Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories

Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories | 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 Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories Aly Khalifa, Rada Hussein This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7402571/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Artificial intelligence (AI) is poised to transform healthcare through the lens of 6P medicine: personalized, predictive, preventive, participatory, precision, and public health. This review explores how AI technologies contribute to these six crucial dimensions, offering powerful solutions for enhanced early disease detection, tailored treatment protocols, proactive preventive health measures, and significantly improved patient engagement in their care journey. Our findings highlight AI's immense potential in revolutionizing diagnostics by enabling more accurate and rapid analyses, as well as strengthening public health surveillance capabilities. However, successful and responsible implementation hinges on rigorously addressing crucial cross-cutting challenges such as ensuring data quality, safeguarding patient privacy, navigating complex ethical considerations, seamless system integration, and fostering model interpretability. Aligning AI applications with evolving regulatory frameworks, such as the European Health Data Space (EHDS) and the AI Act, is absolutely essential to accelerate the ethical and effective adoption of 6P medicine. Ultimately, interdisciplinary collaboration among clinicians, technologists, and policymakers is key to unlocking AI's full power to improve patient outcomes and overall healthcare operational efficiency. 6P Medicine Artificial Intelligence Personalized Medicine Figures Figure 1 1. Introduction Artificial Intelligence (AI) is rapidly redefining the landscape of modern medicine, offering unprecedented opportunities to improve patient outcomes, optimize clinical workflows, and advance population health. Across hospitals and clinics, AI has demonstrated transformative potential through clinical decision support, medical imaging analysis, operational forecasting, and continuous monitoring using smart devices. By leveraging Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) technologies, healthcare is shifting toward a model that is not only more efficient but also more Predictive, Personalized, Preventive, and Participatory, known as the 4P medicine [ 1 ]. However, there is still some technical and regulatory challenges that need to be addressed on various levels of implementations [ 2 , 3 ]. Recent studies highlight the critical role of AI in diagnostic accuracy, risk prediction, treatment planning, and even administrative functions. From predictive models that anticipate sepsis or cancer progression, to AI-enhanced radiology systems that detect abnormalities beyond human capabilities, the integration of AI technologies is reshaping how care is delivered. These advances also support adding more Ps to the healthcare model, such as Population health (5P) [ 4 – 7 ] and Precision treatment (6P) using advanced diagnostic techniques [ 8 ]. Ultimately, the integrated advances in technology, engineering, and AI, are expected to move toward an 8P health model, including Psycho-cognitive dimension to include mental health, and Planetary to consider the environmental factors [ 9 ]. Despite its promise, AI implementation in healthcare faces notable challenges. These include interoperability across fragmented health systems [ 10 ], ensuring data privacy and security [ 11 ], managing algorithmic bias [ 12 , 13 ], and translating model performance from research to real-world clinical settings [ 10 ]. Moreover, aligning AI systems with existing clinical workflows [ 14 ]and demonstrating clinical utility remains a critical barrier to widespread adoption [ 15 ]. As we look toward the future, collaborative and multidisciplinary approaches will be essential to harness the full potential of AI in healthcare while ensuring safety, accountability, and benefit for all stakeholders. To efficiently address these advances against the associated challenges, we will focus in this study on the 6P medicine. 1.1. The 6P Medicine Model The concept of 6P medicine—personalized, predictive, preventive, participatory, precision, and public medicine—represents a paradigm shift in modern healthcare, emphasizing a holistic and patient-centered approach [ 4 , 6 , 16 – 18 ]. Personalized medicine customizes healthcare interventions to individual genetic, environmental, and lifestyle factors, enhancing treatment efficacy and reducing adverse effects [ 19 ]. Predictive medicine leverages data analytics to forecast disease onset and progression, enabling early intervention and improved patient outcomes. Preventive medicine focuses on proactive measures to avert disease, promoting long-term health and well-being [ 20 ]. Participatory medicine empowers patients to take an active role in their healthcare, fostering engagement and shared decision-making [ 21 ]. Precision medicine integrates multi-omics data to provide accurate diagnoses and targeted therapies [ 22 ]. Public medicine addresses population health, utilizing AI to optimize resource allocation and respond to public health crises [ 23 ]. 1.2. AI Applications in Healthcare AI applications in healthcare are diverse and impactful, encompassing diagnostic and imaging; predictive analysis and risk assessment; clinical documentation and workflow automation; treatment planning and personalization; patient monitoring and remote care; medical translation and communication; drug discovery and development; and operational efficiency and resource management [ 24 ]. Diagnostic and imaging AI tools can analyze medical images to detect diseases at an early stage, improving patient outcomes [ 25 – 27 ]. Predictive analysis and risk assessment leverage AI to forecast disease progression and identify high-risk patients, enabling timely interventions [ 28 , 29 ]. Clinical documentation and workflow automation streamline administrative tasks, allowing healthcare professionals to focus on patient care [ 25 , 30 ]. Treatment planning and personalization use AI to tailor therapies based on individual patient profiles, enhancing treatment efficacy [ 31 ]. Patient monitoring and remote care utilize AI to track patient health in real-time, providing continuous care and reducing hospital visits [ 32 , 33 ]. Medical translation and communication tools facilitate understanding between patients and healthcare providers, improving patient engagement [ 34 ]. Drug discovery and development benefit from AI's ability to analyze vast datasets, accelerating the identification of potential drug candidates [ 35 , 36 ]. Operational efficiency and resource management optimize healthcare processes, reducing costs and improving service delivery [ 37 ]. The integration of AI technologies into the 6P framework holds significant promise for transforming healthcare delivery. AI-driven tools, such as Large Language Models (LLMs), digital twins, AI agents, Generative AI, and Explainable AI (XAI), offer innovative solutions for analyzing vast amounts of medical data, simulating disease progression, and providing personalized health recommendations [ 30 ]. Despite the reported risks, these technologies enhance the accuracy of diagnoses, the efficacy of treatments, and the efficiency of healthcare systems [ 34 , 38 ]. However, the deployment of AI in healthcare also presents several challenges, including data quality and complexity, privacy and security concerns, ethical considerations, technical interoperability issues, and the interpretability of AI models. Addressing these challenges is essential for the successful implementation of AI-driven 6P medicine. European and international initiatives, such as the European Health Data Space (EHDS), the AI Act, and the EU AI Watch, provide regulatory frameworks to support the ethical and safe integration of AI technologies in healthcare. These initiatives advocate for multi-stakeholder engagement, increased transparency, and rigorous clinical validation of AI tools, ensuring that AI-driven innovations are ethically sound, clinically validated, and widely accessible. This paper aims to provide a narrative review of AI applications in 6P medicine, identify the key challenges and opportunities, and offer recommendations for advancing AI integration in healthcare. By aligning AI applications with regulatory frameworks and fostering interdisciplinary collaboration, healthcare organizations can leverage AI to improve patient outcomes, enhance operational efficiency, and foster a more equitable healthcare system. 2. Materials and Methods A narrative review was conducted to thoroughly identify and analyze AI tools, applications, challenges, and expected benefits in the context of 6P medicine. The review encompassed a search of peer-reviewed literature, focusing on studies that elucidate the integration of AI in healthcare. Key databases such as PubMed, IEEE Xplore, and Google Scholar were utilized to gather relevant articles in English language and published between 2019 and August 2025 using the following keywords: Artificial Intelligence, Machine Learning, Large Language Models, Agentic Application Predictive, Personalized, Preventive, Participatory, Precision, Public health 4P, 5P, P5, 6P Medicine The inclusion criteria were studies that specifically addressed AI-driven approaches in the six domains of 6P medicine. Data extraction involved categorizing AI tools and applications, identifying common challenges and summarizing the anticipated benefits in enhancing patient outcomes and healthcare efficiency. Based on the synthesized literature, an implantation analysis was conducted based on four main perspectives including: AI/ML technological patterns across the 6P dimensions Implementation themes 6P medicine Clinical validation approaches Key challenges, relevant success factors, and reported recommendations 3. Results By adhering to the aforementioned methodological framework, this narrative review synthesizes findings from a broad spectrum of more than 80 published and reported research on AI applications across multiple dimensions of 6P medicine. The first section of the consolidated results reveals recurring patterns within AI and ML methodologies employed in these studies. Subsequent sections delineate prevalent AI/ML techniques pertinent to 6P medicine. Additionally, the review presents a comprehensive implementation analysis addressing application trends, cross-dimensional integrations, clinical validation strategies, as well as critical challenges and success factors influencing the effective deployment of AI in advanced 6P medicine paradigms. 