Medical Application

Large language models (LLMs) are rapidly transforming medical applications, aiming to improve efficiency and equity in healthcare delivery. Current research focuses on mitigating biases in LLMs, enhancing privacy through decentralized computing and retrieval-augmented generation, and improving model performance and calibration using techniques like GANetic loss and uncertainty-aware training. These advancements hold significant promise for various medical tasks, including diagnosis, treatment planning, and patient education, but also highlight the critical need for addressing issues of data privacy, model robustness, and equitable access.