dimensions; Focus: International law, cooperation, and stakeholder involvement in AI governance; Position: Somewhat peripheral, suggesting these topics are not tightly integrated with the core discourse but are important extensions. Overall, the field is broadly structured around Core ethical considerations in AI (center), Applied concerns in healthcare (upper right), Philosophical and humanistic ethics (upper left), Educational applications and concerns (lower left), and Global governance/policy (lower periphery). Furthermore, Dim 1 (X-axis) reflects a spectrum from technical/applied topics (right) to conceptual/ethical concerns (left), and Dim 2 (Y-axis) separates abstract/philosophical themes (top) from practical/policy themes (bottom).\\u003c/p\\u003e\\n\\u003cp\\u003eThis is a Country Collaboration Map, commonly used in bibliometric or scientometric analysis to visualize international research partnerships. It illustrates how actively countries are involved in collaborations on a specific research topic, likely AI ethics or related fields, based on previous maps. Darker blue denotes higher research output and collaboration frequency, while lighter blue denotes lower collaboration activity; grey denotes no or negligible participation in the analyzed literature. The United States is the dominant collaborator, with the darkest blue shade, indicating the highest volume of collaborative publications, and serves as a central hub in global research collaborations. The United Kingdom is highly active in international collaboration, which is directly linked with the United States by a visible connection line, suggesting a strong bilateral research relationship. India is a significant contributor with high collaboration levels, which is likely to collaborate with both Western countries and neighboring Asian partners. China is also prominently involved; however, fewer visible direct collaboration lines might indicate more domestic publication or selective partnerships. Australia, Canada, Germany, France, and Italy show strong involvement, medium to dark blue. These countries have active roles in international research, likely collaborating with the US. Other notable collaborators, such as South Korea, Japan, Brazil, South Africa, Nigeria, Pakistan, Iran, Indonesia, and Argentina, exhibit moderate to low levels of collaboration. Their lighter blue indicates participation, though with fewer collaborations. Most collaborations are centered around the U.S. and the U.K., reflecting their roles as global leaders in academic and policy circles regarding AI and ethics. Europe shows intra-regional collaboration, particularly among Western European countries. Asia is active, with India, China, and South Korea playing significant roles, though intercontinental links are less visually prominent. Africa and Latin America are underrepresented but still present, suggesting emerging engagement in the field. The U.S. and U.K. are the dominant collaborative hubs in AI ethics research. Global collaboration is broad but uneven, with most activity concentrated in North America, Western Europe, and certain parts of the Asia-Pacific region. Developing regions are underrepresented, which may point to disparities in access, funding, or infrastructure in AI ethics research.\\u003c/p\\u003e\\n\\u003cp\\u003eThis is a term co-occurrence network map generated using VOSviewer, a tool commonly used in bibliometric analysis to visualize relationships among keywords or terms extracted from a body of academic literature. The purpose of this network map is to illustrate the frequency with which terms co-occur in research publications and the degree of relatedness between different concepts within the scholarly discussion associated with AI in healthcare/ethics, based on the terms. Node Size (Font Size of Terms), such as “artificial intelligence”, “machine learning”, “deep learning”, “healthcare”, “humans”, and “personalized medicine” are the most dominant and widely discussed topics. It also indicates the frequency of occurrence of the term in the dataset. The Edges (Connecting Lines) show co-occurrence links: how frequently two terms appear together. The thicker/denser lines indicate stronger relationships. Terms such as artificial intelligence, machine learning, deep learning, natural language processing, and support vector machine are foundational AI techniques and appear as the core research axis in this domain. Terms such as healthcare, healthcare system, patient care, healthcare personnel, electronic medical record, and telemedicine demonstrate how AI is applied across the healthcare infrastructure and clinical workflows. Terms such as