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Large language models streamline automated machine learning for clinical studies

Author

Listed:
  • Soroosh Tayebi Arasteh

    (University Hospital RWTH Aachen)

  • Tianyu Han

    (University Hospital RWTH Aachen)

  • Mahshad Lotfinia

    (University Hospital RWTH Aachen
    Institute of Heat and Mass Transfer, RWTH Aachen University)

  • Christiane Kuhl

    (University Hospital RWTH Aachen)

  • Jakob Nikolas Kather

    (Technical University Dresden
    University Hospital Heidelberg)

  • Daniel Truhn

    (University Hospital RWTH Aachen)

  • Sven Nebelung

    (University Hospital RWTH Aachen)

Abstract

A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study’s training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.

Suggested Citation

  • Soroosh Tayebi Arasteh & Tianyu Han & Mahshad Lotfinia & Christiane Kuhl & Jakob Nikolas Kather & Daniel Truhn & Sven Nebelung, 2024. "Large language models streamline automated machine learning for clinical studies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45879-8
    DOI: 10.1038/s41467-024-45879-8
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    References listed on IDEAS

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    1. Michael Moor & Oishi Banerjee & Zahra Shakeri Hossein Abad & Harlan M. Krumholz & Jure Leskovec & Eric J. Topol & Pranav Rajpurkar, 2023. "Foundation models for generalist medical artificial intelligence," Nature, Nature, vol. 616(7956), pages 259-265, April.
    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. Ahsan Huda & Adam Castaño & Anindita Niyogi & Jennifer Schumacher & Michelle Stewart & Marianna Bruno & Mo Hu & Faraz S. Ahmad & Rahul C. Deo & Sanjiv J. Shah, 2021. "A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    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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