Large Medical Model

Large medical models (LMMs) are AI systems trained on massive medical datasets to improve healthcare prediction, diagnosis, and management. Current research focuses on adapting transformer-based architectures for tasks like cost prediction, risk assessment, and medical image analysis, often employing techniques like knowledge distillation to improve efficiency and address the challenges of deploying large models. These models hold significant promise for enhancing healthcare analytics, personalized medicine, and public health initiatives, particularly by enabling more accurate predictions and facilitating the integration of diverse medical data types. However, ongoing research also emphasizes the need for robust security measures to mitigate potential risks associated with these powerful tools.