Large Language Models Streamline Automated Machine Learning for Clinical Studies

• URL: http://arxiv.org/abs/2308.14120v5
• Date: Wed, 21 Feb 2024 18:35:25 GMT
• Title: Large Language Models Streamline Automated Machine Learning for Clinical Studies
• Authors: Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia, Christiane Kuhl, Jakob Nikolas Kather, Daniel Truhn, Sven Nebelung
• Abstract summary: ChatGPT Advanced Data Analysis (ADA) is an extension of GPT-4 to perform machine learning analyses efficiently. ADA autonomously developed state-of-the-art ML models based on the original study's training data to predict clinical outcomes. Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts.
• Score: 2.4889420816783963
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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>0.071). 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.

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