personalized medicine, predictive value, treatment planning, biomarkers, and cancer types (including breast, prostate, and lung) focus on AI’s use in diagnostics, genomics, and individualized treatment strategies. Terms such as systematic review, meta-analysis, data extraction, PRISMA, literature review, and Scopus indicate strong emphasis on evidence synthesis, methodology, and bibliometric analysis in this body of work. Terms like ethics, ethical technology, consensus, confidentiality, and ethical challenges, as well as concerns around AI applications, are part of the literature but remain more peripheral. Terms such as radiomics, oncology, biomarkers, chemotherapy, tumor microenvironment, and drug development refer to the use of AI in diagnostic imaging, drug discovery, and cancer care. Overall patterns suggest that the dense web of connections suggests a mature, interdisciplinary field with a strong conceptual core. AI is tightly coupled with both clinical applications and research methodologies, reflecting a robust interest in both practice and policy. Peripheral areas (ethics, specialty domains) suggest growing but still developing research subfields.\\u003c/p\\u003e\"},{\"header\":\"4 Conclusion\",\"content\":\"\\u003cp\\u003eThis bibliometric analysis offers a comprehensive overview of recent scientific trends, issues, and collaborative patterns related to the application of Artificial Intelligence (AI) in healthcare, specifically in diagnostics, drug discovery, personalized medicine, and treatment planning. The rapid growth in publication volume from 2021 to 2025 reflects an intensifying global interest in the transformative potential of AI to address longstanding healthcare challenges, including diagnostic delays, treatment inefficiencies, and the need for individualized care strategies. Key themes identified in co-occurrence and conceptual maps confirm that artificial intelligence, machine learning, deep learning, and personalized medicine are central to the discourse, with applications spanning cancer diagnostics, radiomics, genomics, and predictive analytics. A thematic shift is evident, with earlier research focused on foundational AI methods, now expanding toward more applied, interdisciplinary, and ethically informed domains. Emerging topics such as explainable AI, algorithmic fairness, and data privacy signal a maturing research field that is grappling not only with technical innovation but also with governance, trust, and societal impact. The structural maps reveal a fragmented yet globally expanding research landscape. While countries such as the United States, the United Kingdom, India, China, and Australia dominate output, international collaboration remains uneven, suggesting opportunities for deeper cross-border and cross-disciplinary partnerships. Citation analyses highlight the outsized influence of early publications and the emerging impact of recent studies, especially those addressing both technological performance and ethical frameworks. Despite the impressive momentum, the field continues to face persistent challenges. Declining citation averages over time may indicate content saturation or the difficulty of producing novel contributions amidst a flood of publications. Moreover, ethical and governance-related research remains somewhat peripheral, even as calls for responsible AI grow louder. Low participation from developing regions reflects ongoing global disparities in research capacity, underscoring the need for inclusive strategies in AI deployment. Overall, the analysis confirms that AI is transitioning from conceptual promise to practical implementation in healthcare. For this shift to be sustainable, future research must emphasize robust validation, ethical oversight, equitable access, and interdisciplinary integration. Policymakers, funders, and researchers must collaborate to ensure that AI not only enhances healthcare outcomes but also does so in a manner that is transparent, accountable, and universally beneficial.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study did not receive monetary funds.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical Trial Number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot Applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eSINGLE AUTHOR ONLY\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eHou, Y. J., Xie, J. B., Zhai, Y., \\u0026amp; Lu, G. (2025). Evolution of artificial intelligence in healthcare: a 30-year bibliometric study. Frontiers in Medicine, 11. https://doi.org/10.3389/fmed.2024.1505692\\u003c/li\\u003e\\n\\u003cli\\u003eChowdhury, R. (2024). Intelligent systems for healthcare diagnostics and treatment. World Journal of Advanced Research and Reviews, 23(1), 007\\u0026ndash;015. https://doi.org/10.30574/wjarr.2024.23.1.2015\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Y.