Abstract Objectives Research on artificial intelligence (AI)-based clinical decision-support (AI-CDS) systems has returned mixed results. Sometimes providing AI-CDS to a clinician will improve decision-making performance, sometimes it will not, and it is not always clear why. This scoping review seeks to clarify existing evidence by identifying clinician-level and technology design factors that impact the effectiveness of AI-assisted decision-making in medicine. Materials and Methods We searched MEDLINE, Web of Science, and Embase for peer-reviewed papers that studied factors impacting the effectiveness of AI-CDS. We identified the factors studied and their impact on 3 outcomes: clinicians’ attitudes toward AI, their decisions (eg, acceptance rate of AI recommendations), and their performance when utilizing AI-CDS. Results We retrieved 5850 articles and included 45. Four clinician-level and technology design factors were commonly studied. Expert clinicians may benefit less from AI-CDS than nonexperts, with some mixed results. Explainable AI increased clinicians’ trust, but could also increase trust in incorrect AI recommendations, potentially harming human–AI collaborative performance. Clinicians’ baseline attitudes toward AI predict their acceptance rates of AI recommendations. Of the 3 outcomes of interest, human–AI collaborative performance was most commonly assessed. Discussion and Conclusion Few factors have been studied for their impact on the effectiveness of AI-CDS. Due to conflicting outcomes between studies, we recommend future work should leverage the concept of “appropriate trust” to facilitate more robust research on AI-CDS, aiming not to increase overall trust in or acceptance of AI but to ensure that clinicians accept AI recommendations only when trust in AI is warranted.
Jackson et al. (2026) studied this question.