Digital Health and the use of AI in Healthcare for Universal Health coverage- A PRISMA guided Systematic review

Digital Health and the use of AI in Healthcare for Universal Health coverage- A PRISMA guided Systematic review | 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 Digital Health and the use of AI in Healthcare for Universal Health coverage- A PRISMA guided Systematic review Abhijeet Prasad Sinha, Rohitashwa Kumar, Padmashree Ganapathyraman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9188979/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Introduction: Digital health and artificial intelligence are increasingly acknowledged as critical components in advancing Universal Health Coverage (UHC), particularly within low- and middle-income countries. Objectives The objective of this systematic review is to analyse the impact of digital health and AI-driven interventions on healthcare access, efficiency, quality, and equity, focussing specifically on India. Method A systematic review according to PRISMA guidelines was performed utilising PubMed, Scopus, and Web of Science for publications published from 2015 to 2025. Eligible studies underwent theme analysis. Results AI-powered digital health tools have shown potential benefits in areas such as diagnostics, patient engagement, and improving health systems and delivering services. Initiatives in India, such as the Ayushman Bharat Digital Mission, demonstrate how new, scalable systems are being developed to support Universal Health Coverage goals. Conclusion AI-driven digital health solutions are pivotal for attaining Universal Health Coverage, contingent upon the prioritisation of ethical governance, equity, and system integration. Digital health Universal health coverage Artificial Intelligence Telemedicine Chatbots AI Figures Figure 1 Figure 2 Figure 3 Introduction Universal Health Coverage (UHC) aims to provide comprehensive, high-quality healthcare services to all individuals without financial hardship, measured by service coverage and financial protection [ 1 ]. Countries worldwide encounter worker shortages, unequal access, and escalating expenses, especially in low-resource environments. Digital health technologies, such as telemedicine, electronic health records, mobile health, and artificial intelligence, have become essential facilitators of health system enhancement. Data from several international settings indicates enhanced accessibility, efficiency, and patient involvement via digital technologies Achieving UHC requires a skilled, well-distributed healthcare workforce and access to essential services across health promotion, treatment, and palliative care [ 2 ]. UHC, integral to the Sustainable Development Goals (SDGs), seeks to eliminate the economic burden of healthcare costs, reducing poverty linked to medical expenses. Digital health (DH), utilizing information and communication technology (ICT), is transforming healthcare by enhancing accessibility, efficiency, and patient engagement through tools like telemedicine and health monitoring apps [ 3 ]. These innovations democratize healthcare, improve health outcomes, and foster a patient-cantered approach, impacting social and environmental factors that influence health [ 4 ]. Artificial intelligence (AI) contributes to universal health coverage (UHC) by enhancing healthcare delivery through advanced data analysis and decision-support systems within the digital health ecosystem [ 5 ]. AI technologies, including machine learning, have the potential to refine clinical practice and inform health policy. However, their integration into UHC frameworks necessitates thorough evaluation to ensure that AI applications align with overarching healthcare objectives and contribute to equitable and effective health systems [ 6 ].Notwithstanding increasing popularity, substantial disparities persist in the equitable implementation, governance, and scalability of AI-driven healthcare solutions. This paper synthesizes global and Indian evidence to evaluate the potential of digital health initiatives, including artificial intelligence–enabled applications, to advance universal health coverage and address persistent systemic challenges [ 6 ]. This study aims to evaluate the impact of digital health (DH) initiatives, including artificial intelligence (AI)-driven interventions, on improving healthcare accessibility and quality in diverse healthcare settings worldwide. It also seeks to explore the implications of DH advancements for achieving equitable healthcare access and addressing health disparities, particularly in India and other developing countries. Objectives : To do systematic review of DH initiatives, including electronic health records (EHR), telemedicine platforms, and AI-driven interventions, in improving healthcare accessibility, quality, and equity across diverse populations and regions. To identify the facilitators and barriers to the successful implementation and adoption of DH technologies in India, comparing them with other developing countries. Methods Search Strategy The databases examined (PubMed, Scopus, and other grey literature records).The comprehensive search phrases and Boolean operators employed, the justification for choosing the 2015–2025 period, which signifies the swift advancement and policy significance of AI and digital health interventions in accordance with global and Indian UHC goals. Rationale for database selection predicated on their extensive coverage of biomedical, health systems, and digital health literature. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. The authors (Abhijeet Sinha and Rohitashwa Kumar) independently assessed all eligible studies, with consensus reached in case of disagreement. A comprehensive search of the PubMed database (up to May 2025) was conducted using the terms “Digital Health in Universal Health Coverage,” “AI in Digital Health,” and “AI for Universal Health Coverage.” A comprehensive literature search was performed in PubMed and Scopus databases (up to May 2025) using combinations of keywords pertaining to digital health, artificial intelligence, and universal health coverage. Search queries used Boolean combinations of core terms including “digital health,” “artificial intelligence,” and “universal health coverage.” Studies published between 2015 and 2025 were included to reflect contemporary digital health and AI-driven health system developments. A total of 3,469 records were identified across the databases, including 164 records on digital health and Universal Health Coverage, 3,163 on artificial intelligence in digital health, and 142 on artificial intelligence for Universal Health Coverage. Following the application of predefined inclusion and exclusion criteria, 3,429 records were excluded, resulting in 52 studies included in the final analysis (Fig. 1 ). Selection of Studies and Evaluation of Quality Two reviewers conducted independent screenings of titles, abstracts, and entire texts. The quality of the study was evaluated using established systematic review criteria relevant to the study design. Disputes were settled by consensus. Data Extraction The extracted data encompassed study design, geographical context, type of intervention, health domain, and significance to dimensions of Universal Health coverage. Data Analysis and Visualisation A theme synthesis methodology was employed, concentrating on access, quality, efficiency, equity, and governance ramifications. Inclusion Criteria : DH Initiatives : Articles discussing DH initiatives or programs implemented at national or regional levels. Global Coverage : Initiatives from various countries worldwide, including high-income and low- and middle-income countries (LMICs). Relevance to Universalisation of Healthcare (UHC) : Initiatives directly contributing to advancing UHC goals by improving healthcare accessibility, efficiency, quality, or equity. Technology Integration : Initiatives leveraging ICT, AI, or other digital tools to enhance healthcare delivery, data management, or health system performance. Diverse Settings : Initiatives implemented in diverse healthcare settings, including primary care facilities, hospitals, rural or remote areas, and underserved communities. Exclusion Criteria : Individual or Local Initiatives : Initiatives limited to individual healthcare facilities or local community projects with no broader implications for national or regional healthcare systems. Narrow Scope : Initiatives focused solely on specific diseases or health conditions without addressing broader healthcare delivery or system-level challenges. Non-Digital Interventions : Initiatives that do not utilize digital technologies or ICT tools for healthcare delivery or system improvement. Non-Relevant Content : Articles unrelated to the intersection of DH, AI, and UHC, such as general discussions on healthcare policy or unrelated topics. Study Selection and Quality Appraisal- The included studies were evaluated using relevant checklists corresponding to the study design (e.g., PRISMA guidelines and established critical appraisal criteria for systematic reviews). We assessed the methodological quality of the included studies using established critical appraisal criteria that matched each study design. The thematic categories in the manuscript are results of the synthesis, not methodological subheadings Results The review encompassed 52 studies. AI-powered digital health tools are used around the world, including in India, to support diagnostics, patient care, and more efficient health systems. Initiatives such as the Ayushman Bharat Digital Mission and AI-based diagnostic tools demonstrate how India can build digital health systems that support Universal Health Coverage. Still, there are ongoing challenges with infrastructure, data protection, and digital literacy. Globally and in India, artificial intelligence (AI) and digital health (DH) tools are increasingly adopted to enhance healthcare delivery and move towards Universal Health Coverage (UHC).AI is now being used more often in disease diagnosis, patient care, and the improvement of how healthcare systems work. These applications are still in the early stages, but they show promise for addressing challenges in healthcare delivery and could help improve access to care for underserved groups. AI technologies such as machine learning, computer vision, and deep learning are increasingly used to detect diseases. They play a role in finding early-stage cancer and diagnosing conditions such as diabetic retinopathy and tuberculosis. AI tools such as Manthana, which automates blood sample analysis, and telemedicine platforms including OnliDoc and Lybrate, which facilitate symptom assessment and provide treatment recommendations, exemplify the application of AI in India's healthcare sector. These tools are reported to support healthcare professionals and may improve diagnostic processes, particularly in resource-limited settings. In addition to disease detection, AI tools are being employed for process optimization in hospitals, including managing hospital bed utilization, inventory, and insurance claims. Predictive analytics and chatbots help forecast patient turnover and facilitate appointment management, improving healthcare efficiency. Wearable sensors and AI technologies are also used to monitor essential body functions, offering real-time insights to healthcare providers, which can be crucial in managing chronic diseases and surgical training. A key initiative in India, the Ayushman Bharat Digital Mission (ABDM), aims to enhance healthcare accessibility and safety using AI. By integrating health records across systems, ABDM allows for better decision-making and more efficient resource allocation, with patient privacy and consent as top priorities . India’s Rashtriya Swasthya Bima Yojana (RSBY) is a government program that uses digital tools to help improve healthcare access for marginalised groups. This example shows how digital technologies can support better healthcare for those in need. These initiatives show how AI-powered digital tools are becoming an increasingly important part of India’s health system as the country works toward universal health coverage. While India’s adoption of AI in healthcare is promising, challenges remain, particularly regarding equitable access. Vulnerable populations, especially in rural areas, may not fully benefit from these digital health solutions due to factors such as internet connectivity, digital literacy, and infrastructure constraints. To address these gaps, India must focus on improving digital literacy, enhancing infrastructure, and ensuring that AI solutions are accessible and user-friendly for all demographics. In addition, there is a need for robust data privacy policies to build trust in AI-powered systems. An example of AI use in the literature is Diseaseomics. This system combines biological ontologies with electronic health records (EHRs) to support diagnosis and clinical decision-making. Diseaseomics uses AI to map correlations between diseases and symptoms, enabling healthcare providers to make informed decisions