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Adela-Vasilica Gudiu1, Lăcrămioara Stoicu-Tivadar1, Anca Daniela Ionita2

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Generative Artificial Intelligence (AI) models show improved dermoscopic image labeling, particularly in distinguishing melanoma, across GPT-5 series updates. Testing involved various formats and patient metadata for unbiased evaluation.

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Generative Artificial Intelligence (AI) models, like ChatGPT, are gaining traction across various fields.
  • Dermoscopic image labeling is crucial for accurate skin cancer diagnosis.
  • Evaluating the performance of advanced AI models in medical applications is essential.

Purpose of the Study:

  • To assess the performance of OpenAI's GPT-5 series (GPT-5, GPT-5.2, GPT-5.4) in dermoscopic image labeling.
  • To determine if newer versions of GPT-5 offer improved accuracy in identifying skin conditions, specifically melanoma.
  • To compare the effectiveness of different testing formats for AI-driven image analysis.

Main Methods:

  • Utilized three distinct testing formats: free-form answers, label selection, and label selection with patient metadata.
  • Employed paid ChatGPT Plus subscriptions, disabling the "Improve the model for everyone" option for unbiased results.
  • Tested GPT-5 models using the "Thinking" variant across various timeframes for comprehensive evaluation.

Main Results:

  • GPT-5.4 demonstrated notable improvements in distinguishing melanoma compared to earlier versions.
  • Performance variations were observed across the different testing formats.
  • The inclusion of patient metadata alongside images influenced labeling accuracy.

Conclusions:

  • Iterative updates to GPT-5 models show enhanced capabilities in dermoscopic image analysis.
  • The study highlights the potential of advanced AI in improving diagnostic accuracy for skin conditions like melanoma.
  • Further research is warranted to optimize AI model performance and integration into clinical workflows.