-X., Zhu, C., Wu, Z.-X., Lu, L., \\u0026amp; Yu, Y.-T. (2022). A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness. Annals of Translational Medicine, 10(16), 854. https://doi.org/10.21037/atm-22-913\\u003c/li\\u003e\\n\\u003cli\\u003eApriyanto, N. P., Alamsyah, N., Wijaya, O., Loniza, E., \\u0026amp; Satriyandari, Y. (2024). Roles of AI in Healthcare. 289\\u0026ndash;294. https://doi.org/10.1109/icitcom62788.2024.10762060\\u003c/li\\u003e\\n\\u003cli\\u003eBhagat, I. A., Wankhede, K. G., Kopawar, N. A., \\u0026amp; Sananse, D. A. (2024). Artificial Intelligence in Healthcare: A Review. International Journal of Scientific Research in Science, Engineering and Technology, 11(4), 133\\u0026ndash;138. https://doi.org/10.32628/ijsrset24114107\\u003c/li\\u003e\\n\\u003cli\\u003eGencer, A. (2024). Bibliometric analysis and research trends of artificial intelligence in lung cancer. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e24665\\u003c/li\\u003e\\n\\u003cli\\u003eRich, E., \\u0026amp; Winston, P. (2024). AI in Healthcare. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-15285\\u003c/li\\u003e\\n\\u003cli\\u003eAndrade-Arenas, L., \\u0026amp; Yactayo-Arias, C. (2024). A bibliometric analysis of the advances of artificial intelligence in medicine. International Journal of Electrical and Computer Engineering. https://doi.org/10.11591/ijece.v14i3.pp3350-3361\\u003c/li\\u003e\\n\\u003cli\\u003eDhudum, R., Ganeshpurkar, A., \\u0026amp; Pawar, A. (2024). Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications. https://doi.org/10.3390/ddc3010009\\u003c/li\\u003e\\n\\u003cli\\u003eGuo, J., Tong, H. H. Y., \\u0026amp; To, W. M. (2024). Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review. Mini-Reviews in Medicinal Chemistry. https://doi.org/10.2174/0113895575271267231123160503\\u003c/li\\u003e\\n\\u003cli\\u003eKhandagale, P. (2024). Emerging trends in drug discovery: Harnessing artificial intelligence and machine learning for drug development. 12(03), 56\\u0026ndash;61. https://doi.org/10.31690/ipplanet.2024.v012i03.016\\u003c/li\\u003e\\n\\u003cli\\u003eDatta, D., \\u0026amp; De, A. (2023). Impact of Artificial Intelligence on Medical Literature. Acta Scientific Otolaryngology, 5(8), 09\\u0026ndash;12. https://doi.org/10.31080/asol.2023.05.0586\\u003c/li\\u003e\\n\\u003cli\\u003eDe Nicol\\u0026ograve;, V., \\u0026amp; La Torre, D. (2024). Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012\\u0026ndash;2022) (pp. 151\\u0026ndash;177). Elsevier BV. https://doi.org/10.1016/b978-0-443-13671-9.00004-1\\u003c/li\\u003e\\n\\u003cli\\u003eVijayan, V. K., Is, S., \\u0026amp; John, J. (2024). An insight on the interventions of AI in healthcare\\u0026mdash;A bibliometric study. \\u003cem\\u003eJournal of Autonomous Intelligence\\u003c/em\\u003e. https://doi.org/10.32629/jai.v7i4.1148\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026Ouml;zalp, S. (2024). AI and Personalized Treatment. \\u003cem\\u003eNext Frontier.\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e(1), 179. https://doi.org/10.62802/e6fyw805\\u003c/li\\u003e\\n\\u003cli\\u003eKhanna, P. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. https://doi.org/10.3390/pharmaceutics16101328\\u003c/li\\u003e\\n\\u003cli\\u003eNihalani, G. (2024). Applications of Artificial Intelligence in Pharmaceutical Research: An Extensive Review. Bioequivalence \\u0026amp; Bioavailability International Journal, 8(1), 1\\u0026ndash;7. https://doi.org/10.23880/beba-16000231\\u003c/li\\u003e\\n\\u003cli\\u003eShah, P. N., \\u0026amp; Shah, R. P. (2023). Artificial Intelligence in the Paradigm Shift of Pharmaceutical Sciences: A Review. Nano Biomedicine and Engineering. https://doi.org/10.26599/nbe.2023.9290043\\u003c/li\\u003e\\n\\u003cli\\u003eHussain, W., Mabrok, M. A., Gao, H., Rabhi, F. A., \\u0026amp; Rashed, E. A. (2024). Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. \\u003cem\\u003eDigital Health\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e. https://doi.org/10.1177/20552076241258757\\u003c/li\\u003e\\n\\u003cli\\u003eChawla, M. (2024). Artificial Intelligence in Drug Discovery: Transforming the Future of Medicine. \\u003cem\\u003ePremier Journal of Science.