in real-time. This tool has the capacity to significantly impact rural and underserved areas by providing medical knowledge that is typically accessible only in large healthcare centers. As AI technology improves, researchers have found that tools like Diseaseomics can help healthcare workers get the most up-to-date diagnostic information. Discussion This assessment shows how AI-powered digital health tools are helping advance Universal Health Coverage (UHC). Global data indicates enhanced service accessibility and efficiency, whereas India presents a persuasive example for extensive adoption. Nonetheless, ethical governance, interoperability, workforce capacity, and equal access persist as significant issues. Resolving these difficulties is crucial for the sustained integration of AI into healthcare systems. Digital Health Tools in UHC The integration of digital health (DH) tools into the universal health coverage (UHC) framework has expanded, though adoption remains uneven across specialties, populations and settings. Recent literature indicates increasing interest in healthcare models that emphasize collaboration, patient engagement, and enhanced access to personal health information. Patient portals enable secure communication between patients and clinicians and extend care beyond office visits by providing access to medical data. Despite the widespread availability of health information resources via the Internet and mobile applications, clinician uptake of technologies that enable accessible and convenient care has been slow. Regulatory barriers, resistance to patient-centred care models, and professional burnout are often mentioned as reasons why these technologies are not widely adopted. (Table 1 ) [ 7 ]. The literature identifies telemedicine as a promising strategy for enhancing healthcare accessibility, particularly by minimizing the necessity for patients to travel to medical facilities. However, its use remains limited and skewed toward urban areas with better access to the necessary technology (Figure) [ 8 ]. Personal monitoring devices, including wearable technology and internet-connected health tools, facilitate tracking health goals and monitoring vital signs between clinical appointments. Although integration with electronic health records (EHR) is still in its early stages, these tools hold potential for enhancing patient engagement and improving the efficiency of clinical workflows, provided the data remains simple and meaningful for both patients and healthcare providers sharing mechanisms. Thus, bridging the gap between DH tools and UHC requires addressing barriers to adoption and ensuring seamless integration within existing healthcare frameworks [ 9 ]. Artificial Intelligence in DH : AI is increasingly integrated within digital health (DH) applications to support healthcare delivery in the context of UHC. Through machine learning algorithms, AI optimizes diagnostics, treatment planning, and resource allocation, thereby enhancing the efficiency and quality of care. AI-driven platforms streamline patient management, improve clinical decision-making, facilitate remote consultations, and bridge healthcare gaps in underserved regions. AI applications automate repetitive tasks and analyze large datasets, thereby facilitating personalized care and contributing to the reduction of healthcare disparities. Overall, AI-enabled digital health tools are discussed as potential contributors to more accessible and efficient healthcare systems aligned with UHC goals. [ 10 ]. Chatbots AI-powered psychological tool, for reducing depression and anxiety symptoms among college students. The results indicated significant improvements in emotional well-being among users with unrestricted access to Tess compared to those receiving only informational resources. Tess's regular check-ins, either daily or biweekly, present a cost-effective and accessible solution for mental Healthcare, contributing to universal health coverage [ 10 , 11 ]. These findings highlight the potential of AI in mental health treatment, with further research needed to enhance the effectiveness of AI-driven solutions for psychological support explored the use of Woebot, an automated conversational agent, to provide mental health support for university students with anxiety and depression. The study showed significant reductions in depressive symptoms among users of Woebot compared to a control group. The high level of user engagement and the emphasis on empathy and therapeutic outcomes underscore the importance of integrating mental health professionals in the development of digital tools. Woebot is discussed as a scalable digital intervention with potential relevance for expanding access to mental health support [ 11 ].Zuri, a chatbot designed to provide psychological support to pregnant women and new mothers in Kenya. Although the impact on mood was modest, Zuri demonstrated the potential for AI-driven interventions to expand mental health services, particularly in resource-limited settings. The perceived anonymity of Zuri encouraged disclosure of mental health issues, highlighting a critical gap in mental health services and suggesting the need for continued research in this area. [ 10 , 11 ]. Surgery AI technologies, including machine learning (ML), computer vision (CV), augmented reality (AR), and anatomical segmentation, are increasingly applied in surgical practice. AI tools improve surgical performance by aiding in procedure comprehension, detection, and navigation. Systems like da Vinci enable remote surgeries, while AI segmentation enhances precision. AI-powered platforms facilitate access to expert advice in real-time. Despite challenges such as contextualizing visual data and ensuring robust localization, AI is enhancing surgical safety, management, and decision-making. The OR Black Box, for example, monitors procedures in real-time, improving outcomes and reducing costs [ 11 ]. Cancer Pharmacotherapy AI's application in cancer surveillance and epidemiology research has been examined in multiple studies. Using machine learning and natural language processing (NLP) techniques like BERT, AI analyses large healthcare databases, identifying patterns related to cancer incidence, treatment, and outcomes. This approach enhances predictive models, contributing to improved accuracy and efficiency in cancer surveillance. While challenges exist, such as data transparency and performance variability, AI shows promise for advancing cancer research and guiding evidence-based interventions [ 12 ]. Gastroenterology A systematic review of AI in colonoscopy found that deep convolutional neural network-based AI was associated with improved adenoma and polyp detection rates compared to standard colonoscopy. The study demonstrated AI's potential to enhance early detection, enabling better colorectal cancer prevention [ 13 ]. Liver and Kidney Transplantation AI, particularly artificial neural networks (ANNs), has been utilized to predict the success of liver transplants from deceased donors. AI models demonstrated higher predictive accuracy as compared to the traditional methods such as the Donor Risk Index (DRI) and the Model for End-Stage Liver Disease (MELD), offering better predictive accuracy and enhancing organ distribution and post-transplant outcomes [ 14 ]. In kidney transplants, machine learning techniques have shown potential for predicting graft failure, though results comparing these methods with traditional regression models remain inconclusive [ 15 ]. Chronic Obstructive Pulmonary Disease (COPD) Machine learning, particularly cluster analysis, is being used to identify distinct COPD phenotypes, enhancing diagnosis and personalized treatment plans. A systematic review highlighted the progress made in understanding COPD through AI but also noted gaps in the literature, calling for further research to refine phenotyping and clinical outcomes [ 16 ]. Table 1 ; AI utility in various Healthcare domain Healthcare Domain Role of AI Examples/Tools Mental Health Augmenting mental health support through integrative psychological AI Tess, Woebot, Zuri Surgery Supporting surgical practice through machine learning, computer vision, and augmented reality da Vinci surgical system, OR Black Box, POTTER app Cancer Pharmacotherapy Utilizing pharmacy data for the purpose of cancer surveillance and epidemiological research Natural language processing (NLP) techniques, namely Bidirectional Encoder Representations from Transformers (BERT) Gastroenterology Enhancing the results of colonoscopy procedures using artificial intelligence systems based on deep convolutional neural networks (DCNN). AI colonoscopy systems Liver and Kidney Transplantation Prediction of graft outcomes in liver and kidney transplantation using artificial intelligence techniques. Artificial neural networks (ANNs) Chronic Obstructive Pulmonary Disease Identifying distinct COPD phenotypes and improving diagnosis and treatment strategies through cluster analysis and machine learning Cluster analysis, machine learning algorithms Diabetes Mellitus Type 2 Predicting Diabetes Type 2 onset in community settings and improving predictive accuracy through machine learning algorithms Various machine learning algorithms Abdominal Aortic Aneurysm (AAA) Enhancing imaging analysis, quantitative assessment of AAA morphology, and prediction of AAA growth and rupture using AI Image segmentation, predictive and prognostic programs AI Applications in Healthcare: Current Trends and Future Directions: Chatbots Woebot, a fully automated conversational agent, led to notable reductions in depressive symptoms, emphasizing the importance of empathy and user engagement in digital mental health tools [ 11 ]. A chatbot delivering psychological support to pregnant women in Kenya via SMS and Facebook Messenger. Although the impact on mood was modest, the tool’s perceived anonymity facilitated disclosure of mental health concerns, underscoring [ 11 ]. AI’s potential in resource-constrained settings. Surgery AI, particularly machine learning (ML), computer vision (CV), and augmented reality (AR), is transforming surgical practices. AI improves precision in procedures through tools like the da Vinci surgical system, which allows remote surgeries. It also enhances decision-making by integrating real-time data, such as that from the OR Black Box, to monitor and improve outcomes. Challenges remain in contextualizing visual data and ensuring robustness in AI-based systems, but AI holds great promise for enhancing surgical performance and patient safety [ 11 ]. Cancer Pharmacotherapy AI's role in cancer research is expanding, particularly in analysing pharmacy data for epidemiological insights. Techniques like natural language processing (NLP) and machine learning algorithms are aiding in the identification of patterns related to cancer treatment outcomes. While challenges in data transparency persist, AI’s potential to inform evidence-based interventions in cancer care is clear [ 12 ]. Gastroenterology AI-based deep learning systems have improved colonoscopy outcomes by enhancing adenoma and polyp detection rates. Meta-analysis has shown that AI significantly outperforms traditional methods, contributing to early colorectal cancer detection [ 13 ].Transplantation: AI models, particularly artificial neural networks (ANNs), are being utilized to predict the success of liver transplants, with better accuracy than traditional methods like the Donor Risk Index. Similarly, machine learning methods have shown promise in predicting kidney transplant outcomes, although comparisons with traditional techniques remain inconclusive [ 17 ]. Chronic Obstructive Pulmonary Disease (COPD) Cluster analysis using machine learning has enabled better understanding of COPD phenotypes, enhancing diagnosis and treatment personalization. Although further research is required to define these phenotypes more clearly, AI offers promising tools for improving COPD management [ 16 ]. Table 2 Digital Health focus on various Developing Countries Country Initiative Focus Potential Contribution to UHC Angola National evaluation report on the health information system (2011) Improve data management and inform decision-making Likely to contribute to UHC Benin National Cyber Health Strategy (2017–2018) Strengthen health sector governance, human resource development, and health information systems Enhance service delivery and support UHC goals Botswana eHealth strategy (2020–2024) Leverage digital solutions to enhance health service delivery Aligns with efforts to improve access and quality of care, potentially contributing to UHC Burkina Faso Sectoral eHealth strategy (2016–2020) Build health system capacity in Information and Communication Technologies (ICTs) Support efforts towards UHC by improving healthcare access and delivery Burundi National plan for the development of Health Informatics (2015) Improve data management and health information systems Contribute to evidence-based decision-making and support UHC objectives Cameroon National digital health strategic plan (2020–2024) Improve health system