Narrow Results Clear All
Search results for "Dermatology"
Journal Article > Review
Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. JAMA Dermatol. 2019 Jun 19; [Epub ahead of print].
Advanced computing holds promise for reducing missed diagnoses of cancer. This metanalysis found that computer-aided diagnosis effectively detects melanoma; however, studies were low in quality. The authors suggest that these systems may help assist dermatologists in overcoming the limitations of human cognition for performing repetitive tasks.
Journal Article > Study
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.
Tschandl P, Codella N, Akay BN, et al. Lancet Oncol. 2019;20:P938-947.
Machine learning may have the potential to improve clinical decision-making and diagnosis. In this study, machine-learning algorithms generally performed better than human experts in accurately diagnosing 7 types of pigmented skin lesions and the top 3 algorithms performed better than the 27 physicians.