Clinical Decision Support

Clinical decision support studies how computer systems can help clinicians interpret patient records, identify risks, choose tests, and plan treatment. Approaches range from statistical and causal models to Bayesian reasoning, reinforcement learning, guideline rules and knowledge graphs, and language-model systems using retrieval or multiple agents; hybrid designs often use AI to extract information and deterministic logic to apply clinical rules. Systems are tested on electronic health records, guideline-based cases, medical images, and medication tasks, with growing use of reader studies and prospective workflow evaluations. A central challenge is that strong benchmark accuracy does not ensure safe performance with incomplete or shifting data, and systems must manage uncertainty, harmful errors, clinician oversight, and fit with real workflows.