"AI Tools Based On One Organization's Data May Need More 'Education'" - Fred Bazzoli
A study suggests that artificial intelligence tools used to diagnose images may need a wider range of images to train on than previously thought. The research, conducted at the Icahn School of Medicine at Mount Sinai, found that AI tools trained on only one organization's images may suffer declines in performance and accuracy when tested on data from other healthcare organizations. “Our findings should give pause to those considering rapid deployment of artificial intelligence platforms without rigorously assessing their performance in real-world clinical settings reflective of where they are being deployed,” said senior author, Eric Oermann, MD, instructor in neurosurgery at the Icahn School of Medicine at Mount Sinai. “If CNN systems are to be used for medical diagnosis, they must be tailored to carefully consider clinical questions, tested for a variety of real-world scenarios and carefully assessed to determine how they impact accurate diagnosis,” said first author, John Zech, a medical student at the Icahn School of Medicine at Mount Sinai.
— Eric Oermann, MD, Instructor, Department of Neurosurgery, Icahn School of Medicine at Mount Sinai
— John Zech, Medical Student, Icahn School of Medicine at Mount Sinai
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