New York: In a first such warning when it comes to the role of Artificial Intelligence in making sense of critical health data, a team of US researchers has said AI in the medical space must be carefully tested for performance across a wide range of populations as the deep learning models may fall short.

The findings should give pause to those considering rapid deployment of AI platforms without rigorously assessing their performance in real-world clinical settings reflective of where they are being deployed, observed the team from the Icahn School of Medicine at Mount Sinai School of Medicine. AI tools trained to detect pneumonia on chest X-rays suffered significant decreases in performance when tested on data from outside health systems. These findings suggest that the deep learning models may not perform as accurately as expected.

“Deep learning models trained to perform medical diagnosis can generalise well, but this cannot be taken for granted since patient populations and imaging techniques differ significantly across institutions,” said Senior Author Eric Oermann, MD, Instructor in Neurosurgery at the Icahn School of Medicine at Mount Sinai.

To reach this conslusion, the researchers assessed how AI models identified pneumonia in 158,000 chest X-rays across three medical institutions. In three out of five comparisons, the convolutional neural networks’ (CNNs) performance in diagnosing diseases on X-rays from hospitals outside of its own network was significantly lower than on X-rays from the original health system.

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