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.