The results showed that reviewers were more likely to align their diagnostic decision with AI advice and underwent a shorter period of consideration when AI provided local explanations.

"Compared with global AI explanations, local explanations yielded better physician diagnostic accuracy when the AI advice was correct," Dr. Yi said. "They also increased diagnostic efficiency overall by reducing the time spent considering AI advice."


When the AI advice was correct, the average diagnostic accuracy among reviewers was 92.8% with local explanations and 85.3% with global explanations. When AI advice was incorrect, physician accuracy was 23.6% with local and 26.1% with global explanations.

"When provided local explanations, both radiologists and non-radiologists in the study tended to trust the AI diagnosis more quickly, regardless of the accuracy of AI advice," Dr. Yi said.

Study co-senior author, Chien-Ming Huang, Ph.D., John C. Malone Assistant Professor in the Department of Computer Science at Johns Hopkins University, pointed out that this trust in AI could be a double-edged sword because it risks over-reliance or automation bias.

"When we rely too much on whatever the computer tells us, that's a problem, because AI is not always right," Dr. Yi said. "I think as radiologists using AI, we need to be aware of these pitfalls and stay mindful of our diagnostic patterns and training."

Based on the study, Dr. Yi said AI system developers should carefully consider how different forms of AI explanation might impact reliance on AI advice.

"I really think collaboration between industry and health care researchers is key," he said. "I hope this paper starts a dialog and fruitful future research collaborations."

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