3.1. Consolidated Results The analysis of AI applications in the context of 6P medicine reveals significant advancements and diverse use cases across various healthcare domains. Table 1 summarizes the key findings of this analysis in terms of employed AI tools, success metrics, and reported challenges. Table 1 Dimensions of 6P medicine and their corresponding AI technologies, key outcomes, and reported challenges P Dimension Main AI methods Key Outcomes (and Implementation Success Metrics) Challenges Predictive Deep learning (convolutional neural networks, recurrent neural networks), ensemble methods (random forest, gradient boosting machines), decision trees Machine Learning (ML) (Random Forest, Support Vector Machine, Decision Tree, Gradient Boosting Machine), Deep Learning (DL) (Convolutional Neural Network, Long Short-Term Memory), ensemble, causal ML High accuracy in disease prediction, early detection, risk stratification [ 39 ] Accuracy (up to 99%), Area Under the Curve, sensitivity, specificity, F1 [ 39 ], recall Data quality [ 40 ], lack of external validation[ 41 ], model interpretability[ 42 ], integration [ 2 ], bias [ 43 ] Personalized Multi-modal machine learning, XAI, clustering, federated learning Machine Learning (ML), Deep Learning (DL), Explainable AI (XAI), feature importance, federated learning Individualized treatment, risk profiles, tailored interventions Personalized recommendations [ 44 ], high Area Under the Curve, patient stratification [ 45 ] Data integration [ 46 ], privacy [ 47 ], model complexity (black box problem) [ 48 ], Data heterogeneity [ 49 ], clinician trust [ 50 , 51 ] Preventive Predictive analytics, mobile health, wearables Predictive analytics, Machine Learning (ML), Deep Learning (DL) Early intervention, risk identification, proactive care [ 52 ] Early detection rates, reduced ICU occupancy, cost savings [ 53 ] Limited real-world evidence [ 54 ], data privacy [ 47 ], engagement [ 55 , 56 ], Data standardization [ 57 ], ethical concerns [ 58 , 59 ], workflow fit [ 60 ] Participatory Genomics, imaging, XAI, multi-task learning Mobile health, Clinical Decision Support Systems, participatory apps Patient engagement, shared decision-making [ 61 ] User engagement, Retention [ 62 ] Underrepresentation [ 63 ], user acceptance [ 64 ], workflow integration [ 2 ], Digital literacy [ 65 , 66 ], transparency [ 48 ] Precision Genomics, imaging, XAI, multi-task learning Multimodal fusion, transfer learning Subgroup targeting, high-resolution disease definition [ 67 ] Diagnostic accuracy [ 68 ], precision, recall, specificity Interpretability [ 42 ], regulatory approval [ 69 , 70 ], data heterogeneity [ 49 ], Data integration [ 46 ], Validation [ 40 ], scalability [ 71 ] Public Surveillance, resource allocation, population health machine learning Machine Learning (ML)/Deep Learning (DL) for surveillance, resource allocation Outbreak detection [ 72 – 74 ], resource optimization [ 23 ] Population-level outcomes, cost savings [ 53 ], operational gains [ 23 ] Data standardization [ 57 ], ethical concerns [ 58 , 59 ], scalability [ 71 ], Infrastructure [ 75 ], data access [ 76 ], regulatory barriers [ 69 , 70 ] 3.2. Pertinent Technologies Per Each Dimension This section describes the reported technologies per each “P” dimension of the 6P medicine paradigm, where each couple of closely related dimensions are grouped due to common technologies and aligned goals. Some technologies span more than one “P” dimension even after grouping relevant “Ps” because some AI applications serve more than one purpose. This observation aligns with the integrated concept of the overall 6P medicine. These patterns are further discussed within the “Implementation Analysis” section below. Predictive and Preventive Healthcare AI methodologies are most frequently applied to predictive and preventive healthcare, including early disease detection, risk stratification, and resource optimization. Machine learning models (e.g., random forests, support vector machines, ensemble methods) and deep learning architectures (e.g., Convolutional Neural Networks, Long Short-Term Memory networks) are widely used for forecasting clinical events, identifying at risk populations, and supporting preventive interventions. Several studies report high accuracy, sensitivity, and specificity in predictive tasks, though external validation and real-world impact are less consistently documented [ 77 – 80 ]. Many studies focused on predictive modeling for early detection, risk assessment, and preventive intervention [ 29 ]. AI technologies, particularly LLMs and digital twins, have shown promise in predictive and preventive healthcare as described in Table 2 . LLMs can analyze vast amounts of medical literature, research papers, and patient records to identify subtle patterns and correlations that might indicate future health risks or disease outbreaks. While digital twins, which are virtual replicas of patients or physiological systems, can simulate disease progression and predict potential health outcomes under different scenarios [ 81 ]. For example: Forecast disease progression and optimize treatment regimens in diabetes [ 82 ]. Clinical practice and research of chronic lung diseases [ 83 ]. Drug discovery and clinical trial simulations [ 84 ] Table 2 AI Applications in Predictive and Preventive Healthcare Technology Example Use Case Description LLMs Analyzing medical images alongside patient history and genomic data [ 85 ] Predicts disease progression or treatment response more accurately than unimodal models Digital Twins Simulating the impact of different chemotherapy regimens on a cancer patient's digital twin [ 86 ] Identifies the most effective and least toxic treatment option Personalized and Precision Medicine Personalized and precision medicine is a central theme, particularly in oncology [ 87 , 88 ], chronic disease management [ 82 , 83 ], and pharmacotherapy [ 89 , 90 ]. AI models leverage multimodal data (clinical, genomic, imaging, behavioral) to tailor treatment recommendations, predict therapy response, and optimize dosing. Explainable AI and feature importance methods are increasingly adopted to enhance transparency and clinician trust. Reported performance metrics are often high (Area Under the Curve values > 0.9 in some oncology applications), but generalizability and integration into clinical workflows remain challenges. Several included studies reported the use of digital twins and LLMs to enable highly personalized patient modeling and care. These systems often leverage advanced LLMs or hybrid architectures to synthesize complex data and provide actionable insights. Integration with existing clinical systems and privacy-preserving data architectures (e.g., federated learning) were highlighted as critical for real-world adoption [ 49 ]. AI-driven genomic analysis and multi-omics data integration enhance diagnostic accuracy and treatment efficacy [ 67 , 91 ]. Table 3 highlights some examples of relevant AI technologies for these purposes. AI agents and generative AI can play crucial roles in generating personalized health advice, reminders, and customized educational materials based on individual risk factors and lifestyle [ 71 ]. Table 3 AI Applications in Personalized and Precision Medicine Technology Example Use Case Description AI Agents Monitoring a diabetic patient's glucose levels and activity patterns [ 92 , 93 ] Predicts potential hypoglycemic episodes and suggests adjustments to insulin dosage or dietary intake Generative AI Creating personalized educational materials and visual aids [ 94 ] Helps patients understand their conditions and treatment options better Participatory and Public Health Participatory and public health dimensions are less frequently operationalized but are addressed in studies involving patient engagement (e.g., mobile health, shared decision-making), population health surveillance [ 95 ], and resource allocation [ 76 ]. AI-driven tools for public health (e.g., epidemic prediction, resource optimization in Low- and Middle-Income Countries) demonstrate potential for large-scale impact, though evidence of sustained adoption and outcome improvement is limited. LLMs are increasingly used to enhance patient engagement and participation. For example, use of LLMs for generating patient education materials [ 96 , 97 ], simulating virtual patients for training [ 98 ], and facilitating history-taking [ 99 – 101 ]. These applications aim to improve health literacy, empower patients, and foster participatory care models. Visualization of digital twins and conversational interfaces were also explored as means to increase patient involvement in their own care [ 102 – 104 ]. AI-driven health assistants and chatbots may facilitate patient engagement and self-management, empowering individuals to take an active role in their healthcare as shown in Table 4 . AI-supported shared decision-making tools and personalized health education interventions may have the potential to improve health literacy and patient empowerment [ 61 , 105 – 107 ]. In public health, AI-powered surveillance systems and population-level analytics optimize resource allocation and enhance the ability to respond to health crises [ 23 , 76 ]. Table 4 AI Applications in Participatory and Public Health Technology Example Use Case Description LLM, AI Agents Providing clear and concise explanations of lab results to patients [ 108 ] Enhances patient understanding and engagement Generative AI Creating tailored content for public health campaigns [ 109 ] Promotes healthy behaviors and disease prevention 3.3. Implementation Analysis The implementation analysis involved four main perspectives emphasizing AI/ML patterns, cross-dimensional implementations, clinical validation approaches as well as key challenges and success factors as described below. Implementation Across Healthcare Dimensions Predictive analytics is the most commonly addressed dimension. AI systems are used to forecast disease onset, adverse events, and treatment outcomes. Several studies report high accuracy and area under the curve values, but many do not describe their validation methods in detail [ 88 , 110 – 112 ]. Personalization is operationalized through individualized treatment recommendations, risk stratification, and tailored interventions based on patient-specific data (such as genomics, imaging, and demographics). This is a central theme in oncology, cardiology, and pain management studies [ 113 – 115 ]. Preventive care is addressed through early detection models, risk prediction tools, and proactive monitoring (including wearables and mobile health). However, fewer studies provide robust evidence of preventive impact in real-world settings [ 116 – 118 ]. Participatory approaches are less common but are present in mobile health, app-based interventions, and studies involving patient engagement through wearables or co-created public health interventions [ 92 , 119 , 120 ]. Precision medicine is achieved through the integration of multi-modal data (genomics, imaging, electronic health records) and the development of models targeting specific subgroups or disease phenotypes. Some studies emphasize the need for explainable and interpretable models for regulatory approval and clinical adoption [ 117 , 121 , 122 ]. Public health integration is addressed in studies focusing on surveillance, resource allocation, and population-level interventions. The operationalization of AI at the public health level remains limited compared to individual-level applications [ 23 , 76 , 95 ]. Clinical Validation Approaches In the context of implementing AI within 6P medicine, clinical validation approaches are essential to ensure reliability and safety across diverse patient populations and care settings [ 41 , 78 ]. This involves rigorous evaluation of AI models through retrospective and prospective clinical studies, focusing on accuracy, generalizability, and real-world applicability [ 41 ]. Validation approaches range from internal cross-validation to external validation on independent datasets. Some studies report real-world deployment and operational metrics (e.g., reduction in scheduling times, improved resource utilization), while others remain at the proof-of-concept stage [ 41 , 79 ]. Metrics such as accuracy, Area Under the Curve, sensitivity, specificity, and F1 score are commonly used, but reporting is inconsistent. Validation and performance reporting are inconsistent. While several studies report high performance metrics, the lack of standardized validation methods and external validation limits the generalizability of findings [ 78 , 80 ]. There is a need for more rigorous reporting and real-world evaluation, as noted in the reviewed publications. Key Challenges, Success Factors, and Indicators The results above highlight the diverse applications and significant potential of AI in advancing 6P medicine. Addressing the identified challenges through targeted recommendations can further enhance the integration and impact of AI technologies in healthcare. This section elaborates on the reported challenges while providing practical recommendations for better implementation strategies. Table 5 consolidates the reported challenges, mitigation approaches, and success indicators. In addition, the lack of standardized validation, evaluation, and reporting metrics can limit the adoption rate of some promising AI applications. Table 5 Implementation challenges, mitigation approaches, and success indicators. Challenge Category Evidence-Based Solutions Success Indicators Adoption Considerations Data Quality & Heterogeneity Data cleaning, feature engineering, local data validation Improved model performance, reduced bias Access to representative data, standardization Privacy & Security Federated learning, blockchain, anonymization Compliance, user trust Regulatory alignment, technical feasibility Interpretability Explainable AI (XAI), feature importance, Local Interpretable Model-agnostic Explanations (LIME) / SHapley Additive exPlanations (SHAP) Clinician acceptance, transparency Training, usability Workflow Integration Substitutable Medical Applications, Reusable Technologies (SMART) on Fast Healthcare Interoperability Resources (FHIR), modular platforms, stakeholder engagement Operational efficiency, user adoption Leadership, change