Ambiguity-aware AI Assistants for Medical Data Analysis
Mike Schaekermann, Graeme Beaton, Elaheh Sanoubari, Andrew Lim, Kate Larson, Edith Law
Abstract
Artificial intelligence (AI) assistants for clinical decision making show increasing promise in medicine. However, medical assessments can be contentious, leading to expert disagreement. This raises the question of how AI assistants should be designed to handle the classification of ambiguous cases. Our study compared two AI assistants that provide classification labels for medical time series data along with quantitative uncertainty estimates: conventional vs. ambiguity-aware. We simulated our ambiguity-aware AI based on real-world expert discussions to highlight cases likely to lead to expert disagreement, and to present arguments for conflicting classification choices. Our results demonstrate that ambiguity-aware AI can alter expert workflows by significantly increasing the proportion of contentious cases reviewed. We also found that the relevance of AI-provided arguments (selected from guidelines either randomly or by experts) affected experts' accuracy at revising AI-suggested labels. Our work contributes a novel perspective on the design of AI for contentious clinical assessments.