Work

Patient safety and health equity of AI-enabled triage tools in primary care: a systematic review

Year: 2026

Type: review

Abstract: Objectives This review synthesised real-world patient safety evidence for artificial intelligence (AI)-enabled triage tools in primary care and mapped whether safety has been examined across sociodemographic groups.Methods PubMed, Embase, Web of Science and Scopus were searched (January 2015–November 2025) with citation searching. Prospective studies evaluating AI triage tools in real-world primary care settings were eligible. Quality was assessed using the Mixed Methods Appraisal Tool; equity dimensions were mapped using PROGRESS-Plus. Random-effects meta-analysis pooled comparable proportion-based outcomes.Results Four studies were included; all evaluated rule-based or decision-tree tools as no machine learning or generative AI system met the eligibility criteria. Safe triage rates ranged from 81% to 97.6%, under-triage rates ranged from 2% to 17.2% and over-triage rates ranged from 8% to 29%. An exploratory random-effects meta-analysis of three studies (n=1303) gave a pooled safe triage proportion of 0.908 (95% CI 0.319 to 0.995) with substantial heterogeneity (I²=97.0%); given incompatible outcome definitions across studies, these estimates are hypothesis-generating rather than summary measures. No study analysed differential triage accuracy by any sociodemographic factor despite available demographic data.Discussion Safety outcomes were confined to technical performance proxies rather than direct measures of harm, and the absence of equity-stratified analysis may reflect a systemic evidence gap.Conclusion To our knowledge, this is the first systematic review to examine both real-world patient safety and equity of AI-enabled triage in primary care; findings may not extend to machine learning or generative AI systems. A Safety-Equity Evidence Mapping (SEEM) matrix and an evidence maturity framing are proposed to characterise this evidence and inform emerging consensus on evidence thresholds.PROSPERO registration number CRD420251179046.

Source: BMJ Health & Care Informatics

DOI: 10.1136/bmjhci-2026-102427

Authors: Aymn Alamoudi, Yanying Gai, Salwa S Zghebi, Evangelos Kontopantelis, Benjamin C Brown

Institutions: University of Manchester, Jazan University, National University of Singapore

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,207