ArticleCureus2025
Multiclass Classification of Pigmented Skin Lesions Using a Multimodal Large Language Model.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- DVM-SLC: A Dual-View Meta-Aware Model for Reliable Multi-Class Skin Lesion Classification from Clinical and Dermoscopic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning-Based Acne Severity Classification Using Standardized Facial Images of Japanese Patients.Cureus · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPigmented skin lesions span benign to malignant entities that often appear similar on standard clinical photographs, complicating accurate diagnosis without specialized imaging. Recently, multimodal large language models (MMLLMs) have attracted attention as image-based diagnostic aids and hold promise as decision-support tools in resource-limited settings where dermoscopy may be unavailable.
objectivesThis study aimed to determine whether a fine-tuned MMLLM can accurately classify eight common pigmented skin conditions using only clinical photographs, thereby providing a non-dermoscopic diagnostic support tool.
methodsWe fine-tuned InstructBLIP-flan-t5-xl (Salesforce, San Francisco, CA) using Hugging Face's Seq2SeqTrainer (Hugging Face Inc., New York City, NY) on a curated dataset of 979 manually cropped regions of interest depicting one of eight lesion types (acquired dermal melanocytosis, basal cell carcinoma, ephelis, malignant melanoma, melasma, nevus, seborrheic keratosis, or solar lentigo). Images were split 80% for training and 20% for validation. During training, lesion labels were masked to encourage learning of visual-text correlations. Model performance was evaluated by macro-average sensitivity, specificity, F1 score, and area under the receiver operating characteristic area under the curve (ROC AUC) for each class.
resultsOn the validation set, the model achieved a macro-average sensitivity of 86.0%, specificity of 98.2%, and F1 score of 0.86. ROC AUC exceeded 0.95 for six of eight classes. Malignant melanoma showed the highest performance (sensitivity 94%, ROC AUC 0.98), while nevus exhibited the lowest sensitivity (78%, ROC AUC 0.89).
conclusionsFine-tuned MMLLMs can accurately classify common pigmented skin lesions from clinical photographs alone, enabling rapid diagnostic support in environments lacking dermoscopy. Future work should expand dataset diversity, undertake multicenter validation, and assess real-world clinical utility to confirm broader applicability.
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