  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.
