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Initial Proof-of-Concept Study for a Plastic Surgery-Specific Artificial Intelligence Large Language Model:

Berk B Ozmen, Ibrahim Berber, Graham S Schwarz

    Aesthetic Surgery Journal
    |April 8, 2025
    PubMed
    Summary

    A new large language model, PlasticSurgeryGPT, was developed using plastic surgery literature. It shows improved performance over general models for clinical support, education, and research in plastic surgery.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Natural Language Processing
    • Plastic Surgery Research

    Background:

    • General-purpose large language models (LLMs) have limited applicability in specialized medical fields like plastic surgery due to a lack of domain-specific knowledge.
    • Plastic surgery research requires advanced tools for information synthesis and knowledge dissemination.

    Purpose of the Study:

    • To develop and evaluate PlasticSurgeryGPT, a dedicated LLM fine-tuned on plastic surgery literature.
    • To enhance performance in clinical decision support, surgical education, and research within plastic surgery.

    Main Methods:

    • A dataset of 25,389 plastic surgery abstracts (2010-2024) from PubMed was curated.
    • The GPT-2 model was fine-tuned on this domain-specific dataset using PyTorch and HuggingFace.
    • Performance was evaluated against the default GPT-2 using BLEU, METEOR, and ROUGE-1 metrics.

    Main Results:

    • PlasticSurgeryGPT demonstrated substantial improvements in capturing semantic nuances of plastic surgery text.
    • The fine-tuned model outperformed the generic GPT-2 across BLEU (0.1355 vs 0.1302), METEOR (0.5836 vs 0.5505), and ROUGE-1 (0.2168 vs 0.2155) metrics.
    • These results indicate enhanced relevance and accuracy in generated content.

    Conclusions:

    • PlasticSurgeryGPT is the first plastic surgery-specific LLM, showing superior performance over general models.
    • Domain-specific LLMs hold significant potential for advancing clinical practice, surgical education, and research in plastic surgery.
    • Future work should explore full-text articles, multimodal data, and larger models for further enhancement.