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Fault detection and isolation method for gas turbines using self-organizing type-3 fuzzy wavelet neural networks.

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DermaGPT a federated multimodal framework with a meta learned trust function for interpretable dermatology

Nastaran Mehrabi Hashjin1, Mohammad Hussein Amiri2, Maryam Khanian Najafabadi3

  • 1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.

Scientific Reports
|February 7, 2026
PubMed
Summary

DermaGPT, a new AI system, offers accurate and explainable dermatology diagnostics using federated learning for privacy. It achieves high accuracy in lesion identification and malignancy prediction while ensuring data security.

Keywords:
Dermatology diagnosticsFederated learningMeta-learned trustMultimodal AI

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Area of Science:

  • Artificial Intelligence in Medicine
  • Dermatology AI
  • Federated Learning

Background:

  • Generative and federated AI advance privacy-aware diagnostic systems.
  • Multimodal reasoning and explainability are key for trustworthy AI in healthcare.

Purpose of the Study:

  • Introduce DermaGPT, a federated multimodal framework for dermatology decision support.
  • Emphasize trustworthy use with heterogeneous, privacy-sensitive data.

Main Methods:

  • Combined PaLI-Gemma 2 vision-language backbone with retrieval-augmented LLM.
  • Utilized meta-learned trust function (MLTF) for robust federated training.
  • Evaluated on 4,452 biopsy-confirmed images across multiple datasets.

Main Results:

  • Achieved 90.2% diagnostic accuracy for 11 lesion types.
  • Reached 93.3% accuracy in malignancy prediction with well-calibrated outputs.
  • Expert dermatologists found explanations clear and clinically relevant.

Conclusions:

  • Trust-aware, federated multimodal design enables interpretable, efficient, and privacy-aware dermatology AI.
  • DermaGPT augments, rather than replaces, clinician judgment.
  • Local image processing and secure text transmission enhance privacy.