How Explainable Artificial Intelligence Can Increase or Decrease Clinicians’ Trust in AI Applications in Health Care: Systematic Review

作者：Rikard Rosenbacke, Åsa Melhus, Martin McKee, David Stückler · 发表于：JMIR AI · 年份：2024 · DOI：10.2196/53207 · 被引用次数：213 · 研究领域：Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)、Clinical Reasoning and Diagnostic Skills

BACKGROUND: Artificial intelligence (AI) has significant potential in clinical practice. However, its "black box" nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. OBJECTIVE: This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians' trust in AI-driven clinical decision-making. METHODS: A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians' trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. RESULTS: The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also inco...