Clinical Decision Support System

Clinical Decision Support Systems (CDSS) aim to improve healthcare by providing clinicians with timely, evidence-based recommendations. Current research emphasizes enhancing CDSS accuracy and interpretability through various machine learning techniques, including large language models (LLMs), adaptive feature selection algorithms, and decision trees, often incorporating visual analytics for improved usability. This focus on explainability and user experience is crucial for building trust and facilitating wider adoption of CDSS in diverse clinical settings, ultimately improving patient care and efficiency. The integration of LLMs and knowledge graphs is a particularly active area, aiming to leverage both structured and unstructured data for more comprehensive and accurate decision support.