management Ethical/Legal Policy frameworks, auditability, risk management Compliance, reduced liability Legal clarity, stakeholder buy-in Resource Constraints Cloud computing, cost-effective models Cost savings, scalability Investment, infrastructure Cross-Cutting Implementation Challenges : Common technical challenges include data quality and heterogeneity, privacy and security concerns, lack of interpretability, and difficulties integrating AI into existing clinical workflows. Several studies highlight the “black box” nature of deep learning as a barrier to clinician acceptance. We didn’t find any healthcare dimension without at least one AI method and one implementation challenge reported within the reviewed publications. Figure 1depicts some of the crossing cutting challenges across the continuum of AI application development, deployment, and maintenance [ 56 , 75 , 123 , 124 ]. Data integration of heterogeneous sources (electronic health records, imaging, genomics, wearables, Internet of Things) is a recurring challenge and opportunity [ 49 ]. Studies highlight the need for robust data preprocessing, feature engineering, and standardization. In addition, clinical workflow integration is identified as a barrier, including the need for validation in real-world settings and user acceptance [ 2 , 51 , 60 ]. Participatory and explainable AI approaches are proposed in several studies to enhance adoption [ 124 ]. Success Factors Reported success factors include addressing data heterogeneity, ensuring algorithm transparency, mitigating biases, securing regulatory approvals, and fostering multidisciplinary collaboration. These factors hinge on robust clinical evidence, seamless integration into clinical workflows, user trust, and continuous monitoring to adapt AI tools to evolving medical knowledge and patient needs. Evidence-based solutions include the adoption of XAI, federated learning for privacy, robust data governance, stakeholder engagement, and technical frameworks for integration. Success indicators include improved clinical or operational outcomes, user trust, and scalability. Addressing these challenges is crucial for the successful deployment of AI technologies in 6P medicine. Table 4 lists most prominent challenges and corresponding recommendations. Together, these elements shape the pathway for AI to effectively support the transformative goals of 6P medicine. Table 6 Cross-Cutting Implementation Challenges and Recommendations Challenge Description Recommendation Data Quality and Complexity Ensuring high-quality, representative data for AI model training Establish robust data governance frameworks and advanced data management platforms Privacy and Security Data privacy concerns, particularly in personalized medicine and genomic analysis Adopt comprehensive data protection measures and ensure compliance with regulations such as GDPR Ethical Considerations Addressing algorithmic bias, fairness, and transparency Use diverse datasets and develop transparent, explainable algorithms Integration with Existing Systems Technical interoperability issues Implement interoperable platforms supporting HL7 and FHIR standards Model Interpretability "Black box" nature of some AI algorithms Develop and implement explainable AI techniques 4. Discussion The results of this narrative review underscore the transformative potential of AI in advancing the concept of 6P medicine. The integration of AI technologies such as LLMs, digital twins, AI agents, generative AI, and XAI across various domains of healthcare demonstrates significant improvements in early disease detection, personalized treatment planning, preventive health measures, patient engagement, precision diagnostics, and public health surveillance [ 24 , 71 ]. AI applications in predictive healthcare, such as early disease detection and forecasting treatment outcomes, are crucial for optimizing patient care and resource utilization. Preventive healthcare benefits from AI-powered health monitoring systems and personalized health recommendations, which can significantly reduce the incidence of chronic diseases and improve population health outcomes. The integration of AI in personalized and precision medicine, particularly through genomic analysis and multi-omics data integration, enhances the accuracy of diagnoses and the efficacy of treatments [ 91 ]. AI-driven predictive modeling for individual patient responses to therapies ensures tailored interventions, reducing adverse drug reactions and accelerating drug discovery processes. In addition, AI-driven health assistants and chatbots facilitate patient engagement and self-management, empowering individuals to take an active role in their healthcare. AI-supported shared decision-making tools and personalized health education interventions improve health literacy and patient empowerment. In public health, AI-powered surveillance systems and population-level analytics optimize resource allocation and enhance the ability to respond to health crises [ 23 ]. These applications highlight the role of AI to support the various dimensions of 6P medicine [ 71 ]. 4.1 Aligning to National, Regional, and International Initiatives and Regulations There is an urgent need to establish clear and efficient AI regulations at the national, regional, and international levels to ensure optimal resource allocation, streamlined development processes, and robust implementation of AI projects. On the national level, the United States, Germany, and UK have issued their strategies and initiatives for AI in healthcare [ 125 – 127 ]. In addition, the U.S. Food and Drug Administration (FDA) has issued its AI/ML Software as a Medical Device Action Plan outlining required steps to regulate AI/ML applications as part of medical products [ 128 ]. The USA AI Risk Management Framework addresses best practices to mitigate relevant risks through the AI lifecycle from design to implementation [ 129 ]. Other countries such as Australia and UK have initiated similar activities to motivate and regulate AI/ML for medical purposes [ 130 – 132 ]. European and international initiatives such as EHDS, the AI Act, and the EU AI Watch, emphasize the importance of fostering AI integration into healthcare practice through appropriate policies to enhance equity, improve care, and ensure the benefits of new technologies [ 133 , 134 ]. The EHDS aims to create a common framework for health data exchange across Europe, facilitating the use of AI in healthcare by ensuring data interoperability and accessibility [ 135 , 136 ]. The AI Act provides a regulatory framework to ensure the safe and ethical deployment of AI technologies, promoting transparency, accountability, and fairness in AI applications [ 137 ]. EU AI Watch monitors and supports the implementation of AI policies, providing insights and recommendations to enhance AI adoption in various sectors, including healthcare [ 138 ]. Moreover, the Coalition for Health AI (CHAI) and World Health Organization (WHO) have recently released several guiding documents on ethics and governance of AI for health [ 139 , 140 ]. These initiatives advocate for multi-stakeholder engagement, increased transparency, and rigorous clinical validation of AI tools. However, more work is still needed to cope with the very dynamic and rapidly growing field of AI/ML in terms of new technologies, opportunities and challenges. Aligning AI applications with these regulatory frameworks can accelerate the adoption of 6P medicine, ensuring that AI-driven innovations are ethically sound, clinically validated, and widely accessible. By adhering to these guidelines, healthcare organizations can leverage AI to improve patient outcomes, enhance operational efficiency, and foster a more equitable healthcare system [ 130 ]. 4.2 Recommendations for Advancing AI Applications Based on the results listed above, several key recommendations can be made to advance AI applications and support the realization of 6P medicine. Both medical, technical, and regulatory parties need to collaborate to address data quality and complexity as well as establishing robust data governance frameworks with standardized protocols for data collection, cleaning, and integration. This approach needs to consider implementing advanced data management platforms capable of handling large volumes of unstructured and multi-omics data to ensure that AI models are trained on high-quality, representative datasets. Additional safeguards should consider enhancing the reliability of AI predictions and to mitigate the risk of biases that can arise from poor data quality. Considering the privacy and security in the deployment of AI in healthcare by adopting comprehensive data protection measures, including encryption, anonymization, and secure data storage solutions, must be adopted to safeguard patient information, e.g., federated learning approach [ 49 ]. Ensuring compliance with regulations such as GDPR and HIPAA by prioritizing data minimization and pseudonymization is crucial for maintaining patient trust and legal compliance and to balance the need for data accessibility with the imperative to protect patient privacy. Developing and implementing XAI techniques will enhance model transparency and model interpretability, allowing healthcare professionals to understand and trust AI-driven decisions. Integrating user-friendly visualization tools and interactive dashboards to present AI insights may bridge the gap between complex AI models and practical clinical applications. There is a critical and growing need to develop, disseminate, and adopt standardized validation, evaluation, and reporting metrics for AI applications in healthcare to ensure patient safety, clinical efficacy, and regulatory compliance. The complexity of AI models—especially generative and multimodal systems integrating diverse data types such as text, imaging, and videos—demands robust, objective, and reproducible frameworks to assess their accuracy, reliability, and clinical utility. Without such standards, inconsistencies in AI performance evaluation limit the ability to compare studies, impede regulatory approval, and reduce clinician and patient trust. Recent efforts highlight frameworks like the METRICS checklist for standardized reporting in generative AI health studies and emerging clinician-informed evaluation protocols that emphasize transparency, fairness, and clinical relevance. These frameworks are vital to bridging the translational gap from AI model development to scalable, safe deployment in clinical workflows, aligning with evolving regulatory requirements such as the EU AI Act and U.S. Executive Orders on AI. Overall, coordinated multidisciplinary collaboration is essential to advance these standards, which will underpin ethical, effective, and equitable AI integration in healthcare. Finally, all stakeholders (e.g., researchers, developers, clinicians, patients, and policy makers) must put ethical considerations at the forefront of AI implementation in healthcare. Using diverse and representative datasets during model training and update can help mitigate algorithmic bias, ensuring equitable outcomes for all patient groups. Developing transparent and explainable algorithms can promote accountability and trust among stakeholders. Also, integrating AI with existing healthcare systems requires the implementation of interoperable platforms that support interoperability standards such as HL7 FHIR and OHDSI OMOP standards for seamless data exchange. Embracing digital transformation (including AI technologies) necessitates cultural shifts within healthcare organizations to ensure successful integration and optimal utilization of AI tools. These recommendations collectively aim to advance AI applications in healthcare, supporting the realization of 6P medicine and improving patient outcomes. 4.3 Limitations and Future Research Steps This study has several limitations, including the reliance on narrative review methodology, which may introduce selection bias and limit the comprehensiveness of the findings [ 141 ]. Additionally, the rapid evolution of AI technologies necessitates continuous updates to ensure relevance. Future research should focus on the long-term impact of AI applications on patient outcomes and healthcare systems, as well as to develop standardized metrics for evaluating AI performance in clinical settings by conducting systematic reviews and meta-analyses [ 142 , 143 ]. 