performance through optimal use of digital technologies Likely to enhance healthcare accessibility and quality, supporting UHC efforts Comoros National Cyber Health Strategy (2017–2021) Strengthen HR capacities, improve health information systems, and enhance telemedicine Potentially support UHC goals by improving access and quality of care Côte d'Ivoire Strategic plan for cyber health (2012) Ensure digital management of the health system Likely to improve efficiency and effectiveness of healthcare delivery, supporting UHC objectives Democratic Republic of the Congo National Health Informatics Development Plan (2014) Rationalize investments in eHealth Improve health information management and support evidence-based decision-making for UHC Eswatini Kingdom of Eswatini eHealth Strategy (2016–2020) Lay foundations for eHealth and provide practical and sustainable solutions Likely to contribute to UHC by improving healthcare access and quality Ethiopia Information Revolution Strategic Plan (2018–2025) Transform data culture and improve health system performance Potentially support UHC efforts through enhanced decision-making and service delivery Gabon Strategic Master Plan for the Health Information System of Gabon (2017–2022) Improve data management and inform decision-making Supporting UHC objectives Ghana National eHealth Strategy (2010) Deploy ICT to improve health services Support UHC by enhancing accessibility and quality of care Madagascar Strategic Plan for Strengthening the Health Information System (2018–2022) Improve data availability and utilization Potentially support evidence-based decision-making for UHC Malawi National Digital Health Strategy (2020–2025) Establish reliable ICT infrastructure and improve service delivery Likely supporting UHC goals by enhancing accessibility and quality of care Mali National Cyber Health Policy (2013) Improve health system efficiency through ICTs Potentially support UHC by enhancing service delivery and accessibility Mauritania Initiatives to strengthen telemedicine Likely improving access to healthcare services Supporting UHC goals Mauritius Health 2015 Seamless Continuity of Care initiative Improve health information management Potentially support UHC by enhancing service delivery and accessibility Namibia National eHealth Strategy (2021–2025) Strengthen health service delivery through electronic solutions Likely contributing to UHC goals by improving accessibility and quality of care Nigeria National Health ICT Strategic Framework (2015–2020) Deploy ICT to improve service delivery and access Potentially supporting UHC objectives Rwanda National Digital Health Strategic Plan (2018–2023) Improve service delivery through DH Likely supporting UHC goals by enhancing accessibility and quality of care Senegal Digital Health Strategic Plan (2018–2023) Improve UHC through improved health information and decision-making Sierra Leone National Digital Health Strategy (2018–2023) Improve service delivery and accessibility through ICT Potentially supporting UHC goals South Africa National Digital Health Strategy (2019–2024) Improve patient management, efficiency, and quality of care Potentially supporting UHC goals through enhanced service delivery Togo Strategic Plan for Development of Cyber Health (2013–2015) Improve health system efficiency and accessibility through ICTs Potentially supporting UHC objectives United Republic of Tanzania National Digital Health Strategy Strengthen governance, improve client experience, empower healthcare providers, ensure resource availability, and standardize information exchange Supporting UHC objectives Zambia eHealth strategy (2017–2022) Mainstream ICTs in health for socio-economic development Potentially supporting UHC goals through enhanced service delivery Zimbabwe E-Health Strategy Improve health practitioner education, private-public sector collaboration, service reach, resource utilization, and patient identification Likely supporting UHC objectives Digital Health Tools AI and Digital Health Tools in India: Enhancing Healthcare and Advancing Universal Health Coverage (UHC). India is leveraging artificial intelligence (AI) as a transformative tool in its digital health (DH) initiatives, aiming to improve healthcare access, efficiency, and outcomes as part of its broader objective of achieving Universal Health Coverage (UHC). AI applications span multiple healthcare domains, from disease detection to patient management, process optimization, and healthcare delivery (Table 2 ). Applications of AI in Healthcare AI, particularly through machine learning (ML) and deep learning algorithms, is used in diagnostic decision support systems for diseases like cancer, tuberculosis (TB), and diabetic retinopathy. Advanced computer vision algorithms assess medical images (e.g., X-rays and CT healthcare access, particularly in rural areas [ 19 ]. Additionally, AI is applied in process optimization for hospital management, from improving bed utilization and inventory management to automating insurance claims processing. scans) to detect cancers at early stages. AI-powered tools like SigTuple’s Manthana automate the analysis of blood samples, improving diagnostic efficiency [ 18 ]. Telemedicine platforms like OnliDoc and Lybrate use AI to assess symptoms and recommend treatments. AI in Patient Engagement and Monitoring Chatbots and wearable sensors are central to patient-facing applications in India’s healthcare ecosystem. AI-powered chatbots help with appointment scheduling, symptom checking, and mental health support. Wearable devices, paired with AI, monitor essential body functions and provide real-time data for healthcare professionals, enhancing patient management [ 20 ]. Furthermore, AI-driven systems like surgical simulators are revolutionizing surgical training, offering virtual environments for learning complex procedures like spine and knee surgery. Innovative AI Applications in Disease Management (Fig. 3). AI’s role in disease management is exemplified in innovative projects such as the development of AI applications for detecting Malaria and Dengue in Karnataka. These systems leverage data from diverse health repositories, transforming heterogeneous data into computable formats suitable for AI analysis. Despite challenges in data integration, this initiative successfully demonstrates AI's potential to uncover valuable insights, enhancing disease detection and prognosis [ 21 ]. Diseasenomics and Access to Care One of the emerging AI-driven tools is Diseaseomics, a system that uses AI to improve the precision of medical diagnoses. By incorporating biological ontologies and electronic health records (EHR), Diseaseomics enhances decision-making and supports differential diagnoses. This tool can assist healthcare workers, especially in resource-limited settings, by providing accessible, up-to-date medical information. It’s a powerful asset in India’s pursuit of UHC, ensuring that medical knowledge is available to both healthcare professionals and underserved populations [ 22 ]. Mobile Health Innovations and Human-Centred Design The Vinyasa Tool represents a breakthrough in the design of mobile health (mHealth) solutions. Developed through extensive user research, this tool aims to enhance mHealth solutions by exploring the experiences and perceptions of healthcare workers. It has been instrumental in optimizing non-communicable disease screening and management in India, contributing to UHC by providing user-centred solutions that cater to the needs of both healthcare providers and patients [ 23 ]. Digital Health and UHC Initiatives in India India's major digital health initiatives, such as the National Health Stack, National Digital Health Blueprint, and the Ayushman Bharat Digital Mission (ABDM), aim to leverage AI and digital technology to improve healthcare delivery. ABDM, launched in 2021, prioritizes patient privacy and consent while optimizing healthcare workflows through digital solutions like Scan and Share. Key components like the Ayushman Bharat Health Account (ABHA) enable the efficient exchange of patient data across healthcare providers, facilitating more streamlined and accessible care. Programs like the Rashtriya Swasthya Bima Yojana (RSBY) also demonstrate how AI can contribute to UHC. RSBY uses smart-card technology to extend healthcare access to marginalized populations, underscoring AI’s role in digital welfare programs and healthcare delivery [ 24 ]. However, challenges remain, such as ensuring equitable access to digital health tools for all populations, especially those in vulnerable or remote areas. Limitation The review included only key databases, specifically PubMed and Scopus. Articles from other databases were excluded because of duplication or limited relevance to the review objectives. Ongoing challenges concerning data privacy, algorithmic bias, and governance necessitate the development of robust regulatory frameworks and targeted capacity-building initiatives to ensure responsible implementation of artificial intelligence in healthcare. Conclusion: AI-enabled digital health initiatives offer a promising approach to advancing Universal Health Coverage. Strategic governance, ethical protections, and inclusive design are crucial for optimising impact. The use of AI and digital health tools together is changing how healthcare is delivered by making it easier to diagnose diseases, manage patients, and make the system work better. Initiatives such as ABDM, RSBY, and Diseaseomics demonstrate the application of digital technologies in national programs to enhance healthcare access and facilitate system integration in the context of universal health coverage (UHC). The reviewed evidence indicates that AI-enabled solutions, when implemented alongside policies that promote equity and interoperability, can address service delivery gaps and contribute to health system strengthening. India's experience offers practical lessons for other nations aiming to leverage AI for healthcare transformation, despite ongoing challenges in scaling and sustaining these innovations. Declarations Conflicts of interest : As per the ICMJE universal disclosure form, all authors state the following: Payment/services information: All authors have stated that they did not get any financial support from any organization for the work they submitted. Financial disclosures : All authors have stated that they currently have no financial links or affiliations with any organizations that may have a vested interest in the submitted work, both presently and within the past three years. Funding Disclosure - No Funding received. Clinical trial number: not applicable Ethical Approval NA Consent to Participate NA Consent to Publish declarations: Yes Data availability statement (DAS)- Yes on request Author Contribution The authors confirm contribution to the paper as follows: study conception and design by Abhijeet Prasad Sinha Author, Rohit Ashwa Kumar co-author did data collection, interpretation of results by Abhijeet Prasad Sinha, Rohitashwa Kumar and Abhijeet Prasad Sinha drafted manuscript: Rohitashwa Kumar Author. Padmashree Ganapathyraman contributed to manuscript editing and revisions based on reviewer and editorial comments. All authors reviewed the results and approved the final version of the manuscript. References World Health Organization. Universal health coverage (UHC) [Internet], Geneva WHO. 2024 [cited 2024 May 11]. Available from: https://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc ). Bold B, Lkhagvajav Z, Dorjsuren B. Role of artificial intelligence in achieving universal health coverage: a Mongolian perspective. Korean J Radiol. 2023;24(9):821–824. Available from: https://doi.org/10.3348/kjr.2023.0203 World Health Organization. Digital health (eHealth) [Internet]. Geneva: WHO. 2024 [cited 2024 May 11]. Available from: https://www.who.int/health-topics/digital-health Oh H, Rizo C, Enkin M, Jadad A. What is eHealth (3): a systematic review of published definitions. J Med Internet Res. 2005;7(1): e1. Available from: https://www.jmir.org/2005/1/e1/ Kickbusch I, Piselli D, Agrawal A, Balicer R, Banner O, Adelhardt M et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet. 2021;398(10312):1727–1776. Available from: https://doi.org/10.1016/S0140-6736(21)01824-9 Topol E. The patient will see you now: the future of medicine is in your hands. New York: Basic Books; 2015. Available from: https://www.basicbooks.com/titles/eric-topol/the-patient-will-see-you-now/9780465061839/ Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22–28. Available from: https://doi.org/10.1177/0141076818815510 Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. Available from: https://doi.org/10.1056/NEJMra1814259 Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16–26. Available from: https://doi.org/10.1055/s-0039-1677908 Petersen BK, Yang J, Grathwohl WS, Cockrell C, Santiago C, And G et al. Deep reinforcement learning and simulation as a path toward precision medicine. J Comput Biol. 2019;26(6):597–604. Available from: https://doi.org/10.1089/cmb.2018.0168 Krist AH, Phillips R, Leykum L, Olmedo B. Digital health needs for implementing high-quality primary care: recommendations from the National Academies of Sciences, Engineering, and Medicine. J Am Med Inform Assoc. 