{
  "abstract": "Background\n\nAI-enabled clinical decision support systems (CDSS) are being used in primary care, but their effects on clinical decisions and patient outcomes remain uncertain.Objective\n\nThis study aimed to synthesize real-world evaluations of clinician-facing AI-enabled CDSS in primary care and assess evidence for diagnostic performance, changes in care, clinician efficiency, and patient-important outcomes.Methods\n\nFollowing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020, 5 principal bibliographic databases (PubMed, Scopus, Web of Science Core Collection, Cochrane CENTRAL, and CINAHL Ultimate), a supplementary EBSCOhost platform search, and 2 trial registries were searched for publications from 2011 through 2025. Eligible studies evaluated a learned (data-derived) or case-based AI component used by clinicians in routine primary or ambulatory care and reported a clinical, behavioral, process, efficiency, or diagnostic accuracy outcome. Screening was performed independently by 2 reviewers. Risk of bias was assessed with design-appropriate tools, and GRADE (Grading of Recommendations Assessment, Development and Evaluation) was applied to 4 outcome bodies. Deterministic rule-based tools were retained only as contextual comparison.Results\n\nA total of 66 reports were assessed at full text, and 24 studies met the review criteria; 10 were AI-enabled and formed the appraised review population, while 14 deterministic studies were contextual only. Detection findings were inconsistent: AI electrocardiography increased new low ejection fraction diagnoses (odds ratio [OR] 1.32, 95% CI 1.01-1.61), whereas an electronic health record machine learning dementia marker used alone did not (adjusted OR 0.84, 95% CI 0.63-1.11). Diagnostic studies showed high melanoma discrimination (area under the receiver operating characteristic curve [AUROC] 0.960) but only moderate glaucoma discrimination (AUROC 0.80; sensitivity 65%). Nonrandomized before-after studies reported increased urinary tract infection treatment success (from 75% to 80%) and changed diabetic retinopathy screening criteria in 3 of 4 general practitioners. A pre-exposure prophylaxis trial was null overall; a multicomponent fall prevention CDSS improved shared decision-making, with inconclusive medication-change effects. Clinicians reported lower estimated record review time in asthma care. Neither trial assessing patient-important outcomes demonstrated benefit from AI-CDSS; one musculoskeletal functional outcome favored usual care.Conclusions\n\nEvidence published through December 2025 showed mixed effects of AI-enabled CDSS in primary care. Some studies reported improvements in detection or care processes, but patient-important benefit was not demonstrated. Heterogeneity and limited outcome measurement prevented causal explanations for these differences. Future trials should assess clinical decisions and patient outcomes together.Trial registration\n\nPROSPERO CRD420261302104; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261302104.",
  "abstract_shown": true,
  "affiliations": [
    "Department of General Practice, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, Beijing, China."
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yash Jain",
      "orcid": "0000-0002-8029-9131"
    },
    {
      "affs": [
        0
      ],
      "name": "Dishan Wu",
      "orcid": "0000-0003-1215-9126"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhong Wang",
      "orcid": "0000-0003-4590-0232"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/90nxpdfxsv5rc.bib",
  "cited_by": 0,
  "doi": "10.2196/98630",
  "fulltext_url": "https://europepmc.org/articles/PMC13691469",
  "id": "90nxpdfxsv5rc",
  "issns": [
    "1439-4456",
    "1438-8871"
  ],
  "landing_url": "https://europepmc.org/article/MED/42849036",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220988716",
  "pdf_url": "https://europepmc.org/articles/PMC13691469?pdf=render",
  "pmcid": "PMC13691469",
  "pmid": "42849036",
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/90nxpdfxsv5rc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "MED:42849036",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42849036"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Real-World Effectiveness of AI-Enabled Clinical Decision Support in Primary Care: Systematic Review.",
  "title_text": "Real-World Effectiveness of AI-Enabled Clinical Decision Support in Primary Care: Systematic Review.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/90nxpdfxsv5rc",
  "venue": "Journal of medical Internet research",
  "versions": []
}