Clinical Transformer

Clinical transformers are deep learning models leveraging the transformer architecture to analyze and interpret diverse clinical data, aiming to improve healthcare decision-making and patient outcomes. Current research focuses on applying these models to tasks such as disease prediction (e.g., Alzheimer's, sepsis), treatment recommendation, and medical image analysis, often employing multi-modal approaches and incorporating clinical knowledge to enhance performance and interpretability. These advancements hold significant promise for automating complex clinical tasks, improving diagnostic accuracy, personalizing treatment plans, and ultimately reducing clinician workload and improving patient care.