2021;28(12):2738–2742. Available from: https://doi.org/10.1093/jamia/ocab195 Field MJ, editor. Telemedicine: a guide to assessing telecommunications in healthcare. Washington (DC): National Academies Press; 1996. Available from: https://nap.nationalacademies.org/catalog/5296 Dinh-Le C, Chuang R, Chokshi S, Mann D. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR Mhealth Uhealth. 2019;7(9): e12861. Available from: https://mhealth.jmir.org/2019/9/e12861/ Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: an overview of systematic reviews. Int J Med Inform. 2022;166:104855. Available from: https://doi.org/10.1016/j.ijmedinf.2022.104855 Fulmer RJ, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health. 2018;5(4):e64. Available from: https://mental.jmir.org/2018/4/e64/ Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy using a fully automated conversational agent (Woebot): randomized controlled trial. JMIR Ment Health. 2017;4(2): e19. Available from: https://mental.jmir.org/2017/2/e19/ Green EP, Lai Y, Pearson N, Rajasekharan S, Rauws M, Joerin A et al. Expanding access to perinatal depression treatment in Kenya through automated psychological support. JMIR Form Res. 2020;4(4):e17895. Available from: https://formative.jmir.org/2020/4/e17895/ De Simone B, Chouillard E, Gumbs AA, Loftus TJ, Kaafarani H, Catena F. Artificial intelligence in surgery: the emergency surgeon’s perspective (ARIES project). Discov Health Syst. 2022;1:9. Available from: https://doi.org/10.1007/s44250-022-00009-8 Grothen AE, Tennant B, Wang C, Torres A, Bloodgood Sheppard B, Abastillas G et al. Application of artificial intelligence methods to pharmacy data for cancer surveillance and epidemiology research. JCO Clin Cancer Inform. 2020;4:1051–1058. Available from: https://doi.org/10.1200/CCI.20.00054 \Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. Impact of deep convolutional neural network-based AI on colonoscopy outcomes: systematic review and meta-analysis. J Gastroenterol Hepatol. 2020;35(10):1676–1683. Available from: https://doi.org/10.1111/jgh.15009 Jusril H, Ariawan I, Damayanti R, Lazuardi L, Musa M, Wulandari SM et al. Digital health for real-time monitoring of a national immunisation campaign in Indonesia. BMJ Open. 2020;10:e038282. Available from: https://bmjopen.bmj.com/content/10/10/e038282 Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation. Int J Med Inform., Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis. Respir Med. 2020;171:106093. Available from: https://doi.org/10.1016/j.rmed.2020.106093. Raffort J, Adam C, Carrier M, Ballaith A, Coscas R, Jean-Baptiste E et al. Artificial intelligence in abdominal aortic aneurysm. J Vasc Surg. 2020;72(1):321–333.e1. Available from: https://doi.org/10.1016/j.jvs.2019.10.09 Silva K, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Machine learning models for type 2 diabetes prediction. Int J Med Inform. 2020;143:104268. Available from: https://doi.org/10.1016/j.ijmedinf.2020.104268 Das SK, Roy DK, Deb Roy S, Shil D. AI in Indian healthcare: from roadmap to reality. Intelligent Pharmacy [Internet]. 2024 Feb 19. Available from: https://www.pharmaceuticalintelligence.com/ Talukder AK, Schriml L, Ghosh A, Biswas R, Chakrabarti P, Haas RE. Diseasomics: machine-interpretable disease knowledge at point-of-care. PLOS Digit Health. 2022;1(9):e0000128. Available from: https://doi.org/10.1371/journal.pdig.0000128 Thomas V, Kalidindi B, Waghmare A, Bhatia A, Raj T, Balsari S. The Vinyasa tool for mHealth solutions. JMIR Form Res. 2023;7:e45250. Available from: https://formative.jmir.org/2023/1/e45250/ Sharma RS, Ar R, Jain S, Singh D. The Ayushman Bharat Digital Mission (ABDM). CSI Trans ICT. 2023;11(1):3–9. Available from: https://doi.org/10.1007/s40012-022-00401- Al Dahdah M, Mishra RK. Digital health for all: digitized healthcare in India. Soc Sci Med. 2023;319:114968. Available from: https://doi.org/10.1016/j.socscimed.2023.114968 Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstractAIDHUHCEnhancedAS.pptx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 12 May, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 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-9188979","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":612463292,"identity":"d38abf8e-1028-49e5-99ff-76a47688e0a5","order_by":0,"name":"Abhijeet Prasad Sinha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACCR6GAxJgFmMDMwODDYjReIAYLRJQLWlgvQS1gCkQAdRyGMzDq4V/du/BAxYVNnXm/IfbHhfUnLdb234YaEuNTTROS+6cSzggcSZNwnJGYrvxjGO3k7edSQRqOZaW24BLz40cgwOSbYclDG4wtknzsN1ONjsA1MLYcBinFnmwln//JQzOHwRq+Xcu2ez8Q/xaDMBaGg5IGBxIbJPmbTtgZ3aDgC2Gd84YHJA4liy5cwZIS19ygtkNoC0JePwid7vH+LNEjR2/Of/xZ9I83+zszc6nP3zwocYGt/eBgBkUKQZQTiJYZQIe5SDA+AFJiz0BxaNgFIyCUTACAQDqOGY+oBMLCQAAAABJRU5ErkJggg==","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Abhijeet","middleName":"Prasad","lastName":"Sinha","suffix":""},{"id":612463293,"identity":"9f1e679b-c370-445d-87db-201362e93378","order_by":1,"name":"Rohitashwa Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rohitashwa","middleName":"","lastName":"Kumar","suffix":""},{"id":612463294,"identity":"e0f00466-0c31-46e3-8f4f-a7cf35867bc9","order_by":2,"name":"Padmashree Ganapathyraman","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Padmashree","middleName":"","lastName":"Ganapathyraman","suffix":""}],"badges":[],"createdAt":"2026-03-22 04:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9188979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9188979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106637496,"identity":"f29a7811-e0b2-4038-bc18-60b645e4a6f0","added_by":"auto","created_at":"2026-04-10 17:01:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePRISMA 2020 flow diagram depicting the processes of study identification, screening, eligibility assessment, and inclusion.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9188979/v1/d5b0499e093ccd09e84bee57.png"},{"id":106726461,"identity":"fd98dfa0-bba8-4753-a5e8-20608f4ca81b","added_by":"auto","created_at":"2026-04-12 18:36:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImprovement in Digital health tools utilisation during 2019-2022(Association, 2022)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9188979/v1/7dbc19f2dad03b29c0343d52.png"},{"id":106637497,"identity":"aa799873-e1c8-4418-bbfa-d275b386bd57","added_by":"auto","created_at":"2026-04-10 17:01:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUtilisation of AI as a tool of Digital Health for Universal Health Coverage\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9188979/v1/defc8ef5015db153c0301435.png"},{"id":106728690,"identity":"dcb792d2-d8b8-4c18-bb37-6b119b7793fb","added_by":"auto","created_at":"2026-04-12 18:43:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1471094,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9188979/v1/0df45f50-bd44-4470-b084-ab715697e192.pdf"},{"id":106637498,"identity":"a4ed596f-fa02-4576-ad54-27b416aba134","added_by":"auto","created_at":"2026-04-10 17:01:53","extension":"pptx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":41059,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractAIDHUHCEnhancedAS.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9188979/v1/7f42feaab0d3071ce5dda84a.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Health and the use of AI in Healthcare for Universal Health coverage- A PRISMA guided Systematic review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUniversal Health Coverage (UHC) aims to provide comprehensive, high-quality healthcare services to all individuals without financial hardship, measured by service coverage and financial protection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Countries worldwide encounter worker shortages, unequal access, and escalating expenses, especially in low-resource environments. Digital health technologies, such as telemedicine, electronic health records, mobile health, and artificial intelligence, have become essential facilitators of health system enhancement. Data from several international settings indicates enhanced accessibility, efficiency, and patient involvement via digital technologies Achieving UHC requires a skilled, well-distributed healthcare workforce and access to essential services across health promotion, treatment, and palliative care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. UHC, integral to the Sustainable Development Goals (SDGs), seeks to eliminate the economic burden of healthcare costs, reducing poverty linked to medical expenses. Digital health (DH), utilizing information and communication technology (ICT), is transforming healthcare by enhancing accessibility, efficiency, and patient engagement through tools like telemedicine and health monitoring apps [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These innovations democratize healthcare, improve health outcomes, and foster a patient-cantered approach, impacting social and environmental factors that influence health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Artificial intelligence (AI) contributes to universal health coverage (UHC) by enhancing healthcare delivery through advanced data analysis and decision-support systems within the digital health ecosystem [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. AI technologies, including machine learning, have the potential to refine clinical practice and inform health policy. However, their integration into UHC frameworks necessitates thorough evaluation to ensure that AI applications align with overarching healthcare objectives and contribute to equitable and effective health systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Notwithstanding increasing popularity, substantial disparities persist in the equitable implementation, governance, and scalability of AI-driven healthcare solutions. This paper synthesizes global and Indian evidence to evaluate the potential of digital health initiatives, including artificial intelligence\u0026ndash;enabled applications, to advance universal health coverage and address persistent systemic challenges [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This study aims to evaluate the impact of digital health (DH) initiatives, including artificial intelligence (AI)-driven interventions, on improving healthcare accessibility and quality in diverse healthcare settings worldwide. It also seeks to explore the implications of DH advancements for achieving equitable healthcare access and addressing health disparities, particularly in India and other developing countries.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObjectives\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo do systematic review of DH initiatives, including electronic health records (EHR), telemedicine platforms, and AI-driven interventions, in improving healthcare accessibility, quality, and equity across diverse populations and regions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the facilitators and barriers to the successful implementation and adoption of DH technologies in India, comparing them with other developing countries.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eThe databases examined (PubMed, Scopus, and other grey literature records).The comprehensive search phrases and Boolean operators employed, the justification for choosing the 2015\u0026ndash;2025 period, which signifies the swift advancement and policy significance of AI and digital health interventions in accordance with global and Indian UHC goals. Rationale for database selection predicated on their extensive coverage of biomedical, health systems, and digital health literature. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. The authors (Abhijeet Sinha and Rohitashwa Kumar) independently assessed all eligible studies, with consensus reached in case of disagreement. A comprehensive search of the PubMed database (up to May 2025) was conducted using the terms \u0026ldquo;Digital Health in Universal Health Coverage,\u0026rdquo; \u0026ldquo;AI in Digital Health,\u0026rdquo; and \u0026ldquo;AI for Universal Health Coverage.\u0026rdquo; A comprehensive literature search was performed in PubMed and Scopus databases (up to May 2025) using combinations of keywords pertaining to digital health, artificial intelligence, and universal health coverage. Search queries used Boolean combinations of core terms including \u0026ldquo;digital health,\u0026rdquo; \u0026ldquo;artificial intelligence,\u0026rdquo; and \u0026ldquo;universal health coverage.\u0026rdquo; Studies published between 2015 and 2025 were included to reflect contemporary digital health and AI-driven health system developments. A total of 3,469 records were identified across the databases, including 164 records on digital health and Universal Health Coverage, 3,163 on artificial intelligence in digital health, and 142 on artificial intelligence for Universal Health Coverage. Following the application of predefined inclusion and exclusion criteria, 3,429 records were excluded, resulting in 52 studies included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSelection of Studies and Evaluation of Quality\u003c/strong\u003e \u003cp\u003eTwo reviewers conducted independent screenings of titles, abstracts, and entire texts. The quality of the study was evaluated using established systematic review criteria relevant to the study design. Disputes were settled by consensus.