Diagnostic performance of machine-learning-based screening methods for skin disease

Abstract The timely identification of skin diseases is of utmost importance, as it affects around 420 million individuals globally. In order to ensure prompt treatment, it is crucial to make a precise diagnosis. To aid in this process, machine learning (ML) techniques offer dependable screening approaches. Two researchers, operating independently, extensively investigated reputable studies sourced from PubMed, Web of Science, and Scopus databases. Their objective was to assess the efficacy of various ML methods and determine the suitability of a screening system for skin disease diagnosis. The analysis also considered factors such as parameters, dataset characteristics, and settings.