\\u003c/em\\u003e, \\u003cem\\u003e2024\\u003c/em\\u003e. https://doi.org/10.70389/pjs.100034\\u003c/li\\u003e\\n\\u003cli\\u003eGlicksberg, B. S., \\u0026amp; Klang, E. (2024). Unveiling Recent Trends in Biomedical Artificial Intelligence Research: Analysis of Top-Cited Papers. \\u003cem\\u003eApplied Sciences\\u003c/em\\u003e. https://doi.org/10.3390/app14020785\\u003c/li\\u003e\\n\\u003cli\\u003eRastogi, M. (2023). The growth and potential of ai applications in medicine and healthcare. \\u003cem\\u003eIndian Journal of Applied Research\\u003c/em\\u003e. https://doi.org/10.36106/ijar/7206074\\u003c/li\\u003e\\n\\u003cli\\u003eKabilan, K. (2024). \\u003cem\\u003eAI in Drug Development: Speeding Up the Search for New Medicines \\u0026ndash; A Comprehensive Review\\u003c/em\\u003e. https://doi.org/10.14293/pr2199.001372.v1\\u003c/li\\u003e\\n\\u003cli\\u003eOkolo, C. A., Olorunsogo, T., \\u0026amp; Babawarun, O. (2024). A comprehensive review of AI applications in personalized medicine. \\u003cem\\u003eInternational Journal of Science and Research Archive\\u003c/em\\u003e, \\u003cem\\u003e11\\u003c/em\\u003e(1), 2544\\u0026ndash;2549. https://doi.org/10.30574/ijsra.2024.11.1.0338\\u003c/li\\u003e\\n\\u003cli\\u003eWeerarathna, I. N. (2024). \\u003cem\\u003eArtificial intelligence in healthcare\\u003c/em\\u003e (pp. 346\\u0026ndash;362). https://doi.org/10.58532/v3biai4p4ch2\\u003c/li\\u003e\\n\\u003cli\\u003eGuo, Y., Hao, Z., Zhao, S., Gong, J., \\u0026amp; Yang, F. (2020). Artificial Intelligence in Health Care: Bibliometric Analysis. \\u003cem\\u003eJournal of Medical Internet Research\\u003c/em\\u003e, \\u003cem\\u003e22\\u003c/em\\u003e(7). https://doi.org/10.2196/18228\\u003c/li\\u003e\\n\\u003cli\\u003eBhattamisra, S. K., Banerjee, P., Gupta, P., Mayuren, J., Patra, S., \\u0026amp; Candasamy, M. (2023). Artificial Intelligence in Pharmaceutical and Healthcare Research. \\u003cem\\u003eBig Data and Cognitive Computing\\u003c/em\\u003e, \\u003cem\\u003e7\\u003c/em\\u003e(1), 10. https://doi.org/10.3390/bdcc7010010\\u003c/li\\u003e\\n\\u003cli\\u003eKhanday, S., \\u0026amp; Ahmed, M. (2024). Leveraging Artificial Intelligence for Enhanced Healthcare Diagnostics: Opportunities and Challenges. \\u003cem\\u003eInternational Journal of Science and Research\\u003c/em\\u003e. https://doi.org/10.21275/sr24316150834\\u003c/li\\u003e\\n\\u003cli\\u003eMottaghi-Dastjerdi, N., \\u0026amp; Soltany‐Rezaee‐Rad, M. (2024). Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review. \\u003cem\\u003eDeleted Journal\\u003c/em\\u003e, \\u003cem\\u003e23\\u003c/em\\u003e(1). https://doi.org/10.5812/ijpr-150510\\u003c/li\\u003e\\n\\u003cli\\u003eSenthil, R., Thirunavukarasou, A., Somala, C. S., \\u0026amp; Saravanan, K. M. (2024). Bibliometric analysis of artificial intelligence in healthcare research: trends and future directions. \\u003cem\\u003eFuture Healthcare Journal\\u003c/em\\u003e, \\u003cem\\u003e11\\u003c/em\\u003e(3), 100182. https://doi.org/10.1016/j.fhj.2024.100182\\u003c/li\\u003e\\n\\u003cli\\u003eSerrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., S\\u0026aacute;nchez-Guirales, S. A., Mart\\u0026iacute;nez Sim\\u0026oacute;n, J., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., \\u0026amp; Lalatsa, A. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. \\u003cem\\u003ePharmaceutics\\u003c/em\\u003e, \\u003cem\\u003e16\\u003c/em\\u003e(10), 1328. https://doi.org/10.3390/pharmaceutics16101328\\u003c/li\\u003e\\n\\u003cli\\u003eBhabad, S., Lamkhade, D., Koyate, S., Karanjkhele, K., Kale, V., \\u0026amp; Doke, R. (2023). Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research. \\u003cem\\u003eIP International Journal of Comprehensive and Advanced Pharmacology\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e(4), 210\\u0026ndash;219. https://doi.org/10.18231/j.ijcaap.2023.034\\u003c/li\\u003e\\n\\u003cli\\u003eKatwaroo, A. R., Adesh, V. S., Lowtan, A., \\u0026amp; Umakanthan, S. (2023). The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. \\u003cem\\u003ePostgraduate Medical Journal\\u003c/em\\u003e. https://doi.org/10.1093/postmj/qgad135\\u003c/li\\u003e\\n\\u003cli\\u003eWu, L., Li, J., Guan, Q., \\u0026amp; Duan, J. (2023). \\u003cem\\u003eGlobal research trends of artificial intelligence applied in drug design: A bibliometric analysis based on Vosviewer and Citespace\\u003c/em\\u003e. https://doi.org/10.1145/3644116.3644324\\u003c/li\\u003e\\n\\u003cli\\u003eAmirineni, S. (2024). The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications. \\u003cem\\u003eWorld Journal of Advanced Research and Reviews\\u003c/em\\u003e, \\u003cem\\u003e23\\u003c/em\\u003e(2), 2026\\u0026ndash;2030. https://doi.org/10.30574/wjarr.2024.23.2.2559\\u003c/li\\u003e\\n\\u003cli\\u003ePurohit, S., \\u0026amp; Kumar C. N., S. (2024). \\u003cem\\u003eAI in Health Care: A Comprehensive Review\\u003c/em\\u003e. https://doi.org/10.52711/jnmr.2024.26\\u003c/li\\u003e\\n\\u003cli\\u003eZahoor, F., Abdullah, M., Tahir, M. W., \\u0026amp; Islam, A. (2024). \\u003cem\\u003eThe 100 most influential papers in medical artificial intelligence; a bibliometric analysis.