Table 2:

Summary of reader accuracy versus that of automated classifiers

	Readers	All algorithms	Top 3 algorithms
AK*	0·43 (0·23–0·63)	0·44 (0·42–0·46)	0·83 (0·77–0·89)
BCC*	0·70 (0·61–0·79)	0·80 (0·77–0·82)	0·91 (0·88–0·95)
BKL	0·48 (0·36–0·60)	0·37 (0·35–0·39)	0·43 (0·37–0·50)
DF*	0·50 (0·30–0·71)	0·33 (0·30–0·36)	0·73 (0·50–0·95)
MEL	0·62 (0·53–0·71)	0·58 (0·56–0·60)	0·70 (0·64–0·77)
NV*	0·56 (0·46–0·66)	0·76 (0·74–0·79)	0·76 (0·74–0·77)
NT†	0·26 (0·17–0·35)	0·06 (0·05–0·08)	0·01 (0·01–0·02)
SCC	0·65 (0·46–0·83)	0·31 (0·29–0·33)	0·62 (0·55–0·69)
VASC	0·83 (0·68–0·97)	0·46 (0·43–0·49)	0·79 (0·66–0·92)

Data are accuracy of mean count (95% CI). Mean count of correct reader classifications in batches of 30 lesions was 15·7 (95% CI 14·46–16·94). Mean count of correct algorithm (best) classifications in batches of 30 lesions was 18·95 (18·20–19·70). AK=actinic keratosis. BCC=basal cell carcinoma. BKL=benign keratosis. DF=dermatofibroma. MEL=melanoma. NT=not trained. NV=nevi. SCC=squamous cell carcinoma. VASC=vascular lesion.

^*Top three algorithms (average) performed >20% better than readers.

^†Readers performed ≥20% better than algorithms.