Supplementary Materials for “Effect of Fitzpatrick Skin Type Prompting on Diagnostic Accuracy in Multimodal Large Language Models: A Within-Image Experimental Study”

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

This supplementary dataset supports the study “Effect of Fitzpatrick Skin Type Prompting on Diagnostic Accuracy in Multimodal Large Language Models: A Within-Image Experimental Study.” It includes the original study protocol, final prompt templates, predefined diagnostic scoring ontology, processed model outputs used for scoring, diagnostic scores, the fully executed analysis notebook, post hoc sensitivity-analysis results, and descriptive top-1 and top-3 diagnostic accuracy results. The study evaluated 656 biopsy-confirmed photographs from the Diverse Dermatology Images dataset under four prompt submissions per browser-based model configuration: no-FST, DDI-concordant FST, and two DDI-discordant FST prompts. ChatGPT 5.2 Edu and Gemini 3.1 Pro were evaluated, producing 5,248 model-output evaluations. Histopathologic diagnosis served as the diagnostic reference standard. Fitzpatrick skin type was treated as DDI-assigned metadata rather than independently verified ground truth. The deposited workbook contains the processed ranked differentials used for scoring; raw pre-trimming responses were not retained. These materials document the study workflow and support reproduction of the reported primary, exploratory, and post hoc analyses.

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