{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nDiscussion: 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.\n\nConclusion: 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.\n\nPROSPERO registration number: CRD420251179046.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK",
    "Department of Public Health, Health Informatics Program, School of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia",
    "Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK",
    "Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore",
    "Centre for Research in Health Systems Performance, National University of Singapore, Singapore"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Aymn Alamoudi",
      "orcid": "0000-0001-8101-8218"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanying Gai",
      "orcid": "0009-0000-2224-4952"
    },
    {
      "affs": [
        0
      ],
      "name": "Salwa S Zghebi",
      "orcid": "0000-0002-7978-1094"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Evangelos Kontopantelis",
      "orcid": "0000-0001-6450-5815"
    },
    {
      "affs": [
        0
      ],
      "name": "Benjamin C Brown",
      "orcid": "0000-0001-9975-4782"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/csnxjnx9b02gk.bib",
  "cited_by": 0,
  "doi": "10.1136/bmjhci-2026-102427",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723792/",
  "id": "csnxjnx9b02gk",
  "issns": [
    "2632-1009"
  ],
  "landing_url": "https://doi.org/10.1136/bmjhci-2026-102427",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "1506"
    ],
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7221839562",
  "pdf_url": "https://informatics.bmj.com/content/33/1/e102427.full.pdf",
  "pmcid": "PMC13723792",
  "pmid": "42951327",
  "published": "2026-10-28",
  "publisher": "BMJ Publishing Group",
  "ris": "https://research.draconitic.com/paper/csnxjnx9b02gk.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:22:21Z",
      "native_id": "PMC13723792",
      "source": "pmc",
      "url": "https://doi.org/10.1136/bmjhci-2026-102427"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Patient safety and health equity of AI-enabled triage tools in primary care: a systematic review",
  "title_text": "Patient safety and health equity of AI-enabled triage tools in primary care: a systematic review",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:32Z",
  "url": "https://research.draconitic.com/paper/csnxjnx9b02gk",
  "venue": "BMJ Health & Care Informatics",
  "versions": []
}