Machine-Learning Classifiers Bested Experts in Diagnosing Skin Lesions

Automated classifiers may be better than physicians when it comes to diagnosing pigmented skin lesions, but human supervision is still needed, researchers found. All machine-learning algorithms reached a mean of 2.01 more correct diagnoses than did all human readers (17.91 vs 19.92; P 0.0001), reported Harald Kittler, MD, of the Medical University of Vienna in Austria, and colleagues in The Lancet Oncology. When comparing the top three machine learning algorithms with 27 human experts with over a decade of experience, the algorithms still outperformed the experts (18.78 vs 25.43; P 0.0001), the investigators found. Notably, the difference between the top three algorithms and experts was significantly lower for images that were gathered from centers that did not contribute images for the training set when compared with other image sets, although there was human under-performance once again (11.4% vs 3.6%; P 0.0001), the researchers wrote. In this study, machine-learning classifiers performed better than experienced human readers in the diagnosis of pigmented skin lesions, suggesting that machine learning should have a more important role in clinical practice, the investigators said.