Clinical decision making (CDM) is a multifaceted process that remains a formidable challenge for AI. While large language models (LLMs) have shown promise in medical knowledge tests, their ability to navigate the intricacies of real-world clinical scenarios is still limited. Existing benchmarks often fail to capture the personalized, interactive, and sequential nature of actual medical practice. A new research paper introduces MedChain, a dataset designed to bridge this gap by presenting 12,163 clinical cases that mimic the flow of real patient interactions, encompassing five key stages from specialty referral to treatment. MedChain emphasizes personalization by incorporating detailed patient-specific data. It forces AI to actively gather information interactively, much like a real doctor-patient consultation. Crucially, it also reflects the sequential nature of CDM, where decisions at each stage impact subsequent steps. To tackle the challenges posed by MedChain, the researchers also developed MedChain-Agent, a multi-agent AI system with a feedback loop and a retrieval-augmented generation (RAG) component called MCase-RAG. This system learns from previous cases and adapts its responses, much like a doctor building experience. MedChain-Agent outperforms existing LLM-based approaches, demonstrating an improved ability to dynamically gather information and manage sequential tasks. The ability to incorporate feedback and learn from past cases proved crucial to MedChain-Agent’s success. This research represents a significant advancement in evaluating and developing medical AI. By mimicking the complex and nuanced reality of clinical practice, MedChain and MedChain-Agent offer a more robust framework for building AI systems capable of assisting—and perhaps one day even leading—complex medical decision-making.