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 Code Interpreter (CI), 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 CI without specific guidance. ChatGPT CI 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.
Strikingly, these ML models matched or outperformed their published
counterparts. We conclude that chatGPT CI offers a promising avenue to
democratize ML in medicine, making advanced analytics accessible to non-ML
experts and promoting broader applications in medical research and practice.

chatGPT Code Interpreter (CI) 通过自主开发 ML 模型来预测临床结果并在医学研究和实践中推广广泛应用，从而弥合机器学习开发者和临床实践者之间的知识鸿沟。