MedicinemedRxiv
Heuristic editor, no API keyVerdict: MajorAI-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 depends on the nurse selecting which lesions to forward for diagnosis, and performance varies with experience.
Key numbers
- 95% CI 1.68-1.77
- 98,422 patients
VerdictA leading story on any desk.
Abstract
Nurse-led skin cancer screening extends specialist reach but depends on the nurse selecting which lesions to forward for diagnosis, and performance varies with experience. We evaluated whether real-time decision support using artificial intelligence (AI) improves malignancy detection in routine nurse-led teledermoscopy screening across MoleMap clinics in New Zealand and Australia (January 2024-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 vs 15.7 malignancies per 1,000 lesions; odds ratio adjusted for nurse experience 1.73, 95% CI 1.68-1.77), a finding 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; findings are therefore associational and require prospective, outcome-based confirmation.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 10% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | █████ 5 | 32% | Definitive: phase 3 randomized evidence on hard endpoints, multi-lab replication, or community verification at scale. |
| Novelty | ██░░░ 2 | 8% | A new combination of known ideas. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ███░░ 3 | 25% | Meaningful benefit to many people within a few years. |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (many tasks); gains (versus baseline, efficacy); design (randomized, multicenter); verification (confidence interval); stakes (global scale, major disease).
How the score was computed
- Merit
- 7.0 / 10
- Adjusted merit
- 5.4 / 10
- Attention
- 0%
- Freshness
- 92%
- Citations0 (reference 20, via semantic-scholar, Oct 3, 2026, 06:17 UTC)