Physicians value AI explanations in medical imaging, but trust false diagnoses over XAI

A new study analyzing explainability in AI-based medical image diagnosis surveyed 33 physicians, finding that 88% believe AI explanations are important for diagnoses. Participants rated a combination of bounding boxes and textual reports as the most effective explainability method. Notably, 50% of physicians trusted false AI diagnoses over all tested explainability techniques, highlighting a significant gap in trust and understanding. AI

IMPACT Highlights the critical need for effective explainability in medical AI to ensure trust and proper adoption by physicians.

RANK_REASON This is a research paper published on arXiv detailing a user-centric analysis of explainability methods in AI for medical image diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

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