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData Extraction\u003c/b\u003e The extracted data encompassed study design, geographical context, type of intervention, health domain, and significance to dimensions of Universal Health coverage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Analysis and Visualisation\u003c/b\u003e A theme synthesis methodology was employed, concentrating on access, quality, efficiency, equity, and governance ramifications.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion Criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDH Initiatives\u003c/b\u003e: Articles discussing DH initiatives or programs implemented at national or regional levels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGlobal Coverage\u003c/b\u003e: Initiatives from various countries worldwide, including high-income and low- and middle-income countries (LMICs).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRelevance to Universalisation of Healthcare (UHC)\u003c/b\u003e: Initiatives directly contributing to advancing UHC goals by improving healthcare accessibility, efficiency, quality, or equity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTechnology Integration\u003c/b\u003e: Initiatives leveraging ICT, AI, or other digital tools to enhance healthcare delivery, data management, or health system performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiverse Settings\u003c/b\u003e: Initiatives implemented in diverse healthcare settings, including primary care facilities, hospitals, rural or remote areas, and underserved communities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion Criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndividual or Local Initiatives\u003c/b\u003e: Initiatives limited to individual healthcare facilities or local community projects with no broader implications for national or regional healthcare systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNarrow Scope\u003c/b\u003e: Initiatives focused solely on specific diseases or health conditions without addressing broader healthcare delivery or system-level challenges.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNon-Digital Interventions\u003c/b\u003e: Initiatives that do not utilize digital technologies or ICT tools for healthcare delivery or system improvement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNon-Relevant Content\u003c/b\u003e: Articles unrelated to the intersection of DH, AI, and UHC, such as general discussions on healthcare policy or unrelated topics.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Selection and Quality Appraisal-\u003c/h3\u003e\n\u003cp\u003eThe included studies were evaluated using relevant checklists corresponding to the study design (e.g., PRISMA guidelines and established critical appraisal criteria for systematic reviews). We assessed the methodological quality of the included studies using established critical appraisal criteria that matched each study design. The thematic categories in the manuscript are results of the synthesis, not methodological subheadings\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe review encompassed 52 studies. AI-powered digital health tools are used around the world, including in India, to support diagnostics, patient care, and more efficient health systems.\u003cbr\u003e\u0026nbsp;Initiatives such as the Ayushman Bharat Digital Mission and AI-based diagnostic tools demonstrate how India can build digital health systems that support Universal Health Coverage. Still, there are ongoing challenges with infrastructure, data protection, and digital literacy. Globally and in India, artificial intelligence (AI) and digital health (DH) tools are increasingly adopted to enhance healthcare delivery and move towards Universal Health Coverage (UHC).AI is now being used more often in disease diagnosis, patient care, and the improvement of how healthcare systems work. These applications are still in the early stages, but they show promise for addressing challenges in healthcare delivery and could help improve access to care for underserved groups.\u003c/p\u003e\n\u003cp\u003eAI technologies such as machine learning, computer vision, and deep learning are increasingly used to detect diseases. They play a role in finding early-stage cancer and diagnosing conditions such as diabetic retinopathy and tuberculosis. AI tools such as Manthana, which automates blood sample analysis, and telemedicine platforms including OnliDoc and Lybrate, which facilitate symptom assessment and provide treatment recommendations, exemplify the application of AI in India\u0026apos;s healthcare sector. These tools are reported to support healthcare professionals and may improve diagnostic processes, particularly in resource-limited settings.\u003c/p\u003e\n\u003cp\u003eIn addition to disease detection, AI tools are being employed for process optimization in hospitals, including managing hospital bed utilization, inventory, and insurance claims. Predictive analytics and chatbots help forecast patient turnover and facilitate appointment management, improving healthcare efficiency. Wearable sensors and AI technologies are also used to monitor essential body functions, offering real-time insights to healthcare providers, which can be crucial in managing chronic diseases and surgical training.\u003c/p\u003e\n\u003cp\u003eA key initiative in India, the Ayushman Bharat Digital Mission (ABDM), aims to enhance healthcare accessibility and safety using AI. By integrating health records across systems, ABDM allows for better decision-making and more efficient resource allocation, with patient privacy and consent as top priorities\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eIndia\u0026rsquo;s Rashtriya Swasthya Bima Yojana (RSBY) is a government program that uses digital tools to help improve healthcare access for marginalised groups. This example shows how digital technologies can support better healthcare for those in need. These initiatives show how AI-powered digital tools are becoming an increasingly important part of India\u0026rsquo;s health system as the country works toward universal health coverage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile India\u0026rsquo;s adoption of AI in healthcare is promising, challenges remain, particularly regarding equitable access. Vulnerable populations, especially in rural areas, may not fully benefit from these digital health solutions due to factors such as internet connectivity, digital literacy, and infrastructure constraints. To address these gaps, India must focus on improving digital literacy, enhancing infrastructure, and ensuring that AI solutions are accessible and user-friendly for all demographics. In addition, there is a need for robust data privacy policies to build trust in AI-powered systems.\u003c/p\u003e\n\u003cp\u003eAn example of AI use in the literature is Diseaseomics. This system combines biological ontologies with electronic health records (EHRs) to support diagnosis and clinical decision-making. Diseaseomics uses AI to map correlations between diseases and symptoms, enabling healthcare providers to make informed decisions in real-time. This tool has the capacity to significantly impact rural and underserved areas by providing medical knowledge that is typically accessible only in large healthcare centers. As AI technology improves, researchers have found that tools like Diseaseomics can help healthcare workers get the most up-to-date diagnostic information.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis assessment shows how AI-powered digital health tools are helping advance Universal Health Coverage (UHC). Global data indicates enhanced service accessibility and efficiency, whereas India presents a persuasive example for extensive adoption.\u003c/p\u003e\n\u003cp\u003eNonetheless, ethical governance, interoperability, workforce capacity, and equal access persist as significant issues. Resolving these difficulties is crucial for the sustained integration of AI into healthcare systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Health Tools in UHC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integration of digital health (DH) tools into the universal health coverage (UHC) framework has expanded, though adoption remains uneven across specialties, populations and settings. Recent literature indicates increasing interest in healthcare models that emphasize collaboration, patient engagement, and enhanced access to personal health information. Patient portals enable secure communication between patients and clinicians and extend care beyond office visits by providing access to medical data. Despite the widespread availability of health information resources via the Internet and mobile applications, clinician uptake of technologies that enable accessible and convenient care has been slow. Regulatory barriers, resistance to patient-centred care models, and professional burnout are often mentioned as reasons why these technologies are not widely adopted. (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The literature identifies telemedicine as a promising strategy for enhancing healthcare accessibility, particularly by minimizing the necessity for patients to travel to medical facilities. However, its use remains limited and skewed toward urban areas with better access to the necessary technology (Figure) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Personal monitoring devices, including wearable technology and internet-connected health tools, facilitate tracking health goals and monitoring vital signs between clinical appointments. Although integration with electronic health records (EHR) is still in its early stages, these tools hold potential for enhancing patient engagement and improving the efficiency of clinical workflows, provided the data remains simple and meaningful for both patients and healthcare providers sharing mechanisms. Thus, bridging the gap between DH tools and UHC requires addressing barriers to adoption and ensuring seamless integration within existing healthcare frameworks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence in DH\u003c/strong\u003e: AI is increasingly integrated within digital health (DH) applications to support healthcare delivery in the context of UHC. Through machine learning algorithms, AI optimizes diagnostics, treatment planning, and resource\u003c/p\u003e\n\u003cp\u003eallocation, thereby enhancing the efficiency and quality of care. AI-driven platforms streamline patient management, improve clinical decision-making, facilitate remote consultations, and bridge healthcare gaps in underserved regions. AI applications automate repetitive tasks and analyze large datasets, thereby facilitating personalized care and contributing to the reduction of healthcare disparities. Overall, AI-enabled digital health tools are discussed as potential contributors to more accessible and efficient healthcare systems aligned with UHC goals. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChatbots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-powered psychological tool, for reducing depression and anxiety symptoms among college students. The results indicated significant improvements in emotional well-being among users with unrestricted access to Tess compared to those receiving only informational resources. Tess\u0026apos;s regular check-ins, either daily or biweekly, present a cost-effective and accessible solution for mental Healthcare, contributing to universal health coverage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings highlight the potential of AI in mental health treatment, with further research needed to enhance the effectiveness of AI-driven solutions for psychological support explored the use of Woebot, an automated conversational agent, to provide mental health support for university students with anxiety and depression. The study showed significant reductions in depressive symptoms among users of Woebot compared to a control group. The high level of user engagement and the emphasis on empathy and therapeutic outcomes underscore the importance of integrating mental health professionals in the development of digital tools. Woebot is discussed as a scalable digital intervention with potential relevance for expanding access to mental health support [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Zuri, a chatbot designed to provide psychological support to pregnant women and new mothers in Kenya. Although the impact on mood was modest, Zuri demonstrated the potential for AI-driven interventions to expand mental health services, particularly in resource-limited settings. The perceived anonymity of Zuri encouraged disclosure of mental health issues, highlighting a critical gap in mental health services and suggesting the need for continued research in this area. