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Framing Bias in a Large Language Model: A Diagnostic Accuracy Study of Prompt Effects on ChatGPT’s Melanoma Classification

  • Daniele Omar Traini (Creator)
  • Gerardo Palmisano (Creator)
  • Alessandro Stefani (Creator)
  • Ketty Peris (Creator)

Dataset

Description

- Mendeley Supplemental Figure 1. Representative examples of the test. Each image was presented six times under different instructions: a neutral baseline prompt, and five framed prompts.
- Mendeley Supplemental File 1. Detailed Materials and methods (Study design, Model Access and Interaction, Dataset Details, Prompting Procedure, Outcome Measures and Statistical Analysis) of the study
Date made available18 Nov 2025
PublisherUniversità Cattolica del Sacro Cuore

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