AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation

AI-based clinical decision support systems (AI-CDSSs) can help primary care clinicians provide higher quality care amid rising case complexity and resource constraints. However, evidence regarding their effectiveness and safety in primary care practice is lacking.

We synthesized evidence on the effectiveness and safety of AI-CDSSs compared with usual care or non-AI-based systems in primary care settings.

We conducted a systematic review following the Cochrane Handbook for Systematic Reviews of Interventions and reported the results according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. We included experimental and quasi-experimental studies evaluating AI-CDSSs used by health care professionals in primary care settings, compared with usual care or non-AI-based decision support systems, and that reported clinician-, patient-, or system-level outcomes, or safety. An information specialist designed the search strategies and systematically reviewed MEDLINE, Embase, CINAHL, Web of Science, and CENTRAL from inception to June 27, 2025. Reviewers independently performed the selection and extraction processes. Data extraction was informed by the DECIDE-AI and CONSORT-AI (Consolidated Standards of Reporting Trials-AI) reporting guidelines and the APPRAISE-AI tool. We independently assessed the risk of bias using Risk of Bias-2 or Risk of Bias in Non-Randomized Studies of Interventions. Owing to substantial heterogeneity, we conducted a synthesis without meta-analysis and explored heterogeneity using an adapted Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability-informed implementation-context classification. The review protocol was registered in PROSPERO (CRD420251119225).