Autonomous Loop Construction And Supervision For Clinician-Oriented Medical-Ai Research

Medical AI models have made a great impact on biomedical research and real-world clinical applications, but conducting interdisciplinary medical AI research remains challenging, requiring close collaboration between clinicians and AI experts. Recent advances in large language models (LLMs) and autonomous code agents present an opportunity for low cost medical AI development, where clinicians can build AI tailored to their own research questions, even without continuous support from dedicated AI experts. However, enabling code agents to autonomously tackle complex multimodal medical AI development tasks requires clinicians to construct and supervise an AI research loop with detailed technical specifics, demanding substantial expertise in AI and computer science that they often lack. To address this challenge, we introduce the Medical AI Research Loop Agent (MARLA), an agentic framework that completely abstracts the construction and supervision of medical AI research loops from clinicians. Given a clinician-defined research intent, MARLA automatically translates high-level research goals into executable hierarchical research loops, decomposes them into verifiable sub-loops, and specifies the models, datasets, tools, and evaluation protocols required for each task. During execution, MARLA coordinates specialized code agents, monitors progress, diagnoses failures, and iteratively refines research strategies based on experimental feedback to drive the research process toward optimal outcomes. We evaluate MARLA on multimodal medical AI tasks that require closed-loop conversion from high-level clinical study objectives to trained and validated AI models. The results demonstrate MARLA's ability to autonomously conduct complex medical AI research while substantially reducing the need for AI expertise.