5. Conclusions The integration of AI technologies in healthcare offers significant promise for advancing 6P medicine. Several challenges and opportunities arise from the changing role of healthcare professionals in AI-augmented healthcare systems. Balancing flexibility with patient safety and ethical standards, while addressing issues such as cost, healthcare outcomes, technology advancements, available resources/infrastructure, and the satisfaction of healthcare workers and patients, is vital for successful implementation. This work emphasizes the need for interdisciplinary collaboration involving clinicians, data scientists, ethicists, policymakers, and patients to address these challenges and harness the full potential of AI in healthcare. Several recommendations were provided to ensure the generalizability of predictive models across diverse populations is crucial to avoid biases and enhance the reliability of AI-driven interventions. Also, balancing the benefits of early intervention with the risks of overdiagnosis and unnecessary treatments requires careful consideration of ethical concerns, including patient privacy and security. Finally, integrating AI-driven tools into existing healthcare workflows and decision-making processes is essential for seamless adoption and maximizing the impact of AI on patient outcomes. Abbreviations 6P Medicine, personalized, predictive, preventive, participatory, precision, and public medicine; AI, Artificial Intelligence; DL, Deep Learning; EHDS, European Health Data Space; EU, European Union; FAIR, Findability, Accessibility, Interoperability, and Reusability; FHIR, Fast Healthcare Interoperability Resource; GDPR, General Data Protection Regulation; HL7, Health Level 7; LLMs, Large Language Models; ML, Machine Learning; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership; WHO, World Health Organization; XAI, Explainable AI. Declarations Data Statement No data sets were generated during this study. CRediT authorship contribution statement Aly Khalifa: Conceptualization, Methodology, Formal analysis, Writing – Original Draft Preparation, Writing – Review & Editing. Rada Hussein: Conceptualization, Methodology, Formal analysis, Writing – Original Draft Preparation, Writing – Review & Editing. Funding Declaration The authors declare that this research was conducted without any specific grant from funding agencies. Clinical trial number Not applicable Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The last author is a member of the JoMs editorial board. References Alonso SG, de la Torre Díez I, Zapiraín BG. Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature. 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AIDH 2023. https://digitalhealth.org.au/blog/australian-roadmap-for-artificial-intelligence-in-healthcare-to-be-launched-at-ai-care/ (accessed April 22, 2025). Impact of AI on the regulation of medical products. GOVUK n.d. https://www.gov.uk/government/publications/impact-of-ai-on-the-regulation-of-medical-products/impact-of-ai-on-the-regulation-of-medical-products (accessed April 22, 2025). Artificial Intelligence in healthcare - European Commission 2025. https://health.ec.europa.eu/ehealth-digital-health-and-care/artificial-intelligence-healthcare_en (accessed April 22, 2025). Artificial intelligence in healthcare: Applications, risks, and ethical and societal impacts | Panel for the Future of Science and Technology (STOA) | European Parliament n.d. https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2022)729512 (accessed April 22, 2025). European Health Data Space Regulation (EHDS) - European Commission 2025. https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en (accessed April 22, 2025). Petročnik T, Palmieri S, Marot J-A. The AI Act and European Health Data Space Proposal: Seeing AI to AI With Each Other? Eur Law Blog 2023. https://doi.org/10.21428/9885764c.1523cf58. EU Artificial Intelligence Act | Up-to-date developments and analyses of the EU AI Act n.d. https://artificialintelligenceact.eu/ (accessed October 4, 2024). AI Watch 2025. https://ai-watch.ec.europa.eu/index_en (accessed April 22, 2025). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models n.d. https://www.who.int/publications/i/item/9789240084759 (accessed April 22, 2025). Coalition for Health AI (CHAI). CHAI - Coalit Health AI n.d. https://chai.org/ (accessed October 13, 2024). Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195. https://doi.org/10.1186/s12916-019-1426-2. Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 2025;388:e081554. https://doi.org/10.1136/bmj-2024-081554. The Future of AI in Healthcare – 2025. SSC Blue Prism n.d. https://www.blueprism.com/resources/blog/the-future-of-ai-in-healthcare/ (accessed April 22, 2025). Additional Declarations Competing interest reported. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The last author is a member of the Journal of Medical Systems (JoMs) editorial board. 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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. \nThe last author is a member of the Journal of Medical Systems (JoMs) editorial board.","formattedTitle":"Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) is rapidly redefining the landscape of modern medicine, offering unprecedented opportunities to improve patient outcomes, optimize clinical workflows, and advance population health. Across hospitals and clinics, AI has demonstrated transformative potential through clinical decision support, medical imaging analysis, operational forecasting, and continuous monitoring using smart devices. By leveraging Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) technologies, healthcare is shifting toward a model that is not only more efficient but also more Predictive, Personalized, Preventive, and Participatory, known as the 4P medicine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, there is still some technical and regulatory challenges that need to be addressed on various levels of implementations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies highlight the critical role of AI in diagnostic accuracy, risk prediction, treatment planning, and even administrative functions. From predictive models that anticipate sepsis or cancer progression, to AI-enhanced radiology systems that detect abnormalities beyond human capabilities, the integration of AI technologies is reshaping how care is delivered. These advances also support adding more Ps to the healthcare model, such as Population health (5P) [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and Precision treatment (6P) using advanced diagnostic techniques [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ultimately, the integrated advances in technology, engineering, and AI, are expected to move toward an 8P health model, including Psycho-cognitive dimension to include mental health, and Planetary to consider the environmental factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its promise, AI implementation in healthcare faces notable challenges. These include interoperability across fragmented health systems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], ensuring data privacy and security [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], managing algorithmic bias [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and translating model performance from research to real-world clinical settings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, aligning AI systems with existing clinical workflows [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]and demonstrating clinical utility remains a critical barrier to widespread adoption [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As we look toward the future, collaborative and multidisciplinary approaches will be essential to harness the full potential of AI in healthcare while ensuring safety, accountability, and benefit for all stakeholders. To efficiently address these advances against the associated challenges, we will focus in this study on the 6P medicine.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. The 6P Medicine Model\u003c/h2\u003e\u003cp\u003eThe concept of 6P medicine\u0026mdash;personalized, predictive, preventive, participatory, precision, and public medicine\u0026mdash;represents a paradigm shift in modern healthcare, emphasizing a holistic and patient-centered approach [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Personalized medicine customizes healthcare interventions to individual genetic, environmental, and lifestyle factors, enhancing treatment efficacy and reducing adverse effects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Predictive medicine leverages data analytics to forecast disease onset and progression, enabling early intervention and improved patient outcomes. Preventive medicine focuses on proactive measures to avert disease, promoting long-term health and well-being [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Participatory medicine empowers patients to take an active role in their healthcare, fostering engagement and shared decision-making [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Precision medicine integrates multi-omics data to provide accurate diagnoses and targeted therapies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Public medicine addresses population health, utilizing AI to optimize resource allocation and respond to public health crises [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. AI Applications in Healthcare\u003c/h2\u003e\u003cp\u003eAI applications in healthcare are diverse and impactful, encompassing diagnostic and imaging; predictive analysis and risk assessment; clinical documentation and workflow automation; treatment planning and personalization; patient monitoring and remote care; medical translation and communication; drug discovery and development; and operational efficiency and resource management [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Diagnostic and imaging AI tools can analyze medical images to detect diseases at an early stage, improving patient outcomes [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Predictive analysis and risk assessment leverage AI to forecast disease progression and identify high-risk patients, enabling timely interventions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Clinical documentation and workflow automation streamline administrative tasks, allowing healthcare professionals to focus on patient care [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Treatment planning and personalization use AI to tailor therapies based on individual patient profiles, enhancing treatment efficacy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Patient monitoring and remote care utilize AI to track patient health in real-time, providing continuous care and reducing hospital visits [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Medical translation and communication tools facilitate understanding between patients and healthcare providers, improving patient engagement [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Drug discovery and development benefit from AI's ability to analyze vast datasets, accelerating the identification of potential drug candidates [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Operational efficiency and resource management optimize healthcare processes, reducing costs and improving service delivery [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe integration of AI technologies into the 6P framework holds significant promise for transforming healthcare delivery. AI-driven tools, such as Large Language Models (LLMs), digital twins, AI agents, Generative AI, and Explainable AI (XAI), offer innovative solutions for analyzing vast amounts of medical data, simulating disease progression, and providing personalized health recommendations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Despite the reported risks, these technologies enhance the accuracy of diagnoses, the efficacy of treatments, and the efficiency of healthcare systems [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, the deployment of AI in healthcare also presents several challenges, including data quality and complexity, privacy and security concerns, ethical considerations, technical interoperability issues, and the interpretability of AI models. Addressing these challenges is essential for the successful implementation of AI-driven 6P medicine. European and international initiatives, such as the European Health Data Space (EHDS), the AI Act, and the EU AI Watch, provide regulatory frameworks to support the ethical and safe integration of AI technologies in healthcare. These initiatives advocate for multi-stakeholder engagement, increased transparency, and rigorous clinical validation of AI tools, ensuring that AI-driven innovations are ethically sound, clinically validated, and widely accessible.