Transformer Based Medical

Transformer-based models are rapidly transforming medical applications, primarily aiming to improve the analysis and understanding of complex medical data. Current research focuses on adapting transformer architectures, such as BERT and its biomedical variants, for tasks like medical image analysis (including segmentation, classification, and detection), natural language processing of clinical text (e.g., intent detection, entity extraction, and report generation), and robotic surgery (e.g., imitation learning for surgical tasks). These advancements hold significant promise for improving diagnostic accuracy, treatment planning, and overall patient care, as well as accelerating medical research through more efficient data analysis.