Population health management through human phenotype ontology with policy for ecosystem improvement
AimThe manuscript “Population Health Management (PHM) Human Phenotype Ontology (HPO) Policy for Ecosystem Improvement” steward safe science and secure technology in medical reform. The digital HPO policy advances Biological Modelling (BM) capacity and capability in a series of fair classifications....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1496937/full |
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| author | James Andrew Henry |
| author_facet | James Andrew Henry |
| author_sort | James Andrew Henry |
| collection | DOAJ |
| description | AimThe manuscript “Population Health Management (PHM) Human Phenotype Ontology (HPO) Policy for Ecosystem Improvement” steward safe science and secure technology in medical reform. The digital HPO policy advances Biological Modelling (BM) capacity and capability in a series of fair classifications. Public trust in the PHM of HPO is a vision of public health and patient safety, with a primary goal of socioeconomic success sustained by citizen privacy and trust within an ecosystem of predictor equality and intercept parity.MethodScience and technology security evaluation, resource allocation, and appropriate regulation are essential for establishing a solid foundation in a safe ecosystem. The AI Security Institute collaborates with higher experts to assess BM cybersecurity and privacy. Within this ecosystem, resources are allocated to the Genomic Medical Sciences Cluster and AI metrics that support safe HPO transformations. These efforts ensure that AI digital regulation acts as a service appropriate to steward progressive PHM.RecommendationsThe manuscript presents a five-point mission for the effective management of population health. A comprehensive national policy for phenotype ontology with Higher Expert Medical Science Safety stewards reform across sectors. It emphasizes developing genomic predictors and intercepts, authorizing predictive health pre-eXams and precise care eXams, adopting Generative Artificial Intelligence classifications, and expanding the PHM ecosystem in benchmark reforms.DiscussionDiscussions explore medical reform focusing on public health and patient safety. The nation's safe space expansions with continual improvements include stewards developing, authorizing, and adopting digital BM twins. The manuscript addresses international classifications where the global development of PHM enables nations to choose what to authorize for BM points of need. These efforts promote channels for adopting HPO uniformity, transforming research findings into routine phenotypical primary care practices.ConclusionThis manuscript charts the UK's and global PHM's ecosystem expansion, designing HPO policies that steward the modeling of biology in personal classifications. It develops secure, safe, fair, and explainable BM for public trust in authorized classifiers and promotes informed choices regarding what nations and individuals adopt in a cooperative PHM progression. Championing equitable classifications in a robust ecosystem sustains advancements in population health outcomes for economic growth and public health betterment. |
| format | Article |
| id | doaj-art-0ffbfb09157a4c5b81d9c51dd3cb2041 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-0ffbfb09157a4c5b81d9c51dd3cb20412025-08-20T14:27:20ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.14969371496937Population health management through human phenotype ontology with policy for ecosystem improvementJames Andrew HenryAimThe manuscript “Population Health Management (PHM) Human Phenotype Ontology (HPO) Policy for Ecosystem Improvement” steward safe science and secure technology in medical reform. The digital HPO policy advances Biological Modelling (BM) capacity and capability in a series of fair classifications. Public trust in the PHM of HPO is a vision of public health and patient safety, with a primary goal of socioeconomic success sustained by citizen privacy and trust within an ecosystem of predictor equality and intercept parity.MethodScience and technology security evaluation, resource allocation, and appropriate regulation are essential for establishing a solid foundation in a safe ecosystem. The AI Security Institute collaborates with higher experts to assess BM cybersecurity and privacy. Within this ecosystem, resources are allocated to the Genomic Medical Sciences Cluster and AI metrics that support safe HPO transformations. These efforts ensure that AI digital regulation acts as a service appropriate to steward progressive PHM.RecommendationsThe manuscript presents a five-point mission for the effective management of population health. A comprehensive national policy for phenotype ontology with Higher Expert Medical Science Safety stewards reform across sectors. It emphasizes developing genomic predictors and intercepts, authorizing predictive health pre-eXams and precise care eXams, adopting Generative Artificial Intelligence classifications, and expanding the PHM ecosystem in benchmark reforms.DiscussionDiscussions explore medical reform focusing on public health and patient safety. The nation's safe space expansions with continual improvements include stewards developing, authorizing, and adopting digital BM twins. The manuscript addresses international classifications where the global development of PHM enables nations to choose what to authorize for BM points of need. These efforts promote channels for adopting HPO uniformity, transforming research findings into routine phenotypical primary care practices.ConclusionThis manuscript charts the UK's and global PHM's ecosystem expansion, designing HPO policies that steward the modeling of biology in personal classifications. It develops secure, safe, fair, and explainable BM for public trust in authorized classifiers and promotes informed choices regarding what nations and individuals adopt in a cooperative PHM progression. Championing equitable classifications in a robust ecosystem sustains advancements in population health outcomes for economic growth and public health betterment.https://www.frontiersin.org/articles/10.3389/frai.2025.1496937/fullpopulation health managementsafetysecuritypredictive healthprecision careclassifications |
| spellingShingle | James Andrew Henry Population health management through human phenotype ontology with policy for ecosystem improvement Frontiers in Artificial Intelligence population health management safety security predictive health precision care classifications |
| title | Population health management through human phenotype ontology with policy for ecosystem improvement |
| title_full | Population health management through human phenotype ontology with policy for ecosystem improvement |
| title_fullStr | Population health management through human phenotype ontology with policy for ecosystem improvement |
| title_full_unstemmed | Population health management through human phenotype ontology with policy for ecosystem improvement |
| title_short | Population health management through human phenotype ontology with policy for ecosystem improvement |
| title_sort | population health management through human phenotype ontology with policy for ecosystem improvement |
| topic | population health management safety security predictive health precision care classifications |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1496937/full |
| work_keys_str_mv | AT jamesandrewhenry populationhealthmanagementthroughhumanphenotypeontologywithpolicyforecosystemimprovement |