GENOMICS


GIPES UPSTREAM


Genomic 1st generation


2nd generation Epigenomics Imagery


pathology 1st generation


2nd generation Biological OMICs Transcriptomics Proteomics


Environmental symptoms


Culture intelligent workflow, structure and steps to manage system integration Fig 1. Model workflow.


The intelligent workplace in pathology: a safe space To understand the technological foundation of architecture and algorithm workflow, see Figure 2 as the workflow structure illustrates the intelligent workplace algorithms. Multidisciplinary team approaches align genomic flow from upstream inputs, which extend to health determinant factors and imaging through neural networks and transformation cluster search domains.11 An AI research resource expresses interest in using UK large-scale computes to support practitioners in their diagnostic, prognostic, and pathology workflow management tasks as anticipated changes in advanced AI hybrid advancements blend traditional rule- based systems with machine learning.12 Gen AI comparative analytics uncover patterns missed in conventional methods, as phenotype built from real-world data, streamline efficiencies in tackling chronic diseases, which account for 90% of healthcare spending, and drive safer outcomes.13


The AI Digital Regulation


Service develops and authorises a ‘safe space’ within an adaptive mission for science and technology.14


The Genomic AI network with a Higher Expert Medical Science Safety (HEMSS) stewarding approach spans biobanks and life sciences, envisioning a comprehensive


safety ecosystem for AI applications in these critical areas of biological modelling.6-10


This controlled and ethical


environment for developing and testing AI tools has been devised through manuscripts promoting the broader adoption of safe practices derived from trusted research.


Workflow culture and stewardship


Establishing a supportive ecosystem culture is crucial for successfully adopting AI in pathology prediction. Figure 3 shows how this can be achieved by cultivating open dialogue with the public, engagement with stakeholders, and collaboration among clinical practitioners, scientists, IT professionals, and allied health workers. This dialogue addresses concerns regarding AI interpretations, such as the ‘black box’ challenge and the need for explainable algorithms, promoting transparency and building public confidence in AI-driven solutions.15,16 The interconnectedness of cultural aspects, workplace assurances (ensuring a reliable and well-maintained ecosystem), and workstream (adopting authorised classification processes) are methodical to format evidence and probability- based reforms in population health management.6-10


As detailed by the


Pathology’s contribution to population health extends beyond individual diagnoses. AI-driven analysis of pathology data can identify trends, predict disease, enable precision care, and inform public health policies


26


author, this necessitates the stewarding of Biological Modelling Workflow Culture towards a classification ecosystem which is autonomous on predictors (genomic pre-eXams) and alignment of intercepts (eXams).6-10


Globally, we establish a


shared understanding and responsibility for biological modelling classification within an AI health ecosystem that cultivates accuracy and consistency, with ethical considerations paramount in these crucial personalised and autonomous processes.


Population health management: a workflow step-by-step design Building societal trust for population health management design of the future ecosystem relies on robust metric validation (rigorous testing and performance evaluation of AI models) and clear communication of AI capabilities and limitations, facilitating widespread acceptance and effective use of research (secure platforms and privacy, with data analysis and collaboration).17


Figure 4


shows populace health step-by-step as the national action plan advances AI genomics and The Royal College of Pathologists’ cycle.18-19


Note: This


approach is like the author’s original work, as echoed in ISO 15189:2022 Annex A for AI governance, training and assurance,20 as submitted to an NHSE Governance lead for an end-to-end workflow in 2014 [Available on request]. In moving forward over the last decade, the author continued with ecosystem design for Biological Modelling with Human Phenotype Ontology vocabulary and computing, demonstrating continuous development and refinement as postulated, visualised, and realised in manuscripts.6-10


These works developed from steps into an JUNE 2025 WWW.PATHOLOGYINPRACTICE.COM AI


POPULATION HEALTH MANAGEMENT


PERSONALISED SYSTEM PRACTICE


Conformance or Compliance


Significance Prediction Precision


Prescriptive


WORKPLACE ALGORITHM


Workforce and neighbourhoods Cardiocoagulum Pilots in our future health


EXAM WORKSTREAM Biological or Social eXamination


Pre – eXamination social economic burden


Personal or neighbourhood intervention


Post – eXamination Wellbeing and Burden resolved


© copyright James Henry


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