\\u003c/em\\u003e \\u003cem\\u003e74\\u003c/em\\u003e(4), 752\\u0026ndash;761. https://doi.org/10.47391/jpma.10064\\u003c/li\\u003e\\n\\u003cli\\u003eBasubrin, O. (2025). Current Status and Future of Artificial Intelligence in Medicine. \\u003cem\\u003eCureus\\u003c/em\\u003e. https://doi.org/10.7759/cureus.77561\\u003c/li\\u003e\\n\\u003cli\\u003eUdegbe, F. C., Ebulue, O. R., Ebulue, C. C., \\u0026amp; Ekesiobi, C. S. (2024). \\u003cem\\u003eAi\\u0026rsquo;s impact on personalized medicine: tailoring treatments for improved health outcomes\\u003c/em\\u003e. https://doi.org/10.51594/estj.v5i4.1040\\u003c/li\\u003e\\n\\u003cli\\u003eMondal, P. K., Hasan, M. F., Rusho, M. A., \\u0026amp; Rahman, M. H. (2024). \\u003cem\\u003eA Systematic Literature Review on the Application of Artificial Intelligence and Machine Learning in Personalized Medicine: Methodological Advances and Emerging Trends\\u003c/em\\u003e. https://doi.org/10.31219/osf.io/f3jsz\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Concerns on AI, Drug discovery, Personalized medicine, Treatment planning, Bibliometric review\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7370235/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7370235/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study presents a comprehensive bibliometric analysis of the evolving role of Artificial Intelligence (AI) in healthcare, with a specific focus on diagnostics, drug discovery, personalized medicine, and treatment planning. Drawing upon data from the Scopus database between 2021 and 2025, the research examines 48 scholarly publications sourced from 47 journals, conference proceedings, and book chapters. The analysis aims to uncover prevailing research trends, collaboration patterns, thematic developments, and key concerns surrounding the integration of AI into critical healthcare domains. Findings reveal a significant surge in scientific production over the past five years, with an annual growth rate exceeding 120%, indicating heightened global interest in AI-driven healthcare solutions. Despite the rising volume of publications, the average number of citations per article showed a declining trend, highlighting the saturation of the field and a shift from foundational to more applied research. Thematic mapping and keyword analysis identified core research clusters centered on AI technologies such as machine learning, deep learning, and natural language processing applied to oncology diagnostics, clinical decision-making, and precision medicine. Emerging ethical themes, such as data privacy, algorithmic bias, transparency, and explainable AI, also surfaced, reflecting the growing interdisciplinary engagement. Geographically, countries such as India, the United States, Australia, and the United Kingdom lead in publication output, although international collaboration remains uneven, with many contributions being single-country efforts. Notably, citation impact does not always align with productivity, as evidenced by countries such as the UK and Finland, which have demonstrated high citation rates despite lower publication volumes. Visualization tools, such as VOSviewer and Bibliometrix, revealed an increasingly dense and diversified research landscape, with intellectual structures that bridge technical AI development, ethical governance, and healthcare implementation. While AI\\u0026rsquo;s integration into healthcare shows remarkable progress, the study identifies challenges in equitable collaboration, responsible innovation, and ensuring meaningful societal impact. The bibliometric insights offer valuable guidance for researchers, policymakers, and funders, emphasizing the need for interdisciplinary approaches, global cooperation, and ethical oversight to responsibly advance AI\\u0026rsquo;s transformative potential in healthcare.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A bibliometric review on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-25 08:47:45\",\"doi\":\"10.21203/rs.3.rs-7370235/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c4e23ef3-b3b3-4c19-8a0c-48972a75be48\",\"owner\":[],\"postedDate\":\"September 25th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-28T18:21:33+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-25 08:47:45\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7370235\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7370235\",\"identity\":\"rs-7370235\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}