Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 24, 2025
Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026
Date Accepted: Jan 28, 2026
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI Triage in Primary Care: Towards Safer and More Equitable Real-World Evidence
ABSTRACT
AI triage in GP practices is developing rapidly within the primary care digital transformation, promising efficiency gains, and safety standardisation in overwhelmed primary care systems. However, current evidence is drawn from retrospective validations, emergency settings, or vignettes, with scant evaluation of real-world outcomes and almost no equity-stratified safety data, despite known disparities across age, ethnicity, language, and deprivation. From a sociotechnical standpoint i.e., focusing on the fit between people, tasks, technology, and organisational context, key risks stem not only from algorithmic bias and under-triage but also from human factors, workflow misalignment, governance gaps, and lack of post-deployment monitoring. We argue that ensuring AI triage is safe and equitable requires real-world evaluations in primary care settings, equity-focused performance reporting using theoretically informed frameworks, and rigorous post-market surveillance. Without these, deployment may widen existing health inequalities rather than moderate them.
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