Citations

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.

The National Institute of Health (NIH) Genetic Testing Registry (GTR) provides a variety of information about genetic tests such as relevant methods, conditions, and performing laboratories. This study mapped a subset of GTR data to the newly developed HL7®-FHIR® Genomic Study resource. Using open-source tools, a web application was developed to implement data mapping and provides many GTR test records as Genomic Study resources. The developed system demonstrates the feasibility of using open-source tools and the FHIR Genomic Study resource to represent publicly available genetic testing information. This study validates the overall design of the Genomic Study resource and proposes two enhancements to support additional data elements.