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Famend physician-scientist Eric J. Topol, M.D., and Harvard synthetic intelligence (AI) professional Pranav Rajpurkar, Ph.D., recommend for a transparent separation of the jobs between AI methods and radiologists in an article revealed in Radiology.
“We’re stuck between distrust and dependence, and missing out on the full potential of AI,” mentioned Dr. Rajpurkar, affiliate professor of Biomedical Informatics at Harvard College.
The authors urge a rethinking of the assistive function of AI, which is designed to paintings along human radiologists to toughen diagnostic accuracy. However to this point, totally integrating AI into radiology workflows has fallen in need of expectancies.
“It’s still early for getting a definitive assessment,” mentioned Dr. Topol, professor and government vice chairman, Scripps Analysis. “But several recent studies of GenAI have not demonstrated the widely anticipated synergy between AI and physicians.”
“Current evidence suggests that neither fully integrated assistive approaches nor complete automation are optimal,” Dr. Rajpurkar mentioned. “Radiologists don’t know when to trust AI and when to trust themselves. Add AI errors into the mix, and you get a perfect storm of uncertainty.”
Imposing assistive AI has introduced notable demanding situations, together with cognitive biases that reason radiologists to put out of your mind or over-rely on AI tips….
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Publish date : 2025-07-29 15:02:00
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