SynthesisJournal of medical Internet research2026
Real-World Effectiveness of AI-Enabled Clinical Decision Support in Primary Care: Systematic Review.
Synthesis in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAI-enabled clinical decision support systems (CDSS) are being used in primary care, but their effects on clinical decisions and patient outcomes remain uncertain.
objectiveThis 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.
methodsFollowing 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.
resultsA 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.
conclusionsEvidence 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 registrationPROSPERO CRD420261302104; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261302104.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.