Large language models streamline automated machine learning for clinical studies
Nature Communications, vol. 15, pp. 1603
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.
Authors 7
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. soroosh.arasteh@rwth-aachen.de
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. than@ukaachen.de
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
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Mahshad Lotfinia Aachen Department of Diagnostic and Interventional Radiology Institute of Heat and Mass Transfer
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
Institute of Heat and Mass Transfer, RWTH Aachen University, Aachen, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
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University Hospital Heidelberg · National Center for Tumor Diseases · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
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