POLICY AND PRACTICE REVIEWS article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1496945
Population Health Management Fit Lifecycles in Analytics
Provisionally accepted- Institute of biomedical sciences, London, United Kingdom
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Abstract: Introduction "Population Health Management (PHM), Fit Lifecycles in Analytics" examines the policy and practice of AI-driven methodologies to enhance public health and patient safety for Human Phenotype Ontology (HPO). It aims for personalised healthcare delivery through the risk stratification of predictors and pathology segmentation for intercepts. This manuscript introduces the Five-Point PHM strategy as a mission for public trust and governance. Scientific and technological advancements address public genomic inclusiveness and engage biobanks and life sciences for national public health and patient safety oversight. Methods The study assesses genome and socio-environmental health factor variables that segment and image disease through stakeholder engagement in real-world settings such as HPO neighbourhood trials. It assesses practices for data training, emphasising data alliances, scientific themes, and data structure. The manuscript evaluates data pre-processing and prioritises open-source frameworks, ensuring data balance and bias mitigation. Actions The recommendation for a PHM infrastructure assures personal classification under national authority with a comparative analysis of AI architectures, highlighting trade-offs in AI modes for HPO. Structural and continuous control monitoring, explainability, and model performance metrics are emphasised. The actions transform the vision and language for HPO, advocating for a national Generative classification for genome predictor pre-eXam and eXam intercepts. Actions for guardrails and ethics address a secure and safe national programme. Discussion The governance of fit lifecycles in analytics discusses improvement with research science integration and accountability for HPO as primary care. The PHM mission and ten-year infrastructure plan addresses implementation challenges through Government principles for an adoption mission with AISI/AIDRS authority. Diligent PHM through HPO policy action GPT-5, with Federated data learning and Quantum Computing as emerging technologies that synergise for BM of predictors and intercepts. Conclusion The manuscript concludes with the potential of the proposed PHM mission to support the UK AI Action Plan and principles outlined in the UK Government AI Playbook. By integrating research science for HPO evidence-based primary care practice, this paper drives public health and patient safety progress for national wellbeing and growth. The study advocates for the ethical and secure implementation of AI-driven PHM with public science and technology trustworthiness.
Keywords: AI Digital Regulation Service AISI, Population Health Management (PHM), Human Phenotype Ontology (HPO), Biological Modelling (BM), Classify Predictors and Intercepts
Received: 16 Sep 2024; Accepted: 17 Mar 2025.
Copyright: © 2025 Henry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: James Andrew Henry, Institute of biomedical sciences, London, United Kingdom
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