Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
A prospective single-center clinical validation of the Melanoscope AI mobile dermoscopy CDSS demonstrated 88.6% agreement with expert assessment on 176 patients, with no false negatives and 88.3% specificity. The study developed a quantitative interpretability method for cascade deep learning models and a three-zone patient routing algorithm, supporting reproducible and interpretable decision-making for skin cancer screening in resource-limited settings.
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Key points
- The Melanoscope AI system achieved 88.6% agreement with experts on 176 patients, with zero false negatives among 5 malignant lesions.
- Specificity reached 88.3%, with 3 melanomas and 2 basal cell carcinomas histologically confirmed.
- Interpretability was enhanced via attention map visualization (IoU assessment) and a three-zone routing algorithm.
Why it matters
This matters because the Melanoscope AI system achieved 88.6% agreement with experts on 176 patients, with zero false negatives among 5 malignant lesions.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27561] Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
[Submitted on 26 May 2026]
Title:Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
View a PDF of the paper titled Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System, by Elena Sergeevna Kozachok and 1 other authors
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Abstract:Introduction. Early detection of malignant skin lesions is critical for prognosis, yet dermatologist shortages in Russian regions limit screening coverage. Mobile dermoscopy clinical decision support systems (CDSS) offer a promising approach, with model interpretability and standardised patient routing remaining key barriers to adoption.
Aim. To develop a quantitative interpretability assessment method for cascade deep learning models and a three-zone patient routing algorithm, and to conduct a preliminary single-centre prospective clinical validation of the Melanoscope AI CDSS in Russian outpatient practice.
Material and methods. Two-stage cascade classification of dermoscopic images; attention map visualisation (attention rollout for ViT and Swin; Grad-CAM for ConvNeXt and EfficientNetV2); quantitative IoU-based agreement assessment between activation maps and expert annotations; prospective single-centre validation across four "Melanoma Day" sessions (Orel, Russia, June 2025 - April 2026).
Results. On 176 patients: agreement with expert assessment 88.6%; no false negatives among 5 malignant lesions (95% CI: 47.8-100.0%); specificity 88.3%. Three melanomas and two basal cell carcinomas were histologically confirmed; six dysplastic naevi placed under follow-up. Mean IoU (n=180): ViT - 0.69; Swin - 0.64; ConvNeXt - 0.53; EfficientNetV2 - 0.51. Routing thresholds: P=0.50.
Conclusion. No false negatives were observed; specificity was 88.3%, supporting screening use. The integrated cascade classification, attention map visualisation with IoU assessment, and three-zone routing provide reproducible, interpretable clinical decision support adaptable to varying resource levels.
Comments: 24 pages, 6 figures, 5 tables, 21 references
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27561 [cs.CV]
(or arXiv:2605.27561v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.27561
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Elena Kozachok [view email] [v1] Tue, 26 May 2026 18:29:53 UTC (2,115 KB)
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