\u003c/p\u003e\u003cp\u003eThis paper aims to provide a narrative review of AI applications in 6P medicine, identify the key challenges and opportunities, and offer recommendations for advancing AI integration in healthcare. By aligning AI applications with regulatory frameworks and fostering interdisciplinary collaboration, healthcare organizations can leverage AI to improve patient outcomes, enhance operational efficiency, and foster a more equitable healthcare system.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eA narrative review was conducted to thoroughly identify and analyze AI tools, applications, challenges, and expected benefits in the context of 6P medicine. The review encompassed a search of peer-reviewed literature, focusing on studies that elucidate the integration of AI in healthcare. Key databases such as PubMed, IEEE Xplore, and Google Scholar were utilized to gather relevant articles in English language and published between 2019 and August 2025 using the following keywords:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eArtificial Intelligence, Machine Learning, Large Language Models, Agentic Application\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePredictive, Personalized, Preventive, Participatory, Precision, Public health\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e4P, 5P, P5, 6P Medicine\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe inclusion criteria were studies that specifically addressed AI-driven approaches in the six domains of 6P medicine. Data extraction involved categorizing AI tools and applications, identifying common challenges and summarizing the anticipated benefits in enhancing patient outcomes and healthcare efficiency.\u003c/p\u003e\u003cp\u003eBased on the synthesized literature, an implantation analysis was conducted based on four main perspectives including:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAI/ML technological patterns across the 6P dimensions\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eImplementation themes 6P medicine\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClinical validation approaches\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eKey challenges, relevant success factors, and reported recommendations\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eBy adhering to the aforementioned methodological framework, this narrative review synthesizes findings from a broad spectrum of more than 80 published and reported research on AI applications across multiple dimensions of 6P medicine. The first section of the consolidated results reveals recurring patterns within AI and ML methodologies employed in these studies. Subsequent sections delineate prevalent AI/ML techniques pertinent to 6P medicine. Additionally, the review presents a comprehensive implementation analysis addressing application trends, cross-dimensional integrations, clinical validation strategies, as well as critical challenges and success factors influencing the effective deployment of AI in advanced 6P medicine paradigms.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Consolidated Results\u003c/h2\u003e\n \u003cp\u003eThe analysis of AI applications in the context of 6P medicine reveals significant advancements and diverse use cases across various healthcare domains. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the key findings of this analysis in terms of employed AI tools, success metrics, and reported challenges.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable 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\u003eDimensions of 6P medicine and their corresponding AI technologies, key outcomes, and reported challenges\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Dimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMain AI methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey Outcomes\u003c/p\u003e\n \u003cp\u003e(and Implementation Success\u003c/p\u003e\n \u003cp\u003eMetrics)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChallenges\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\"\u003e\n \u003cp\u003ePredictive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep learning (convolutional neural networks, recurrent neural networks), ensemble methods (random forest, gradient boosting machines), decision trees\u003c/p\u003e\n \u003cp\u003eMachine Learning (ML)\u003c/p\u003e\n \u003cp\u003e(Random Forest,\u003c/p\u003e\n \u003cp\u003eSupport Vector Machine,\u003c/p\u003e\n \u003cp\u003eDecision Tree, Gradient\u003c/p\u003e\n \u003cp\u003eBoosting Machine),\u003c/p\u003e\n \u003cp\u003eDeep Learning (DL)\u003c/p\u003e\n \u003cp\u003e(Convolutional Neural\u003c/p\u003e\n \u003cp\u003eNetwork, Long\u003c/p\u003e\n \u003cp\u003eShort-Term Memory),\u003c/p\u003e\n \u003cp\u003eensemble, causal ML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh accuracy in disease prediction, early detection, risk stratification [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eAccuracy (up to 99%),\u003c/p\u003e\n \u003cp\u003eArea Under the Curve,\u003c/p\u003e\n \u003cp\u003esensitivity, specificity,\u003c/p\u003e\n \u003cp\u003eF1 [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData quality [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], lack of external validation[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], model interpretability[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eintegration [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e], bias [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti-modal machine learning, XAI, clustering, federated learning\u003c/p\u003e\n \u003cp\u003eMachine Learning (ML),\u003c/p\u003e\n \u003cp\u003eDeep Learning (DL),\u003c/p\u003e\n \u003cp\u003eExplainable AI (XAI),\u003c/p\u003e\n \u003cp\u003efeature importance,\u003c/p\u003e\n \u003cp\u003efederated learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividualized treatment, risk profiles, tailored interventions\u003c/p\u003e\n \u003cp\u003ePersonalized recommendations [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], high\u003c/p\u003e\n \u003cp\u003eArea Under the Curve,\u003c/p\u003e\n \u003cp\u003epatient stratification [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData integration [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e], privacy [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], model complexity (black box problem) [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eData heterogeneity [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], clinician trust [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreventive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredictive analytics, mobile health, wearables\u003c/p\u003e\n \u003cp\u003ePredictive analytics,\u003c/p\u003e\n \u003cp\u003eMachine Learning (ML),\u003c/p\u003e\n \u003cp\u003eDeep Learning (DL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly intervention, risk identification, proactive care [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eEarly detection rates,\u003c/p\u003e\n \u003cp\u003ereduced ICU occupancy,\u003c/p\u003e\n \u003cp\u003ecost savings [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited real-world evidence [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e], data privacy [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], engagement [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eData standardization [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eethical concerns [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eworkflow fit [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenomics, imaging, XAI, multi-task learning\u003c/p\u003e\n \u003cp\u003eMobile health, Clinical\u003c/p\u003e\n \u003cp\u003eDecision Support\u003c/p\u003e\n \u003cp\u003eSystems, participatory\u003c/p\u003e\n \u003cp\u003eapps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient engagement, shared decision-making [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eUser engagement,\u003c/p\u003e\n \u003cp\u003eRetention [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderrepresentation [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e], user acceptance [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e], workflow integration [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eDigital literacy [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003etransparency [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenomics, imaging, XAI, multi-task learning\u003c/p\u003e\n \u003cp\u003eMultimodal fusion,\u003c/p\u003e\n \u003cp\u003etransfer learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubgroup targeting, high-resolution disease definition [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eDiagnostic accuracy [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eprecision, recall,\u003c/p\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpretability [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], regulatory approval [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e], data heterogeneity [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eData integration [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eValidation [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], scalability [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurveillance, resource allocation, population health machine learning\u003c/p\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003cp\u003e(ML)/Deep Learning\u003c/p\u003e\n \u003cp\u003e(DL) for surveillance,\u003c/p\u003e\n \u003cp\u003eresource allocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutbreak detection [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e], resource optimization [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003ePopulation-level\u003c/p\u003e\n \u003cp\u003eoutcomes, cost savings [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eoperational gains [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData standardization [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e], ethical concerns [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], scalability [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e],\u003c/p\u003e\n \u003cp\u003eInfrastructure [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], data access [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], regulatory\u003c/p\u003e\n \u003cp\u003ebarriers [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Pertinent Technologies Per Each Dimension\u003c/h2\u003e\n \u003cp\u003eThis section describes the reported technologies per each \u0026ldquo;P\u0026rdquo; dimension of the 6P medicine paradigm, where each couple of closely related dimensions are grouped due to common technologies and aligned goals. Some technologies span more than one \u0026ldquo;P\u0026rdquo; dimension even after grouping relevant \u0026ldquo;Ps\u0026rdquo; because some AI applications serve more than one purpose. This observation aligns with the integrated concept of the overall 6P medicine. These patterns are further discussed within the \u0026ldquo;Implementation Analysis\u0026rdquo; section below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePredictive and Preventive Healthcare\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAI methodologies are most frequently applied to predictive and preventive healthcare, including early disease detection, risk stratification, and resource optimization. Machine learning models (e.g., random forests, support vector machines, ensemble methods) and deep learning architectures (e.g., Convolutional Neural Networks, Long Short-Term Memory networks) are widely used for forecasting clinical events, identifying at risk populations, and supporting preventive interventions. Several studies report high accuracy, sensitivity,\u003c/p\u003e\n \u003cp\u003eand specificity in predictive tasks, though external validation and real-world impact are less consistently documented [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. Many studies focused on predictive modeling for early detection, risk assessment, and preventive intervention [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eAI technologies, particularly LLMs and digital twins, have shown promise in predictive and