Factors Influencing the Effectiveness of AI-Assisted Decision-Making in Medicine: A Scoping Review
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Objective
Research on artificial intelligence-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 three outcomes: clinicians’ attitudes toward AI, their decisions (e.g., acceptance rate of AI recommendations), and their performance when utilizing AI-CDS.
Results
We retrieved 5,850 articles and included 45. Four clinician-level and technology design factors were commonly studied. Expert clinicians may benefit less from AI-CDS than non-experts, 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 three 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.
