AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation.
6 affiliations
- 1Faculty of Nursing Sciences, Université Laval, 1050, avenue de la Médecine, Québec, QC, G1V 0A6, Canada, 1 418-290-3089.
- 2VITAM Research Center on Sustainable Healt, Québec, QC, Canada.
- 3Bibliothèque de l'Université Laval, Université Laval, Québec, QC, Canada.
- 4Centre for Digital Health Evaluation, Women's College Hospital, Toronto, QC, Canada.
- 5Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada.
- 6Department of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada.
Abstract
Background
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.Objective
We synthesized evidence on the effectiveness and safety of AI-CDSSs compared with usual care or non-AI-based systems in primary care settings.Methods
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).Results
We identified 4074 records through database searches and 12 additional records through citation searching and trial registration checking. We included 14 records on 11 distinct studies, including 4 experimental and 7 quasi-experimental studies. The risk of bias was high in all experimental studies and serious to critical in quasi-experimental studies. AI-CDSSs showed more consistent positive effects on workflow-related outcomes in structured and protocolized decision contexts, particularly for screening, referral, and alert optimization tasks. Patient-level effects were limited and inconsistent, particularly in more complex contexts involving heterogeneous conditions or preference-sensitive decisions. System-level outcomes were heterogeneous, with isolated improvements in service delivery and utilization outcomes. Safety evaluation was rare and poorly specified. Only 2 studies prespecified safety outcomes, and the reported safety information mainly involved technical malfunctions or self-reported side effects. No study reported clinician interaction measures such as agreement with, override of, or reliance on AI outputs. Interpretation of the findings is limited by substantial heterogeneity, short follow-up periods, predominantly high or critical risk of bias, and evidence derived exclusively from high-income countries.Conclusions
AI-CDSSs in primary care may provide more consistent benefits for workflow-related and process-level outcomes in structured and protocolized decision contexts, while evidence on safety remains insufficient to support confident routine implementation. This systematic review reveals an important gap between the rapid development of AI-CDSSs and the lack of robust, comparable evidence to support their reliable use in primary care.
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BibTeX
@article{naye2026aibased,
title = {{AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation.}},
author = {Florian Naye and Steven Ouellet and Farzaneh Yousefi and Chloé Cachinho and Frédéric Bergeron and Onil Bhattacharyya and Vess Stamenova and Maxime Sasseville},
journal = {Journal of medical Internet research},
year = {2026},
month = oct,
doi = {10.2196/96882},
url = {https://europepmc.org/article/MED/42896153},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation.
AU - Florian Naye
AU - Steven Ouellet
AU - Farzaneh Yousefi
AU - Chloé Cachinho
AU - Frédéric Bergeron
AU - Onil Bhattacharyya
AU - Vess Stamenova
AU - Maxime Sasseville
PY - 2026
DA - 2026/10/19
JO - Journal of medical Internet research
DO - 10.2196/96882
UR - https://europepmc.org/article/MED/42896153
AB - Background 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.Objective We synthesized evidence on the effectiveness and safety of AI-CDSSs compared with usual care or non-AI-based systems in primary care settings.Methods 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).Results We identified 4074 records through database searches and 12 additional records through citation searching and trial registration checking. We included 14 records on 11 distinct studies, including 4 experimental and 7 quasi-experimental studies. The risk of bias was high in all experimental studies and serious to critical in quasi-experimental studies. AI-CDSSs showed more consistent positive effects on workflow-related outcomes in structured and protocolized decision contexts, particularly for screening, referral, and alert optimization tasks. Patient-level effects were limited and inconsistent, particularly in more complex contexts involving heterogeneous conditions or preference-sensitive decisions. System-level outcomes were heterogeneous, with isolated improvements in service delivery and utilization outcomes. Safety evaluation was rare and poorly specified. Only 2 studies prespecified safety outcomes, and the reported safety information mainly involved technical malfunctions or self-reported side effects. No study reported clinician interaction measures such as agreement with, override of, or reliance on AI outputs. Interpretation of the findings is limited by substantial heterogeneity, short follow-up periods, predominantly high or critical risk of bias, and evidence derived exclusively from high-income countries.Conclusions AI-CDSSs in primary care may provide more consistent benefits for workflow-related and process-level outcomes in structured and protocolized decision contexts, while evidence on safety remains insufficient to support confident routine implementation. This systematic review reveals an important gap between the rapid development of AI-CDSSs and the lack of robust, comparable evidence to support their reliable use in primary care.
SN - 1439-4456
SN - 1438-8871
ER -