JMIR Publications

ABSTRACT

AI triage in general practice (GP) 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, which considers the fit between people, tasks, technology, and organisational context, risks arise not only from algorithmic bias and under-triage but also from human factors, workflow misalignment, governance gaps, and inadequate 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.