The educational effectiveness of dermoscopy image-based self-learning on a computer for medical students has not been well examined. To assess the effect of an image-based self-learning session on the dermoscopic diagnostic performance for malignant melanoma (MM), basal cell carcinoma, melanocytic nevus and seborrheic keratosis (SK) on non-acral regions in comparison with a conventional classroom-style lecture, 114 fourth-year medical students (mean age, 23.7 years; male : female, 73:41) were enrolled. The subjects were randomly assigned to either a self-learning to lecture (SL) or lecture to self-learning (LS) group to receive a 15-min image-based self-learning computer session and a 15-min video lecture session in different orders. The user interface of the digital content was the same as that on a website (https://dz-image.casio.jp). Diagnostic performance was determined using the total number of correct answers for the four diseases and by malignancy prediction in examination A (before training), B (after receiving one session) and C (after receiving both sessions). The examinations were all unique and contained five dermoscopic images each of the four diseases. The total number of correct answers and malignancy prediction results for examination B were significantly higher in the SL group than in LS (11.6 and 15.2 vs 10.1 and 13.4, respectively; both P < 0.01), with no remarkable differences for examination C (13.5 and 16.8 vs 13.3 and 16.4, respectively; P = 0.62 and P = 0.21). In subanalyses, the number of correct answers for SK in examination B was significantly higher in the SL group (3.6 vs. 1.8, P < 0.01), while that for MM was significantly lower (2.2 vs 3.0, P < 0.01). Diagnostic performance was comparable between sexes for examination B. In conclusion, computer-assisted dermoscopy image-based self-learning may be a suitable and non-inferior alternative to classroom-style instruction for medical students within an ultra-short training period.