Henry James Andrew
Institute of Biomedical Sciences, London, United Kingdom.
Front Artif Intell. 2025 Aug 13;8:1496935. doi: 10.3389/frai.2025.1496935. eCollection 2025.
Population Health Management (PHM), through strategic integration of the Human Phenotype Ontology (HPO), emphasises the responsible use of digital infrastructure and comprehensive genomic data to promote good health and wellbeing. The UK seeks to steward medical science and phenotype practices in primary care settings with technical approaches for developing a national Biological Modelling (BM) ecosystem. By recognising diverse global healthcare systems, this manuscript offers a means for nations to adapt their HPO operational deployment for global PHM harmony.
The methodological approach incorporates primary care services and funding assessments to address digital infrastructure needs, ensuring secure national data access. Evaluations include ISO standards, systems thinking, alignment of UK infrastructure with informatics requirements, and AI norms within the ecosystem. Specific use cases for genomic predictive health pre-eXams and precise care eXams are assessed, alongside strategies for bias mitigation to ensure fairness in AI-driven classifications.
The manuscript advocates for establishing local agile ecosystem groups for PHM, regional Higher Expert Medical Science Safety (HEMSS) stewardship, national HPO value-based care models, and integrating global PHM general intelligence. Real-world AI and clinical practice comparisons are emphasised for validating digital twin personalised BM via Gen AI in the HPO transformation ecosystem.
Federated Learning and GPT-5 technologies advance international PHM by supporting HPO transformations. Standard personalised BM learning addresses intranational HPO variances, requiring individual classifications. National HPO roadmaps prioritise inclusiveness and stakeholder engagement, supported by informed consent and quantum intelligence. Ethical and equitable HPO deployment demands proactive stewardship and national cooperation to address limitations and ensure robust classifications.
Unified, data-driven HPO transformation utilising advanced AI and genomics is essential for personalised healthcare delivery. Rigorous assessments, ethical considerations, and global collaboration enable impactful implementation. National PHM ecosystems guided by HPO transformation in classifications sustain healthcare, advancing patient outcomes through responsible innovation and informed policy development.
通过人类表型本体论（HPO）的战略整合，人群健康管理（PHM）强调合理利用数字基础设施和全面的基因组数据来促进健康和福祉。英国试图通过技术手段在初级保健环境中管理医学科学和表型实践，以发展国家生物建模（BM）生态系统。通过认识到全球不同的医疗保健系统，本文提供了一种方法，使各国能够调整其HPO业务部署，以实现全球PHM的协调一致。
该方法包括初级保健服务和资金评估，以满足数字基础设施需求，确保国家数据的安全访问。评估包括ISO标准、系统思维、英国基础设施与信息学要求的一致性，以及生态系统内的人工智能规范。评估了基因组预测健康预检查和精准护理检查的具体用例，以及减轻偏差的策略，以确保人工智能驱动分类中的公平性。
本文主张为PHM建立本地敏捷生态系统小组、区域高级专家医学科学安全（HEMSS）管理、国家基于HPO的价值医疗模式，并整合全球PHM通用智能。强调通过在HPO转型生态系统中通过生成式人工智能验证数字孪生个性化BM，进行现实世界的人工智能与临床实践比较。
联邦学习和GPT-5技术通过支持HPO转型推动国际PHM发展。标准的个性化BM学习解决了国内HPO的差异，需要进行个体分类。国家HPO路线图将包容性和利益相关者参与作为优先事项，得到知情同意和量子智能的支持。符合道德和公平的HPO部署需要积极的管理和国家合作，以解决局限性并确保可靠的分类。
利用先进的人工智能和基因组学进行统一的数据驱动的HPO转型对于个性化医疗服务至关重要。严格的评估、道德考量和全球合作能够实现有影响力的实施。以HPO分类转型为指导的国家PHM生态系统维持医疗保健，通过负责任的创新和明智的政策制定推动患者预后改善。
