GENOMICS
Intelligent workflow for precision population health management
To fully realise the transformative power of data- driven services and personalised medicine, they require structured integration of digital and genomic innovations that network and account cultural, and ethical implications, explains James Henry.
Decades of research underscore the necessity of a strong commitment to successfully integrate artificial intelligence (AI) into Biological Modelling and Population Health Management.1
Society
must determine its expectations for AI, including a culture of continuous improvement alongside the cultivation of metrics and fairness while ensuring privacy and security for reliable predictor and intercept classification in primary and social care reform.2
Furthermore, population
management emphasises transitioning from a collaborative approach to the stewardship of data as a standard, aiming to fully realise AI’s potential in healthcare through data science themes within Human Phenotype Ontology domains.3 Globally, nations are uniting on wellbeing to leverage the transformative power of data-driven services in sustaining personalised medicine and establishing goals for the structured integration of genomics. This integration
must network and consider cultural and ethical implications.4
This article builds
upon the authors’ manuscript, ‘Culture Intelligent Workflow, Structure and Steps’, published in Frontiers in Artificial Intelligence.5
This paper and the current
article frame Biological Modelling within a Population Health Ecosystem, where subsequent program manuscripts further segment pathology for improved global health practices.6-10
Introduction ‘Culture Intelligent Workflow, Structure and Steps’, is the first in a series of six papers intended to outline an ecosystem approach to predicting and intercepting mental and physical pathologies, recognising the significant benefits that personalised medicine stands to gain from AI integration.5
Drawing on population
health management principles, the original paper’s figures and text aimed to introduce AI reform in ‘eXaminations’, a vision from 2023, as presented in Figure 1. Figure 1 provides an initial conceptualisation of an ‘eXamination’ (X=Gen AI) workflow. Five subsequent peer-reviewed manuscripts recommended for publication in the Frontiers journal in 2025 now classify genomics-based predictive health pre- eXams and precision care eXams for nations’ to consider biological modelling authorisation of predictors and intercepts in primary care networks.6-10
Notably, the
A standard collaborative approach to stewarding is necessary to fully achieve AI’s potential in the area of genomic innovations and personalised medicine.
WWW.PATHOLOGYINPRACTICE.COM JUNE 2025
‘pre-post eXam’ concept is visualised behind the left eye in Figure 6 (looking out) from the brain’s temporal lobe for perception. This ecosystem genomic approach aims to predict disease [Pre- eXam] and deliver precision care [eXam] for improved patient outcomes [Post- eXams], transitioning from research to routine Gen AI classifiers and biological modelling metrics.
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AdobeStock / Nongluk
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