AI-assisted nurse-led skin cancer screening in a teledermoscopy framework: a multi-site evaluation with one million lesions

Nurse-led skin cancer screening extends specialist reach, but it depends on nurses selecting which lesions to forward for diagnosis, and performance varies with experience. The study evaluated whether real-time artificial intelligence (AI) decision support improves malignancy detection in routine nurse-led teledermoscopy screening across MoleMap clinics in New Zealand and Australia from January 2024 to July 2025.

In this real-world study, clinics using AI decision support were compared with standard clinics. Nurses examined patients, captured dermoscopic images, and forwarded selected lesions to teledermatologists, who provided the reference diagnosis. The analytic cohort comprised 1,102,382 lesions from 98,422 patients across 577 sites.

AI-assisted screening was associated with a higher malignancy detection rate than standard screening: 25.5 versus 15.7 malignancies per 1,000 lesions, with an odds ratio adjusted for nurse experience of 1.73 (95% CI 1.68–1.77). The finding was consistent across all nurse-experience tiers and the three major malignant subtypes. AI assistance was also associated with a shift in recommended management: 21 additional intervention recommendations and 13 additional safety-netting recommendations (self-monitoring, short-term follow-up, or specialist referral) per 1,000 lesions, approximately balanced by 34 fewer no-action recommendations.

The reference standard was teledermatologist diagnosis, and allocation was not randomised. The findings are therefore associational and require prospective, outcome-based confirmation.