preventive healthcare as described in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. LLMs can analyze vast amounts of medical literature, research papers, and patient records to identify subtle patterns and correlations that might indicate future health risks or disease outbreaks. While digital twins, which are virtual replicas of patients or physiological systems, can simulate disease progression and predict potential health outcomes under different scenarios [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. For example:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eForecast disease progression and optimize treatment regimens in diabetes [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eClinical practice and research of chronic lung diseases [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDrug discovery and clinical trial simulations [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable 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\u003eAI Applications in Predictive and Preventive Healthcare\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample Use Case\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\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\"\u003e\n \u003cp\u003eLLMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalyzing medical images alongside patient history and genomic data [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicts disease progression or treatment response more accurately than unimodal models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Twins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulating the impact of different chemotherapy regimens on a cancer patient\u0026apos;s digital twin [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdentifies the most effective and least toxic treatment option\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003ePersonalized and Precision Medicine\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePersonalized and precision medicine is a central theme, particularly in oncology [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e], chronic disease management [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e], and pharmacotherapy [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]. AI models leverage multimodal data (clinical, genomic, imaging, behavioral) to tailor treatment recommendations, predict therapy response, and optimize dosing. Explainable AI and feature importance methods are increasingly adopted to enhance transparency and clinician trust. Reported performance metrics are often high (Area Under the Curve values\u0026thinsp;\u0026gt;\u0026thinsp;0.9 in some oncology applications), but generalizability and integration into clinical workflows remain challenges.\u003c/p\u003e\n \u003cp\u003eSeveral included studies reported the use of digital twins and LLMs to enable highly personalized patient modeling and care. These systems often leverage advanced LLMs or hybrid architectures to synthesize complex data and provide actionable insights. Integration with existing clinical systems and privacy-preserving data architectures (e.g., federated learning) were highlighted as critical for real-world adoption [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eAI-driven genomic analysis and multi-omics data integration enhance diagnostic accuracy and treatment efficacy [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e highlights some examples of relevant AI technologies for these purposes. AI agents and generative AI can play crucial roles in generating personalized health advice, reminders, and customized educational materials based on individual risk factors and lifestyle [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAI Applications in Personalized and Precision Medicine\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample Use Case\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\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\"\u003e\n \u003cp\u003eAI Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitoring a diabetic patient\u0026apos;s glucose levels and activity patterns [\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicts potential hypoglycemic episodes and suggests adjustments to insulin dosage or dietary intake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenerative AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating personalized educational materials and visual aids [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHelps patients understand their conditions and treatment options better\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eParticipatory and Public Health\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eParticipatory and public health dimensions are less frequently operationalized but are addressed in studies involving patient engagement (e.g., mobile health, shared decision-making), population health surveillance [\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e], and resource allocation [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]. AI-driven tools for public health (e.g., epidemic prediction, resource optimization in Low- and Middle-Income Countries) demonstrate potential for large-scale impact, though evidence of sustained adoption and outcome improvement is limited.\u003c/p\u003e\n \u003cp\u003eLLMs are increasingly used to enhance patient engagement and participation. For example, use of LLMs for generating patient education materials [\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e], simulating virtual patients for training [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e], and facilitating history-taking [\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e]. These applications aim to improve health literacy, empower patients, and foster participatory care models. Visualization of digital twins and conversational interfaces were also explored as means to increase patient involvement in their own care [\u003cspan class=\"CitationRef\"\u003e102\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eAI-driven health assistants and chatbots may facilitate patient engagement and self-management, empowering individuals to take an active role in their healthcare as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. AI-supported shared decision-making tools and personalized health education interventions may have the potential to improve health literacy and patient empowerment [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e105\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e107\u003c/span\u003e]. In public health, AI-powered surveillance systems and population-level analytics optimize resource allocation and enhance the ability to respond to health crises [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAI Applications in Participatory and Public Health\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample Use Case\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\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\"\u003e\n \u003cp\u003eLLM, AI Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProviding clear and concise explanations of lab results to patients [\u003cspan class=\"CitationRef\"\u003e108\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhances patient understanding and engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenerative AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating tailored content for public health campaigns [\u003cspan class=\"CitationRef\"\u003e109\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePromotes healthy behaviors and disease prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Implementation Analysis\u003c/h2\u003e\n \u003cp\u003eThe implementation analysis involved four main perspectives emphasizing AI/ML patterns, cross-dimensional implementations, clinical validation approaches as well as key challenges and success factors as described below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImplementation Across Healthcare Dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePredictive analytics\u003c/strong\u003e is the most commonly addressed dimension. AI systems are used to forecast disease onset, adverse events, and treatment outcomes. Several studies report high accuracy and area under the curve values, but many do not describe their validation methods in detail [\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePersonalization\u003c/strong\u003e is operationalized through individualized treatment recommendations, risk stratification, and tailored interventions based on patient-specific data (such as genomics, imaging, and demographics). This is a central theme in oncology, cardiology, and pain management studies [\u003cspan class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e115\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePreventive care\u003c/strong\u003e is addressed through early detection models, risk prediction tools, and proactive monitoring (including wearables and mobile health). However, fewer studies provide robust evidence of preventive impact in real-world settings [\u003cspan class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eParticipatory approaches\u003c/strong\u003e are less common but are present in mobile health, app-based interventions, and studies involving patient engagement through wearables or co-created public health interventions [\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e120\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision medicine\u003c/strong\u003e is achieved through the integration of multi-modal data (genomics, imaging, electronic health records) and the development of models targeting specific subgroups or disease phenotypes. Some studies emphasize the need for explainable and interpretable models for regulatory approval and clinical adoption [\u003cspan class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e121\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e122\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePublic health\u003c/strong\u003e integration is addressed in studies focusing on surveillance, resource allocation, and population-level interventions. The operationalization of AI at the public health level remains limited compared to individual-level applications [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Validation Approaches\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the context of implementing AI within 6P medicine, clinical validation approaches are essential to ensure reliability and safety across diverse patient populations and care settings [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. This involves rigorous evaluation of AI models through retrospective and prospective clinical studies, focusing on accuracy, generalizability, and real-world applicability [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Validation approaches range from internal cross-validation to external validation on independent datasets. Some studies report real-world deployment and operational metrics (e.g., reduction in scheduling times, improved resource utilization), while others remain at the proof-of-concept stage [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]. Metrics such as accuracy, Area Under the Curve, sensitivity, specificity, and F1 score are commonly used, but reporting is inconsistent.\u003c/p\u003e\n \u003cp\u003eValidation and performance reporting are inconsistent. While several studies report high performance metrics, the lack of standardized validation methods and external validation limits the generalizability of findings [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. There is a need for more rigorous reporting and real-world evaluation, as noted in the reviewed publications.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKey Challenges, Success Factors, and Indicators\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe results above highlight the diverse applications and significant potential of AI in advancing 6P medicine. Addressing the identified challenges through targeted recommendations can further enhance the integration and impact of AI technologies in healthcare. This section elaborates on the reported challenges while providing practical recommendations for better implementation strategies. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e consolidates the reported challenges, mitigation approaches, and success indicators. In addition, the lack of standardized validation, evaluation, and reporting metrics can limit the adoption rate of some promising AI applications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImplementation challenges, mitigation approaches, and success indicators.