Jackson, Nicholas J.; Brown, Katherine E.; Miller, Rachael.; Murrow, Matthew.; Cauley, Michael R.; Collins, Benjamin X.; Novak, Laurie L.; Benda, Natalie C.; Ancker, Jessica S. (2026).听.听Journal of the American Medical Informatics Association, 33(5), 1054鈥�1064.听
Research on artificial intelligence tools that help clinicians make decisions has had mixed results: sometimes these tools improve decision-making, and sometimes they do not, and it is not always clear why. This review looked at what factors affect how well AI-based clinical decision-support systems, or AI-CDS, work in medicine. The authors searched three medical databases and found 45 relevant studies out of 5,850 screened articles. They focused on how both clinician factors and technology design features influenced three things: how clinicians feel about AI, whether they accept AI recommendations, and how well they perform when using AI. The review found that experienced clinicians may gain less from AI support than less experienced clinicians, although the results were not consistent across studies. It also found that explainable AI, meaning AI that gives reasons for its suggestions, can increase trust, but it may also lead clinicians to trust incorrect recommendations too much, which can hurt performance when humans and AI work together. Clinicians鈥� existing attitudes toward AI also influenced whether they accepted its advice. Overall, the review suggests that future research should focus on 鈥渁ppropriate trust,鈥� meaning clinicians should rely on AI only when the advice is actually trustworthy, rather than simply trying to increase trust in AI overall.
Figure 1.
PRISMA diagram for study inclusion.