Medical diagnosis with evidence search using RL and LLMs

Artificial intelligence applied to medical diagnosis has undergone remarkable evolution in recent years. Large language models (LLMs) have demonstrated the ability to process clinical information, but their traditional operation is based on a passive inference pattern: they assume that all necessary data is already available. However, real clinical practice is an iterative process, where the professional must actively seek new evidence through questions, examinations, and progressive analysis. To overcome this limitation, a novel approach emerges that combines reinforcement learning with verifiable rewards (RLVR) and a closed evidence search environment. In this paradigm, the model not only responds but decides what information to request next, simulating the reasoning of a physician who discards hypotheses until reaching an accurate diagnosis. This system relies on a retrieval-augmented examination simulator (RAGES), which acts as a high-fidelity clinical oracle, providing realistic and knowledge-based tracking. Empirical results show that LLMs can thus be transformed into autonomous assistants, comparable even to much larger models. For healthcare companies, adopting this type of solution represents a qualitative leap. At Q2BSTUDIO we develop custom applications that integrate artificial intelligence for clinical processes, combining AI agents with AWS and Azure cloud services to scale data processing. Our team also offers business intelligence services with Power BI to visualize diagnostic indicators, as well as cybersecurity to protect sensitive patient information. The key lies in designing custom software that adapts to real workflows, allowing AI for businesses to be not an abstract concept but a practical tool that improves clinical decision-making. If you wish to explore how to implement this type of system in your organization, we invite you to learn about our custom application development services.