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChallenge Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvidence-Based\u003c/p\u003e\n \u003cp\u003eSolutions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSuccess Indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdoption Considerations\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\"\u003e\n \u003cp\u003eData Quality \u0026amp;\u003c/p\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData cleaning, feature\u003c/p\u003e\n \u003cp\u003eengineering, local data\u003c/p\u003e\n \u003cp\u003evalidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved model\u003c/p\u003e\n \u003cp\u003eperformance, reduced\u003c/p\u003e\n \u003cp\u003ebias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to representative\u003c/p\u003e\n \u003cp\u003edata, standardization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivacy \u0026amp; Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFederated learning, blockchain, anonymization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompliance, user trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulatory alignment,\u003c/p\u003e\n \u003cp\u003etechnical feasibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpretability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExplainable AI (XAI),\u003c/p\u003e\n \u003cp\u003efeature importance,\u003c/p\u003e\n \u003cp\u003eLocal Interpretable\u003c/p\u003e\n \u003cp\u003eModel-agnostic\u003c/p\u003e\n \u003cp\u003eExplanations (LIME) /\u003c/p\u003e\n \u003cp\u003eSHapley Additive\u003c/p\u003e\n \u003cp\u003eexPlanations (SHAP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinician acceptance,\u003c/p\u003e\n \u003cp\u003etransparency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining, usability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkflow Integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstitutable Medical\u003c/p\u003e\n \u003cp\u003eApplications, Reusable\u003c/p\u003e\n \u003cp\u003eTechnologies (SMART)\u003c/p\u003e\n \u003cp\u003eon Fast Healthcare\u003c/p\u003e\n \u003cp\u003eInteroperability\u003c/p\u003e\n \u003cp\u003eResources (FHIR),\u003c/p\u003e\n \u003cp\u003emodular platforms,\u003c/p\u003e\n \u003cp\u003estakeholder engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperational efficiency,\u003c/p\u003e\n \u003cp\u003euser adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeadership, change\u003c/p\u003e\n \u003cp\u003emanagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical/Legal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy frameworks,\u003c/p\u003e\n \u003cp\u003eauditability, risk\u003c/p\u003e\n \u003cp\u003emanagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompliance, reduced\u003c/p\u003e\n \u003cp\u003eliability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLegal clarity,\u003c/p\u003e\n \u003cp\u003estakeholder buy-in\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource Constraints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCloud computing,\u003c/p\u003e\n \u003cp\u003ecost-effective models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost savings, scalability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvestment,\u003c/p\u003e\n \u003cp\u003einfrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eCross-Cutting Implementation Challenges\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003eCommon technical challenges include data quality and heterogeneity, privacy and security concerns, lack of interpretability, and difficulties integrating AI into existing clinical workflows. Several studies highlight the \u0026ldquo;black box\u0026rdquo; nature of deep learning as a barrier to clinician acceptance. We didn\u0026rsquo;t find any healthcare dimension without at least one AI method and one implementation challenge reported within the reviewed publications. Figure\u0026nbsp;1depicts some of the crossing cutting challenges across the continuum of AI application development, deployment, and maintenance [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e123\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eData integration of heterogeneous sources (electronic health records, imaging, genomics, wearables, Internet of Things) is a recurring challenge and opportunity [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Studies highlight the need for robust data preprocessing, feature engineering, and standardization. In addition, clinical workflow integration is identified as a barrier, including the need for validation in real-world settings and user acceptance [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. Participatory and explainable AI approaches are proposed in several studies to enhance adoption [\u003cspan class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSuccess Factors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eReported success factors include addressing data heterogeneity, ensuring algorithm transparency, mitigating biases, securing regulatory approvals, and fostering multidisciplinary collaboration. These factors hinge on robust clinical evidence, seamless integration into clinical workflows, user trust, and continuous monitoring to adapt AI tools to evolving medical knowledge and patient needs.\u003c/p\u003e\n \u003cp\u003eEvidence-based solutions include the adoption of XAI, federated learning for privacy, robust data governance, stakeholder engagement, and technical frameworks for integration. Success indicators include improved clinical or operational outcomes, user trust, and scalability. Addressing these challenges is crucial for the successful deployment of AI technologies in 6P medicine. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e lists most prominent challenges and corresponding recommendations. Together, these elements shape the pathway for AI to effectively support the transformative goals of 6P medicine.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCross-Cutting Implementation Challenges and Recommendations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChallenge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecommendation\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\"\u003e\n \u003cp\u003eData Quality and Complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsuring high-quality, representative data for AI model training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablish robust data governance frameworks and advanced data management platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivacy and Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData privacy concerns, particularly in personalized medicine and genomic analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdopt comprehensive data protection measures and ensure compliance with regulations such as GDPR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAddressing algorithmic bias, fairness, and transparency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse diverse datasets and develop transparent, explainable algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegration with Existing Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnical interoperability issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplement interoperable platforms supporting HL7 and FHIR standards\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel Interpretability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026quot;Black box\u0026quot; nature of some AI algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDevelop and implement explainable AI techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results of this narrative review underscore the transformative potential of AI in advancing the concept of 6P medicine. The integration of AI technologies such as LLMs, digital twins, AI agents, generative AI, and XAI across various domains of healthcare demonstrates significant improvements in early disease detection, personalized treatment planning, preventive health measures, patient engagement, precision diagnostics, and public health surveillance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAI applications in predictive healthcare, such as early disease detection and forecasting treatment outcomes, are crucial for optimizing patient care and resource utilization. Preventive healthcare benefits from AI-powered health monitoring systems and personalized health recommendations, which can significantly reduce the incidence of chronic diseases and improve population health outcomes. The integration of AI in personalized and precision medicine, particularly through genomic analysis and multi-omics data integration, enhances the accuracy of diagnoses and the efficacy of treatments [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. AI-driven predictive modeling for individual patient responses to therapies ensures tailored interventions, reducing adverse drug reactions and accelerating drug discovery processes. In addition, AI-driven health assistants and chatbots facilitate patient engagement and self-management, empowering individuals to take an active role in their healthcare. AI-supported shared decision-making tools and personalized health education interventions improve health literacy and patient empowerment. In public health, AI-powered surveillance systems and population-level analytics optimize resource allocation and enhance the ability to respond to health crises [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These applications highlight the role of AI to support the various dimensions of 6P medicine [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Aligning to National, Regional, and International Initiatives and Regulations\u003c/h2\u003e\u003cp\u003eThere is an urgent need to establish clear and efficient AI regulations at the national, regional, and international levels to ensure optimal resource allocation, streamlined development processes, and robust implementation of AI projects. On the national level, the United States, Germany, and UK have issued their strategies and initiatives for AI in healthcare [\u003cspan additionalcitationids=\"CR126\" citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. In addition, the U.S. Food and Drug Administration (FDA) has issued its AI/ML Software as a Medical Device Action Plan outlining required steps to regulate AI/ML applications as part of medical products [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e]. The USA AI Risk Management Framework addresses best practices to mitigate relevant risks through the AI lifecycle from design to implementation [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Other countries such as Australia and UK have initiated similar activities to motivate and regulate AI/ML for medical purposes [\u003cspan additionalcitationids=\"CR131\" citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEuropean and international initiatives such as EHDS, the AI Act, and the EU AI Watch, emphasize the importance of fostering AI integration into healthcare practice through appropriate policies to enhance equity, improve care, and ensure the benefits of new technologies [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. The EHDS aims to create a common framework for health data exchange across Europe, facilitating the use of AI in healthcare by ensuring data interoperability and accessibility [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. The AI Act provides a regulatory framework to ensure the safe and ethical deployment of AI technologies, promoting transparency, accountability, and fairness in AI applications [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. EU AI Watch monitors and supports the implementation of AI policies, providing insights and recommendations to enhance AI adoption in various sectors, including healthcare [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e]. Moreover, the Coalition for Health AI (CHAI) and World Health Organization (WHO) have recently released several guiding documents on ethics and governance of AI for health [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese initiatives advocate for multi-stakeholder engagement, increased transparency, and rigorous clinical validation of AI tools. However, more work is still needed to cope with the very dynamic and rapidly growing field of AI/ML in terms of new technologies, opportunities and challenges. Aligning AI applications with these regulatory frameworks can accelerate the adoption of 6P medicine, ensuring that AI-driven innovations are ethically sound, clinically validated, and widely accessible. By adhering to these guidelines, healthcare organizations can leverage AI to improve patient outcomes, enhance operational efficiency, and foster a more equitable healthcare system [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Recommendations for Advancing AI Applications\u003c/h2\u003e\u003cp\u003eBased on the results listed above, several key recommendations can be made to advance AI applications and support the realization of 6P medicine. Both medical, technical, and regulatory parties need to collaborate to address data quality and complexity as well as establishing robust data governance frameworks with standardized protocols for data collection, cleaning, and integration. This approach needs to consider implementing advanced data management platforms capable of handling large volumes of unstructured and multi-omics data to ensure that AI models are trained on high-quality, representative datasets. Additional safeguards should consider enhancing the reliability of AI predictions and to mitigate the risk of biases that can arise from poor data quality.