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI technologies, including machine learning (ML), computer vision (CV), augmented reality (AR), and anatomical segmentation, are increasingly applied in surgical practice. AI tools improve surgical performance by aiding in procedure comprehension, detection, and navigation. Systems like da Vinci enable remote surgeries, while AI segmentation enhances precision. AI-powered platforms facilitate access to expert advice in real-time. Despite challenges such as contextualizing visual data and ensuring robust localization, AI is enhancing surgical safety, management, and decision-making. The OR Black Box, for example, monitors procedures in real-time, improving outcomes and reducing costs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Pharmacotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI\u0026apos;s application in cancer surveillance and epidemiology research has been examined in multiple studies. Using machine learning and natural language processing (NLP) techniques like BERT, AI analyses large healthcare databases, identifying patterns related to cancer incidence, treatment, and outcomes. This approach enhances predictive models, contributing to improved accuracy and efficiency in cancer surveillance. While challenges exist, such as data transparency and performance variability, AI shows promise for advancing cancer research and guiding evidence-based interventions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGastroenterology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA systematic review of AI in colonoscopy found that deep convolutional neural network-based AI was associated with improved adenoma and polyp detection rates compared to standard colonoscopy. The study demonstrated AI\u0026apos;s potential to enhance early detection, enabling better colorectal cancer prevention [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiver and Kidney Transplantation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI, particularly artificial neural networks (ANNs), has been utilized to predict the success of liver transplants from deceased donors. AI models demonstrated higher predictive accuracy as compared to the traditional methods such as the Donor Risk Index (DRI) and the Model for End-Stage Liver Disease (MELD), offering better predictive accuracy and enhancing organ distribution and post-transplant outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In kidney transplants, machine learning techniques have shown potential for predicting graft failure, though results comparing these methods with traditional regression models remain inconclusive [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronic Obstructive Pulmonary Disease (COPD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine learning, particularly cluster analysis, is being used to identify distinct COPD phenotypes, enhancing diagnosis and personalized treatment plans. A systematic review highlighted the progress made in understanding COPD through AI but also noted gaps in the literature, calling for further research to refine phenotyping and clinical outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e; AI utility in various Healthcare domain\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHealthcare Domain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRole of AI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eExamples/Tools\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAugmenting mental health support through integrative psychological AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTess, Woebot, Zuri\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSupporting surgical practice through machine learning, computer vision, and augmented reality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eda Vinci surgical system, OR Black Box, POTTER app\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCancer Pharmacotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eUtilizing pharmacy data for the purpose of cancer surveillance and epidemiological research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNatural language processing (NLP) techniques, namely Bidirectional Encoder Representations from Transformers (BERT)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGastroenterology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEnhancing the results of colonoscopy procedures using artificial intelligence systems based on deep convolutional neural networks (DCNN).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAI colonoscopy systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLiver and Kidney Transplantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePrediction of graft outcomes in liver and kidney transplantation using artificial intelligence techniques.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eArtificial neural networks (ANNs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eIdentifying distinct COPD phenotypes and improving diagnosis and treatment strategies through cluster analysis and machine learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCluster analysis, machine learning algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetes Mellitus Type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePredicting Diabetes Type 2 onset in community settings and improving predictive accuracy through machine learning algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eVarious machine learning algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAbdominal Aortic Aneurysm (AAA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEnhancing imaging analysis, quantitative assessment of AAA morphology, and prediction of AAA growth and rupture using AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImage segmentation, predictive and prognostic programs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eAI Applications in Healthcare: Current Trends and Future Directions:\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eChatbots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWoebot, a fully automated conversational agent, led to notable reductions in depressive symptoms, emphasizing the importance of empathy and user engagement in digital mental health tools [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A chatbot delivering psychological support to pregnant women in Kenya via SMS and Facebook Messenger. Although the impact on mood was modest, the tool\u0026rsquo;s perceived anonymity facilitated disclosure of mental health concerns, underscoring [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI\u0026rsquo;s potential in resource-constrained settings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI, particularly machine learning (ML), computer vision (CV), and augmented reality (AR), is transforming surgical practices. AI improves precision in procedures through tools like the da Vinci surgical system, which allows remote surgeries. It also enhances decision-making by integrating real-time data, such as that from the OR Black Box, to monitor and improve outcomes. Challenges remain in contextualizing visual data and ensuring robustness in AI-based systems, but AI holds great promise for enhancing surgical performance and patient safety [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Pharmacotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI\u0026apos;s role in cancer research is expanding, particularly in analysing pharmacy data for epidemiological insights. Techniques like natural language processing (NLP) and machine learning algorithms are aiding in the identification of patterns related to cancer treatment outcomes. While challenges in data transparency persist, AI\u0026rsquo;s potential to inform evidence-based interventions in cancer care is clear [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGastroenterology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-based deep learning systems have improved colonoscopy outcomes by enhancing adenoma and polyp detection rates. Meta-analysis has shown that AI significantly outperforms traditional methods, contributing to early colorectal cancer detection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Transplantation: AI models, particularly artificial neural networks (ANNs), are being utilized to predict the success of liver transplants, with better accuracy than traditional methods like the Donor Risk Index. Similarly, machine learning methods have shown promise in predicting kidney transplant outcomes, although comparisons with traditional techniques remain inconclusive [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronic Obstructive Pulmonary Disease (COPD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster analysis using machine learning has enabled better understanding of COPD phenotypes, enhancing diagnosis and treatment personalization. Although further research is required to define these phenotypes more clearly, AI offers promising tools for improving COPD management [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDigital Health focus on various Developing Countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInitiative\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFocus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotential Contribution to UHC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAngola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational evaluation report on the health information system (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove data management and inform decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely to contribute to UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Cyber Health Strategy (2017\u0026ndash;2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eStrengthen health sector governance, human resource development, and health information systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eEnhance service delivery and support UHC goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBotswana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eeHealth strategy (2020\u0026ndash;2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLeverage digital solutions to enhance health service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAligns with efforts to improve access and quality of care, potentially contributing to UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBurkina Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSectoral eHealth strategy (2016\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBuild health system capacity in Information and Communication Technologies (ICTs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSupport efforts towards UHC by improving healthcare access and delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBurundi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational plan for the development of Health Informatics (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove data management and health information systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eContribute to evidence-based decision-making and support UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCameroon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational digital health strategic plan (2020\u0026ndash;2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove health system performance through optimal use of digital technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely to enhance healthcare accessibility and quality, supporting UHC efforts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComoros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Cyber Health Strategy (2017\u0026ndash;2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eStrengthen HR capacities, improve health information systems, and enhance telemedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially support UHC goals by improving access and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eC\u0026ocirc;te d\u0026apos;Ivoire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStrategic plan for cyber health (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eEnsure digital management of the health system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely to improve efficiency and effectiveness of healthcare delivery, supporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDemocratic Republic of the Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Health Informatics Development Plan (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRationalize investments in eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eImprove health information management and support evidence-based decision-making for UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEswatini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eKingdom of Eswatini eHealth Strategy (2016\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLay foundations for eHealth and provide practical and sustainable solutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely to contribute to UHC by improving healthcare access and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInformation Revolution Strategic Plan (2018\u0026ndash;2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTransform data culture and improve health system performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially support UHC efforts through enhanced decision-making and service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGabon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStrategic Master Plan for the Health Information System of Gabon (2017\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove data management and inform decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSupporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational eHealth Strategy (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDeploy ICT to improve health services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSupport UHC by enhancing accessibility and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMadagascar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStrategic Plan for Strengthening the Health Information System (2018\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove data availability and utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially support evidence-based decision-making for UHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMalawi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Digital Health Strategy (2020\u0026ndash;2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eEstablish reliable ICT infrastructure and improve service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely supporting UHC goals by enhancing accessibility and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Cyber Health Policy (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove health system efficiency through ICTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially support UHC by enhancing service delivery and accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMauritania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInitiatives to strengthen telemedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLikely improving access to healthcare services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSupporting UHC goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHealth 2015 Seamless Continuity of Care initiative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove health information management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially support UHC by enhancing service delivery and accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNamibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational eHealth Strategy (2021\u0026ndash;2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eStrengthen health service delivery through electronic solutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely contributing to UHC goals by improving accessibility and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Health ICT Strategic Framework (2015\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDeploy ICT to improve service delivery and access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially supporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRwanda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Digital Health Strategic Plan (2018\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove service delivery through DH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely supporting UHC goals by enhancing accessibility and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDigital Health Strategic Plan (2018\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove UHC through improved health information and decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Digital Health Strategy (2018\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove service delivery and accessibility through ICT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially supporting UHC goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Digital Health Strategy (2019\u0026ndash;2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove patient management, efficiency, and quality of care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially supporting UHC goals through enhanced service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStrategic Plan for Development of Cyber Health (2013\u0026ndash;2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove health system efficiency and accessibility through ICTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially supporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnited Republic of Tanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNational Digital Health Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eStrengthen governance, improve client experience, empower healthcare providers, ensure resource availability, and standardize information exchange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSupporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eeHealth strategy (2017\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMainstream ICTs in health for socio-economic development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePotentially supporting UHC goals through enhanced service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eE-Health Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eImprove health practitioner education, private-public sector collaboration, service reach, resource utilization, and patient identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLikely supporting UHC objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDigital Health Tools\u003c/h2\u003e\n \u003cp\u003eAI and Digital Health Tools in India: Enhancing Healthcare and Advancing Universal Health Coverage (UHC). India is leveraging artificial intelligence (AI) as a transformative tool in its digital health (DH) initiatives, aiming to improve healthcare access, efficiency, and outcomes as part of its broader objective of achieving Universal Health Coverage (UHC). AI applications span multiple healthcare domains, from disease detection to patient management, process optimization, and healthcare delivery (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eApplications of AI in Healthcare\u003c/h3\u003e\n\u003cp\u003eAI, particularly through machine learning (ML) and deep learning algorithms, is used in diagnostic decision support systems for diseases like cancer, tuberculosis (TB), and diabetic retinopathy. Advanced computer vision algorithms assess medical images (e.g., X-rays and CT healthcare access, particularly in rural areas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, AI is applied in process optimization for hospital management, from improving bed utilization and inventory management to automating insurance claims processing.\u003c/p\u003e\n\u003cp\u003escans) to detect cancers at early stages. AI-powered tools like SigTuple\u0026rsquo;s Manthana automate the analysis of blood samples, improving diagnostic efficiency [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Telemedicine platforms like OnliDoc and Lybrate use AI to assess symptoms and recommend treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI in Patient Engagement and Monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChatbots and wearable sensors are central to patient-facing applications in India\u0026rsquo;s healthcare ecosystem. AI-powered chatbots help with appointment scheduling, symptom checking, and mental health support. Wearable devices, paired with AI, monitor essential body functions and provide real-time data for healthcare professionals, enhancing patient management [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, AI-driven systems like surgical simulators are revolutionizing surgical training, offering virtual environments for learning complex procedures like spine and knee surgery. Innovative AI Applications in Disease Management (Fig. 3).\u003c/p\u003e\n\u003cp\u003eAI\u0026rsquo;s role in disease management is exemplified in innovative projects such as the development of AI applications for detecting Malaria and Dengue in Karnataka. These systems leverage data from diverse health repositories, transforming heterogeneous data into computable formats suitable for AI analysis. Despite challenges in data integration, this initiative successfully demonstrates AI\u0026apos;s potential to uncover valuable insights, enhancing disease detection and prognosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDiseasenomics and Access to Care\u003c/h3\u003e\n\u003cp\u003eOne of the emerging AI-driven tools is Diseaseomics, a system that uses AI to improve the precision of medical diagnoses. By incorporating biological ontologies and electronic health records (EHR), Diseaseomics enhances decision-making and supports differential diagnoses. This tool can assist healthcare workers, especially in resource-limited settings, by providing accessible, up-to-date medical information. It\u0026rsquo;s a powerful asset in India\u0026rsquo;s pursuit of UHC, ensuring that medical knowledge is available to both healthcare professionals and underserved populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMobile Health Innovations and Human-Centred Design\u003c/h2\u003e\n \u003cp\u003eThe Vinyasa Tool represents a breakthrough in the design of mobile health (mHealth) solutions. Developed through extensive user research, this tool aims to enhance mHealth solutions by exploring the experiences and perceptions of healthcare workers. It has been instrumental in optimizing non-communicable disease screening and management in India, contributing to UHC by providing user-centred solutions that cater to the needs of both healthcare providers and patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDigital Health and UHC Initiatives in India\u003c/h2\u003e\n \u003cp\u003eIndia\u0026apos;s major digital health initiatives, such as the National Health Stack, National Digital Health Blueprint, and the Ayushman Bharat Digital Mission (ABDM), aim to leverage AI and digital technology to improve healthcare delivery. ABDM, launched in 2021, prioritizes patient privacy and consent while optimizing healthcare workflows through digital solutions like Scan and Share. Key components like the Ayushman Bharat Health Account (ABHA) enable the efficient exchange of patient data across healthcare providers, facilitating more streamlined and accessible care. Programs like the Rashtriya Swasthya Bima Yojana (RSBY) also demonstrate how AI can contribute to UHC. RSBY uses smart-card technology to extend healthcare access to marginalized populations, underscoring AI\u0026rsquo;s role in digital welfare programs and healthcare delivery [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, challenges remain, such as ensuring equitable access to digital health tools for all populations, especially those in vulnerable or remote areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eLimitation\u003c/h2\u003e\n \u003cp\u003eThe review included only key databases, specifically PubMed and Scopus. Articles from other databases were excluded because of duplication or limited relevance to the review objectives. Ongoing challenges concerning data privacy, algorithmic bias, and governance necessitate the development of robust regulatory frameworks and targeted capacity-building initiatives to ensure responsible implementation of artificial intelligence in healthcare.\u003c/p\u003e\n \u003cp\u003eConclusion: AI-enabled digital health initiatives offer a promising approach to advancing Universal Health Coverage. Strategic governance, ethical protections, and inclusive design are crucial for optimising impact. The use of AI and digital health tools together is changing how healthcare is delivered by making it easier to diagnose diseases, manage patients, and make the system work better. Initiatives such as ABDM, RSBY, and Diseaseomics demonstrate the application of digital technologies in national programs to enhance healthcare access and facilitate system integration in the context of universal health coverage (UHC). The reviewed evidence indicates that AI-enabled solutions, when implemented alongside policies that promote equity and interoperability, can address service delivery gaps and contribute to health system strengthening. India\u0026apos;s experience offers practical lessons for other nations aiming to leverage AI for healthcare transformation, despite ongoing challenges in scaling and sustaining these innovations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e: As per the ICMJE universal disclosure form, all authors state the following:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePayment/services information:\u003c/strong\u003e All authors have stated that they did not get any financial support from any organization for the work they submitted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial disclosures\u003c/strong\u003e: All authors have stated that they currently have no financial links or affiliations with any organizations that may have a vested interest in the submitted work, both presently and within the past three years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Disclosure\u003c/strong\u003e- No Funding received.