A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support

Abstract

Human involvement remains critical in most instances of clinical decision-making. Recent advances in AI and machine learning opened the door for designing, implementing, and translating interactive AI systems to support clinicians in decision-making. Assessing the impact and implications of such systems on patient care and clinical workflows requires in-depth studies. Conducting evaluation studies of AI-supported interactive systems to support decision-making in clinical settings is challenging and time-consuming. These studies involve carefully collecting, analyzing, and interpreting quantitative and qualitative data to assess the performance of the underlying AI-supported system, its impact on the human decision-making process, and the implications for patient care. We have previously developed a toolkit for designing and implementing clinical AI software so that it can be subjected to an application-based evaluation. Here, we present a visual analytics frame-work for analyzing and interpreting the data collected during such an evaluation process. Our framework supports identifying subgroups of users and patients based on their characteristics, detecting outliers among them, and providing evidence to ensure adherence to regulatory guidelines. We used early-stage clinical AI regulatory guidelines to drive the system design, implemented multiple-factor analysis and hierarchical clustering as exemplary analysis tools, and provided interactive visualizations to explore and interpret results. We demonstrate the effectiveness of our framework through a case study to evaluate a prototype AI-based clinical decision-support system for diagnosing pediatric brain tumors.

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