Hussein, Rada

The integration of artificial intelligence (AI) into healthcare systems is central to the realization of 6P Medicine that emphasizes Predictive, Preventive, Personalized, Participatory, Precision-oriented, and Public-centered care. While several conceptual AI adoption models have been proposed, few provide prescriptive guidance for real-world validation across technical, sociotechnical, and ethical dimensions. This paper introduces a comprehensive validation framework for the Scalable Multi-Layer AI Adoption Model for 6P Medicine. The framework aligns architectural prerequisites, regulatory governance, and continuous lifecycle monitoring with the six interdependent layers of the model. Validation is addressed across data integrity, model robustness, clinical efficacy, human-AI collaboration, scalability, and ethical governance, drawing on FDA Good Machine Learning Practice (GMLP) principles and WHO regulatory considerations. The resulting framework intends to support continuous, real-world validation, positioning AI as a trustworthy, scalable, and ethically governed enabler of 6P Medicine.

The European Health Data Space (EHDS) aims to enable both the primary use of health data for direct patient care (EHDS1) and the secondary use of data for research, innovation, and public health (EHDS2). While each pillar is supported by distinct infrastructures—MyHealth@EU for clinical continuity and HealthData@EU for trusted reuse—the lack of seamless integration between them poses significant technical, governance, and legal challenges. Existing standards such as HL7 CDA, HL7 FHIR, OHDSI OMOP, CDISC, EEHRxF, and HealthDCAT-AP, together with IHE integration profiles including XDS, XCA, XCPD, ATNA, and BPPC, provide a robust foundation but remain fragmented across domains. This paper introduces the EHDS Data Continuum, a proposed IHE integration profile designed to bridge EHDS1 and EHDS2 by harmonizing interoperability, data quality, privacy, and governance requirements across the full data lifecycle. By aligning primary and secondary use within a coherent technical and policy framework, the EHDS Data Continuum supports the realization of a trustworthy, interoperable, and citizen-centered European Health Data Space.

Developing a scalable multi-layer AI adoption model for 6P medicine (Predictive, Preventive, Personalized, Participatory, Precision-oriented, and Public-centered) requires careful consideration of computational infrastructure, data processing and integration, healthcare-specific requirements, security and privacy, performance optimization, and system interoperability. The described multi-layer architecture in this work provides a flexible conceptual model to accommodate the diverse needs of AI implementation in different healthcare domains while maintaining scalability, security, governance, and efficiency.

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.

Abstract Background Health care has evolved to support the involvement of individuals in decision making by, for example, using mobile apps and wearables that may help empower people to actively participate in their treatment and health monitoring. While the term “participatory health informatics” (PHI) has emerged in literature to describe these activities, along with the use of social media for health purposes, the scope of the research field of PHI is not yet well defined. Objective This article proposes a preliminary definition of PHI and defines the scope of the field. Methods We used an adapted Delphi study design to gain consensus from participants on a definition developed from a previous review of literature. From the literature we derived a set of attributes describing PHI as comprising 18 characteristics, 14 aims, and 4 relations. We invited researchers, health professionals, and health informaticians to score these characteristics and aims of PHI and their relations to other fields over three survey rounds. In the first round participants were able to offer additional attributes for voting. Results The first round had 44 participants, with 28 participants participating in all three rounds. These 28 participants were gender-balanced and comprised participants from industry, academia, and health sectors from all continents. Consensus was reached on 16 characteristics, 9 aims, and 6 related fields. Discussion The consensus reached on attributes of PHI describe PHI as a multidisciplinary field that uses information technology and delivers tools with a focus on individual-centered care. It studies various effects of the use of such tools and technology. Its aims address the individuals in the role of patients, but also the health of a society as a whole. There are relationships to the fields of health informatics, digital health, medical informatics, and consumer health informatics. Conclusion We have proposed a preliminary definition, aims, and relationships of PHI based on literature and expert consensus. These can begin to be used to support development of research priorities and outcomes measurements.