How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?

Abstract

Research applying machine learning and deep learning has become increasingly common in medicine. However, for clinicians lacking Python programming skills, conducting such research has often been an intractable task-even when ample data were available. The emergence of 'vibe coding' in 2025 has substantially lowered this barrier to entry. This review defines vibe coding, provides a taxonomy of its available tools, and illustrates its practical application through several use cases. Vibe coding is a goal-oriented process in which the user focuses on the desired outcome, issuing natural language directives for environment setup, functionality specification, and output format. The generative artificial intelligence (AI) then produces and refines the underlying code through an interactive feedback loop. Tools such as generative AI platforms (e.g., ChatGPT, Gemini, Claude), graphical user interface-based agents (e.g., Memex, Replit), AI-augmented editors (e.g., Cursor, Visual Studio Code), and command-line interface (CLI) agents (e.g., Gemini CLI, Codex CLI, Claude Code) are available. Demonstrative case studies using publicly accessible datasets illustrate how clinicians can generate and refine Python scripts for classification tasks with minimal coding expertise. Researchers are encouraged to select an accessible tool and gain hands-on experience with real-world data. The adoption of these tools by clinicians, residents, and medical students may promote broader engagement with machine learning and accelerate medical research.

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