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Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Author

Listed:
  • Juexiao Zhou

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST)
    LLC)

  • Xiaonan He

    (Affiliated to Capital Medical University)

  • Liyuan Sun

    (Affiliated to Capital Medical University)

  • Jiannan Xu

    (Affiliated to Capital Medical University)

  • Xiuying Chen

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Yuetan Chu

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Longxi Zhou

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Xingyu Liao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Bin Zhang

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Shawn Afvari

    (LLC
    Harvard University
    New York Medical College)

  • Xin Gao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

Abstract

Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.

Suggested Citation

  • Juexiao Zhou & Xiaonan He & Liyuan Sun & Jiannan Xu & Xiuying Chen & Yuetan Chu & Longxi Zhou & Xingyu Liao & Bin Zhang & Shawn Afvari & Xin Gao, 2024. "Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50043-3
    DOI: 10.1038/s41467-024-50043-3
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    References listed on IDEAS

    as
    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Publisher Correction: Large language models encode clinical knowledge," Nature, Nature, vol. 620(7973), pages 19-19, August.
    3. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    4. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Large language models encode clinical knowledge," Nature, Nature, vol. 620(7972), pages 172-180, August.
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