GENOMICS


A unified HPO guide and policy across the UN – as instanced for the UK, and US – is crucial for training high-quality data-driven predictors and intercepts


For an exploration of these concepts, readers can refer to Paper 3 in the series, Population Health Management: Human Phenotype Ontology Policy for Ecosystem Improvement.


n HIMSS require HEMSS stewardship In figure 3 the findings from sections 1-4 are detailed to realise variabilities in mega processes across multiple sectors that benefit from the PHM of HPO using BM. From figure 3, primary care services and specialised hospitals integrate multi- omics and biopharma through multiple processes that align biobanks and bio life sciences. Findings include: Section 1: Primary care HIMSS is a voluntary service with no national science or technology oversight. Ideal findings need enhanced capacity and capabilities under national authorities. Section 2: Specialised hospitals lack a comprehensive ecosystem strategy. Ideal findings enhance the PHM adoption mission with AI guidelines and HPO policy.


Section 3: Genomics use HIMSS, but there is a need to address national cybersecurity, privacy and informed consent. Then, an ideal ecosystem could align classifications. Section 4: Biopharmaceuticals lack a national standard in digital twin approaches through the AIDRS which engineers precise public health, patient safety and equity intercepts.


n UK AI authorities are developing AI Digital Regulation Services (AIDRS) • The AIDRS commission should own the HPO policy in a PHM strategy.


• AIDRS/AISI research, evaluations, regulation and oversight of HPO AI technologies ensure compliance with legal and ethical standards.


• AIDRS/AIRR cluster AI in healthcare with fit-for-purpose BM in HPO. AI Safety Institute (AISI) • Aims to provide an understanding of AI risks, conduct technology research, and inform policy development, like HPO policy with the AIDRS


• Ensure the secure and safe deployment of AI-driven HPO actions with data privacy and security provisions. AI Research Resource (AIRR) • Focuses on expanding AI research capacity, supporting HPO mission- oriented clusters, and enabling AI model training.


• Provides the infrastructure for developing and validating HPO-based AI models for UK Sovereign compute systems.


n UK Sovereign Compute vs. US Federal Compute


• The UK is developing sovereign computing systems, while the US Federal system also seeks to synergise genomics, biobanks, and life-science opportunities.


• The UK potentiates a PHM infrastructure with US commercial services for HPO predictors and intercepts stewarded for adoption in classifications.


• The UK and the US are aligning Federal with sovereign, state with Integrated Care Board stewarding Gen AI HPO classifications.


• The US court ruling against the FDA regarding diagnostic test regulation27 highlights the need for HPO policy and HEMSS to address gaps.


• The US court ruling impacts the UK who are significant proponents of ISO 15189 which have incorporated shall statements in their 2022 review.


• The UK and US memorandum of understanding on AI expands the ecosystem as PHM impacts social determinants, and the AISI evaluations expand AIRR clustering.


Recommendations Building upon figure 3, where the flower represents decentralised training of biological models (BM) through federated learning, this section outlines key recommendations. An AIDRS and Genomics AI Network authority for HPO policy should align the ICS assessments as overseen by the CQC. HEMSS stewardship of classifications is progressive through the AIDRS/Genomics AI network with Medical and Science experts committed to progressive PHM. This will be achieved through evaluations conducted by the AISI and the AIRR, using Generative AI clustering. Figure 3 Section A-D illustrates how HEMSS classifiers can develop personalised BM with outcome- based metrics to support the adoption of probability-based solutions.


n Recommendation A:


UK and US national authority for HEMSS stewardship


Government authorities in the DHSC and WWW.PATHOLOGYINPRACTICE.COM MAY 2025


DIST may consider HEMSS stewardship in a vision for genomics, life science, and biobanks that mission UN SDGs with: • Universal health and wellbeing: Through inclusive public-private engagement and equitable access to both UK sovereign and relevant international (including US federal) healthcare technologies.


• Sustainable economic growth: By supporting advanced innovation and creating employment opportunities within the UK health technology sector.


• National providers and global partnerships: Through collaborative stewardship and adherence to standardised data within an ethical AI ecosystem.


n Recommendation B1: PHM mission for HEMSS stewardship implement and encompass


• Standardised classifications: Utilising genomic predictive pre-eXams and precise eXam intercepts to ensure data consistency and national comparability.


• Ethical public inclusiveness: Implementing national identifiers and DNA passports with robust informed consent protocols to safeguard individual rights with an opt in.


• Engaging stakeholders: Forming public-private partnerships with use of commercial expertise to accelerate adoption.


• Transparent stewardship: Implementing robust data training, AI architecture, and assurance mechanisms, as illustrated in figures 1 and 2, to ensure accountability.


• Continuous improvement: Establishing mechanisms for ongoing evaluation and refinement of classifications based on their adoption and impact.


n Recommendation B2:


National AIDRS HPO policy align with the AISI – AIRR


To facilitate the implementation of a robust HPO ecosystem while awaiting capacity and capability development. Therefore, HEMSS principles should be aligned with the functions of the AIDRS, AISI, and AIRR: • AIDRS alignment: As the owner of the HPO policy, the AIDRS, GMS and RCPATH (primary stakeholder of genomic phases and workflow) may establish HEMSS stewardship of classifications in AI-driven healthcare solutions. The AIDRS ensure that AI technologies employed in PHM adhere to robust, high-quality data governance standards, incorporating ethical AI principles and data privacy requirements.


• AISI alignment: The AISI should conduct research evaluations, in collaboration


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