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Journal Article > Commentary
Teaching patient safety in global health: lessons from the Duke Global Health Patient Safety Fellowship.
Johnston BE, Lou-Meda R, Mendez S, et al. BMJ Glob Health. 2019;4:e001220.
Medical errors are a concern across the economic spectrum worldwide. This commentary describes an educational effort to develop champions to lead patient safety, quality improvement, and infection control initiatives in health systems in low- and middle-income countries. The authors highlight the importance of contextualizing training to consider local needs and resources.
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.