Boosting GPT-4V's accuracy in dermoscopic classification with few-shot learning. Comment on "can ChatGPT vision diagnose melanoma? An exploratory diagnostic accuracy study".

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. Optimizing a Multimodal Large Language Model for Ultrasound-Based Thyroid Nodule Malignancy Classification: A Comparative Study of Few-Shot Learning, Prompt Engineering, and Fine-Tuning.Diagnostics (Basel, Switzerland) · 2026
   Article
2. Limitations and risks of custom GPTs in dermatology. Comment on "ReconGPT: A novel artificial intelligence tool and its potential use in post-Mohs reconstructive decision-making".Journal of the American Academy of Dermatology · 2025
   Article
3. Adapting ChatGPT for Color Blindness in Medical Education.Annals of biomedical engineering · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

2 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

Indexed as