\u003c/p\u003e\u003cp\u003eConsidering the privacy and security in the deployment of AI in healthcare by adopting comprehensive data protection measures, including encryption, anonymization, and secure data storage solutions, must be adopted to safeguard patient information, e.g., federated learning approach [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Ensuring compliance with regulations such as GDPR and HIPAA by prioritizing data minimization and pseudonymization is crucial for maintaining patient trust and legal compliance and to balance the need for data accessibility with the imperative to protect patient privacy.\u003c/p\u003e\u003cp\u003eDeveloping and implementing XAI techniques will enhance model transparency and model interpretability, allowing healthcare professionals to understand and trust AI-driven decisions. Integrating user-friendly visualization tools and interactive dashboards to present AI insights may bridge the gap between complex AI models and practical clinical applications.\u003c/p\u003e\u003cp\u003eThere is a critical and growing need to develop, disseminate, and adopt standardized validation, evaluation, and reporting metrics for AI applications in healthcare to ensure patient safety, clinical efficacy, and regulatory compliance. The complexity of AI models\u0026mdash;especially generative and multimodal systems integrating diverse data types such as text, imaging, and videos\u0026mdash;demands robust, objective, and reproducible frameworks to assess their accuracy, reliability, and clinical utility. Without such standards, inconsistencies in AI performance evaluation limit the ability to compare studies, impede regulatory approval, and reduce clinician and patient trust. Recent efforts highlight frameworks like the METRICS checklist for standardized reporting in generative AI health studies and emerging clinician-informed evaluation protocols that emphasize transparency, fairness, and clinical relevance. These frameworks are vital to bridging the translational gap from AI model development to scalable, safe deployment in clinical workflows, aligning with evolving regulatory requirements such as the EU AI Act and U.S. Executive Orders on AI. Overall, coordinated multidisciplinary collaboration is essential to advance these standards, which will underpin ethical, effective, and equitable AI integration in healthcare.\u003c/p\u003e\u003cp\u003eFinally, all stakeholders (e.g., researchers, developers, clinicians, patients, and policy makers) must put ethical considerations at the forefront of AI implementation in healthcare. Using diverse and representative datasets during model training and update can help mitigate algorithmic bias, ensuring equitable outcomes for all patient groups. Developing transparent and explainable algorithms can promote accountability and trust among stakeholders. Also, integrating AI with existing healthcare systems requires the implementation of interoperable platforms that support interoperability standards such as HL7 FHIR and OHDSI OMOP standards for seamless data exchange. Embracing digital transformation (including AI technologies) necessitates cultural shifts within healthcare organizations to ensure successful integration and optimal utilization of AI tools. These recommendations collectively aim to advance AI applications in healthcare, supporting the realization of 6P medicine and improving patient outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations and Future Research Steps\u003c/h2\u003e\u003cp\u003eThis study has several limitations, including the reliance on narrative review methodology, which may introduce selection bias and limit the comprehensiveness of the findings [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]. Additionally, the rapid evolution of AI technologies necessitates continuous updates to ensure relevance. Future research should focus on the long-term impact of AI applications on patient outcomes and healthcare systems, as well as to develop standardized metrics for evaluating AI performance in clinical settings by conducting systematic reviews and meta-analyses [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe integration of AI technologies in healthcare offers significant promise for advancing 6P medicine. Several challenges and opportunities arise from the changing role of healthcare professionals in AI-augmented healthcare systems. Balancing flexibility with patient safety and ethical standards, while addressing issues such as cost, healthcare outcomes, technology advancements, available resources/infrastructure, and the satisfaction of healthcare workers and patients, is vital for successful implementation. This work emphasizes the need for interdisciplinary collaboration involving clinicians, data scientists, ethicists, policymakers, and patients to address these challenges and harness the full potential of AI in healthcare.\u003c/p\u003e\n\u003cp\u003eSeveral recommendations were provided to ensure the generalizability of predictive models across diverse populations is crucial to avoid biases and enhance the reliability of AI-driven interventions. Also, balancing the benefits of early intervention with the risks of overdiagnosis and unnecessary treatments requires careful consideration of ethical concerns, including patient privacy and security. Finally, integrating AI-driven tools into existing healthcare workflows and decision-making processes is essential for seamless adoption and maximizing the impact of AI on patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e6P Medicine, personalized, predictive, preventive, participatory, precision, and public medicine; AI, Artificial Intelligence; DL, Deep Learning; EHDS, European Health Data Space; \u0026nbsp;EU, European Union; FAIR, Findability, Accessibility, Interoperability, and Reusability; FHIR, Fast Healthcare Interoperability Resource; GDPR, General Data Protection Regulation; \u0026nbsp;HL7, Health Level 7; \u0026nbsp;LLMs, Large Language Models; ML, Machine Learning; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership; WHO, World Health Organization; XAI, Explainable AI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Statement\u003c/h2\u003e\n\u003cp\u003eNo data sets were generated during this study.\u003c/p\u003e\n\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e\n\u003cp\u003eAly Khalifa: Conceptualization, Methodology, Formal analysis, Writing – Original Draft Preparation, Writing – Review \u0026amp; Editing. Rada Hussein: Conceptualization, Methodology, Formal analysis, Writing – Original Draft Preparation, Writing – Review \u0026amp; Editing.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThe authors declare that this research was conducted without any specific grant from funding agencies.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The last author is a member of the JoMs editorial board.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlonso SG, de la Torre D\u0026iacute;ez I, Zapira\u0026iacute;n BG. 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NIST 2021.\u003c/li\u003e\n \u003cli\u003ePalaniappan K, Lin EYT, Vogel S. Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector. Healthcare 2024;12:562. https://doi.org/10.3390/healthcare12050562.\u003c/li\u003e\n \u003cli\u003eHobson J. Australian roadmap for artificial intelligence in healthcare to be launched at AI.Care. AIDH 2023. https://digitalhealth.org.au/blog/australian-roadmap-for-artificial-intelligence-in-healthcare-to-be-launched-at-ai-care/ (accessed April 22, 2025).\u003c/li\u003e\n \u003cli\u003eImpact of AI on the regulation of medical products. 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BMC Med 2019;17:195. https://doi.org/10.1186/s12916-019-1426-2.\u003c/li\u003e\n \u003cli\u003eLekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 2025;388:e081554. https://doi.org/10.1136/bmj-2024-081554.\u003c/li\u003e\n \u003cli\u003eThe Future of AI in Healthcare \u0026ndash; 2025. SSC Blue Prism n.d. https://www.blueprism.com/resources/blog/the-future-of-ai-in-healthcare/ (accessed April 22, 2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"6P Medicine, Artificial Intelligence, Personalized Medicine","lastPublishedDoi":"10.21203/rs.3.rs-7402571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7402571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) is poised to transform healthcare through the lens of 6P medicine: personalized, predictive, preventive, participatory, precision, and public health. This review explores how AI technologies contribute to these six crucial dimensions, offering powerful solutions for enhanced early disease detection, tailored treatment protocols, proactive preventive health measures, and significantly improved patient engagement in their care journey.\u003c/p\u003e\u003cp\u003eOur findings highlight AI's immense potential in revolutionizing diagnostics by enabling more accurate and rapid analyses, as well as strengthening public health surveillance capabilities. However, successful and responsible implementation hinges on rigorously addressing crucial cross-cutting challenges such as ensuring data quality, safeguarding patient privacy, navigating complex ethical considerations, seamless system integration, and fostering model interpretability. Aligning AI applications with evolving regulatory frameworks, such as the European Health Data Space (EHDS) and the AI Act, is absolutely essential to accelerate the ethical and effective adoption of 6P medicine. Ultimately, interdisciplinary collaboration among clinicians, technologists, and policymakers is key to unlocking AI's full power to improve patient outcomes and overall healthcare operational efficiency.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 08:13:18","doi":"10.21203/rs.3.rs-7402571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T03:28:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T19:59:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267543010129700308814035651964906628306","date":"2026-01-22T21:27:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75656074852405747084383426607722604887","date":"2025-11-19T18:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17126956141603304596289259600193865023","date":"2025-10-05T13:39:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T16:38:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294619136400040779552342002160017340585","date":"2025-09-01T23:00:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297906408522722987833540211079278183626","date":"2025-08-27T02:50:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T02:47:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-27T01:42:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-26T05:31:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Medical Systems","date":"2025-08-18T19:41:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b69e25dd-fc2d-486e-b982-c4becb919a01","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T02:39:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 08:13:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7402571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7402571","identity":"rs-7402571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}