Clinical Decision Support

Clinical decision support (CDS) aims to improve healthcare decisions by integrating data-driven insights into clinical workflows. Current research heavily focuses on leveraging large language models (LLMs), often augmented with retrieval mechanisms and clinical guidelines, to enhance accuracy and trustworthiness while mitigating issues like hallucinations and bias. A key challenge lies in balancing model performance with explainability and usability for clinicians, leading to exploration of hybrid AI architectures and novel evaluation methods that assess real-world clinical impact rather than solely relying on benchmark datasets. Ultimately, effective CDS systems promise to improve patient outcomes and optimize resource allocation by providing clinicians with timely, reliable, and interpretable information.