Journal of medical Internet research
Real-World Effectiveness of AI-Enabled Clinical Decision Support in Primary Care: Systematic Review
By Jain Y, Wu D and Wang Z.
Abstract
Background
AI-enabled clinical decision support systems (CDSS) are being used in primary care, but their effects on clinical decisions and patient outcomes remain uncertain.
Objective
This 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
Following 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.