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval NA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate NA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declarations:\u003c/strong\u003e Yes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement (DAS)-\u003c/strong\u003e Yes on request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows: study conception and design by Abhijeet Prasad Sinha Author, Rohit Ashwa Kumar co-author did data collection, interpretation of results by Abhijeet Prasad Sinha, Rohitashwa Kumar and Abhijeet Prasad Sinha drafted manuscript: Rohitashwa Kumar Author. Padmashree Ganapathyraman contributed to manuscript editing and revisions based on reviewer and editorial comments. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Universal health coverage (UHC) [Internet], Geneva WHO. 2024 [cited 2024 May 11]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBold B, Lkhagvajav Z, Dorjsuren B. Role of artificial intelligence in achieving universal health coverage: a Mongolian perspective. Korean J Radiol. 2023;24(9):821\u0026ndash;824. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3348/kjr.2023.0203\u003c/span\u003e\u003cspan address=\"10.3348/kjr.2023.0203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Digital health (eHealth) [Internet]. Geneva: WHO. 2024 [cited 2024 May 11]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/digital-health\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/digital-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh H, Rizo C, Enkin M, Jadad A. What is eHealth (3): a systematic review of published definitions. J Med Internet Res. 2005;7(1): e1. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jmir.org/2005/1/e1/\u003c/span\u003e\u003cspan address=\"https://www.jmir.org/2005/1/e1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKickbusch I, Piselli D, Agrawal A, Balicer R, Banner O, Adelhardt M et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet. 2021;398(10312):1727\u0026ndash;1776. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(21)01824-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)01824-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopol E. The patient will see you now: the future of medicine is in your hands. New York: Basic Books; 2015. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.basicbooks.com/titles/eric-topol/the-patient-will-see-you-now/9780465061839/\u003c/span\u003e\u003cspan address=\"https://www.basicbooks.com/titles/eric-topol/the-patient-will-see-you-now/9780465061839/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22\u0026ndash;28. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0141076818815510\u003c/span\u003e\u003cspan address=\"10.1177/0141076818815510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347\u0026ndash;1358. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMra1814259\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra1814259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16\u0026ndash;26. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-0039-1677908\u003c/span\u003e\u003cspan address=\"10.1055/s-0039-1677908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen BK, Yang J, Grathwohl WS, Cockrell C, Santiago C, And G et al. Deep reinforcement learning and simulation as a path toward precision medicine. J Comput Biol. 2019;26(6):597\u0026ndash;604. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/cmb.2018.0168\u003c/span\u003e\u003cspan address=\"10.1089/cmb.2018.0168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrist AH, Phillips R, Leykum L, Olmedo B. Digital health needs for implementing high-quality primary care: recommendations from the National Academies of Sciences, Engineering, and Medicine. J Am Med Inform Assoc. 2021;28(12):2738\u0026ndash;2742. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jamia/ocab195\u003c/span\u003e\u003cspan address=\"10.1093/jamia/ocab195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eField MJ, editor. Telemedicine: a guide to assessing telecommunications in healthcare. Washington (DC): National Academies Press; 1996. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nap.nationalacademies.org/catalog/5296\u003c/span\u003e\u003cspan address=\"https://nap.nationalacademies.org/catalog/5296\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinh-Le C, Chuang R, Chokshi S, Mann D. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR Mhealth Uhealth. 2019;7(9): e12861. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mhealth.jmir.org/2019/9/e12861/\u003c/span\u003e\u003cspan address=\"https://mhealth.jmir.org/2019/9/e12861/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: an overview of systematic reviews. Int J Med Inform. 2022;166:104855. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijmedinf.2022.104855\u003c/span\u003e\u003cspan address=\"10.1016/j.ijmedinf.2022.104855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulmer RJ, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health. 2018;5(4):e64. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mental.jmir.org/2018/4/e64/\u003c/span\u003e\u003cspan address=\"https://mental.jmir.org/2018/4/e64/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy using a fully automated conversational agent (Woebot): randomized controlled trial. JMIR Ment Health. 2017;4(2): e19. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mental.jmir.org/2017/2/e19/\u003c/span\u003e\u003cspan address=\"https://mental.jmir.org/2017/2/e19/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen EP, Lai Y, Pearson N, Rajasekharan S, Rauws M, Joerin A et al. Expanding access to perinatal depression treatment in Kenya through automated psychological support. JMIR Form Res. 2020;4(4):e17895. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://formative.jmir.org/2020/4/e17895/\u003c/span\u003e\u003cspan address=\"https://formative.jmir.org/2020/4/e17895/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Simone B, Chouillard E, Gumbs AA, Loftus TJ, Kaafarani H, Catena F. Artificial intelligence in surgery: the emergency surgeon\u0026rsquo;s perspective (ARIES project). Discov Health Syst. 2022;1:9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44250-022-00009-8\u003c/span\u003e\u003cspan address=\"10.1007/s44250-022-00009-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrothen AE, Tennant B, Wang C, Torres A, Bloodgood Sheppard B, Abastillas G et al. Application of artificial intelligence methods to pharmacy data for cancer surveillance and epidemiology research. JCO Clin Cancer Inform. 2020;4:1051\u0026ndash;1058. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/CCI.20.00054\u003c/span\u003e\u003cspan address=\"10.1200/CCI.20.00054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\\Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. Impact of deep convolutional neural network-based AI on colonoscopy outcomes: systematic review and meta-analysis. J Gastroenterol Hepatol. 2020;35(10):1676\u0026ndash;1683. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jgh.15009\u003c/span\u003e\u003cspan address=\"10.1111/jgh.15009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJusril H, Ariawan I, Damayanti R, Lazuardi L, Musa M, Wulandari SM et al. Digital health for real-time monitoring of a national immunisation campaign in Indonesia. BMJ Open. 2020;10:e038282. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmjopen.bmj.com/content/10/10/e038282\u003c/span\u003e\u003cspan address=\"https://bmjopen.bmj.com/content/10/10/e038282\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation. Int J Med Inform., Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis. Respir Med. 2020;171:106093. Available from: https://doi.org/10.1016/j.rmed.2020.106093.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaffort J, Adam C, Carrier M, Ballaith A, Coscas R, Jean-Baptiste E et al. Artificial intelligence in abdominal aortic aneurysm. J Vasc Surg. 2020;72(1):321\u0026ndash;333.e1. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jvs.2019.10.09\u003c/span\u003e\u003cspan address=\"10.1016/j.jvs.2019.10.09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva K, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Machine learning models for type 2 diabetes prediction. Int J Med Inform. 2020;143:104268. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijmedinf.2020.104268\u003c/span\u003e\u003cspan address=\"10.1016/j.ijmedinf.2020.104268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas SK, Roy DK, Deb Roy S, Shil D. AI in Indian healthcare: from roadmap to reality. Intelligent Pharmacy [Internet]. 2024 Feb 19. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pharmaceuticalintelligence.com/\u003c/span\u003e\u003cspan address=\"https://www.pharmaceuticalintelligence.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalukder AK, Schriml L, Ghosh A, Biswas R, Chakrabarti P, Haas RE. Diseasomics: machine-interpretable disease knowledge at point-of-care. PLOS Digit Health. 2022;1(9):e0000128. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pdig.0000128\u003c/span\u003e\u003cspan address=\"10.1371/journal.pdig.0000128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas V, Kalidindi B, Waghmare A, Bhatia A, Raj T, Balsari S. The Vinyasa tool for mHealth solutions. JMIR Form Res. 2023;7:e45250. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://formative.jmir.org/2023/1/e45250/\u003c/span\u003e\u003cspan address=\"https://formative.jmir.org/2023/1/e45250/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma RS, Ar R, Jain S, Singh D. The Ayushman Bharat Digital Mission (ABDM). CSI Trans ICT. 2023;11(1):3\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40012-022-00401-\u003c/span\u003e\u003cspan address=\"10.1007/s40012-022-00401-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Dahdah M, Mishra RK. Digital health for all: digitized healthcare in India. Soc Sci Med. 2023;319:114968. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.socscimed.2023.114968\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2023.114968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital health, Universal health coverage, Artificial Intelligence, Telemedicine, Chatbots, AI","lastPublishedDoi":"10.21203/rs.3.rs-9188979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9188979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eDigital health and artificial intelligence are increasingly acknowledged as critical components in advancing Universal Health Coverage (UHC), particularly within low- and middle-income countries.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe objective of this systematic review is to analyse the impact of digital health and AI-driven interventions on healthcare access, efficiency, quality, and equity, focussing specifically on India.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA systematic review according to PRISMA guidelines was performed utilising PubMed, Scopus, and Web of Science for publications published from 2015 to 2025. Eligible studies underwent theme analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAI-powered digital health tools have shown potential benefits in areas such as diagnostics, patient engagement, and improving health systems and delivering services. Initiatives in India, such as the Ayushman Bharat Digital Mission, demonstrate how new, scalable systems are being developed to support Universal Health Coverage goals.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAI-driven digital health solutions are pivotal for attaining Universal Health Coverage, contingent upon the prioritisation of ethical governance, equity, and system integration.\u003c/p\u003e","manuscriptTitle":"Digital Health and the use of AI in Healthcare for Universal Health coverage- A PRISMA guided Systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 17:01:49","doi":"10.21203/rs.3.rs-9188979/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-12T09:36:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T17:51:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T14:19:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T14:18:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Health Systems","date":"2026-03-22T04:10:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3da2ace1-9c58-4565-a984-97549b955d22","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"15","date":"2026-05-12T09:36:27+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:07:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 17:01:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9188979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9188979","identity":"rs-9188979","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}