Fig. 6: Performance of PanDerm in human–AI collaborative assessment of 128 skin conditions using clinical images.

a, Reader demographics (n = 37 readers): dermatology group (n = 20 readers) including residents and specialists, and generalist group (n = 17 readers) including pre-vocational trainees, general practitioners, nurses and clinical trial assistants. Each reviewed up to 50 of 200 cases. b, Geographic distribution of readers. c–e, Reader-wise analysis (each data point represents one reader, n = 37 readers): comparisons without versus with PanDerm support for: top 1 diagnostic assessment score (1–4) (c), top 3 diagnostic accuracy (d) and diagnostic confidence score (1–4) (e). f, Diagnosis change ratio after PanDerm support by specialization group. g,h, Class-wise analysis (each data point represents one skin condition class): comparisons without versus with PanDerm support by specialization groups for the top 1 diagnostic assessment score (1–4) (g) and top 3 diagnostic accuracy (h) (n = 128 classes per group). i,j, Comparisons without versus with PanDerm support by disease category for the top 1 diagnostic assessment score (1–4) (i) and the top 3 diagnostic accuracy (j), stratified by inflammatory (n = 78 classes), neoplastic (n = 37 classes) and other (n = 13 classes) conditions. P values in c–e were calculated using two-sided paired t-test across readers, while P values in g–j were calculated using two-sided paired t-test across classes. In all the boxplots, the horizontal lines represent medians and the white dots represent means. The upper and lower box limits indicate the 1st and 3rd quartiles, with whiskers extending to 1.5 times the interquartile range. Error bars represent 95% CIs.