Clinician-facing artificial intelligence and guideline adherence: a structured narrative review of comparative and interventional evidence.

Authors

Reyhaneh Kalantar, Salimeh Dodangeh, Kazem Khalagi, Bagher Larijani

Published in

Journal of diabetes and metabolic disorders. Volume 25. Issue 2. Pages 226. Epub Aug 07, 2026.

Abstract

To synthesize empirical evidence on whether clinician-facing artificial intelligence (AI) tools improve clinical practice guideline adherence and whether AI-generated recommendations are concordant with physician decision-making.
We conducted a structured narrative review aligned with SANRA and informed by narrative synthesis guidance. PubMed/MEDLINE, Embase, Scopus, Web of Science, and Google Scholar were searched for studies published from January 2020 to May 2026; earlier directly relevant studies were identified through citation tracking. Eligible studies evaluated clinician-facing AI tools, guideline adherence or concordance outcomes, or physician-versus-AI recommendations. Preprints were considered separately as emerging evidence and were not included in the peer-reviewed core synthesis.
Six peer-reviewed empirical studies met the core eligibility criteria. They included one real-world EHR-integrated pathway study, one NLP-based adherence measurement study, one randomized simulation trial of a decision-tree CDSS, and three AI-versus-physician comparative studies. Findings were most favorable for bounded tasks embedded in workflow or structured scenarios. Evidence was weaker for complex real-world decisions, and several studies did not evaluate patient outcomes. Three 2026 preprints were summarized separately as preliminary evidence.
Early evidence suggests that clinician-facing AI may support guideline-concordant care when applied to clearly defined decision tasks and integrated into clinical workflow. However, the evidence base remains small, heterogeneous, and largely indirect for diabetes care. Prospective cardiometabolic studies are needed to evaluate effectiveness, safety, usability, and patient outcomes.
The online version contains supplementary material available at 10.1007/s40200-026-02030-2.

PMID:
42571079
Bibliographic data and abstract were imported from PubMed on 09 Aug 2026.

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