Multi Task Medical

Multi-task medical AI aims to develop single models capable of handling diverse medical tasks and data types simultaneously, improving efficiency and generalizability compared to single-task approaches. Current research focuses on adapting large language models and other deep learning architectures, often employing techniques like Mixture-of-Experts (MOE) and low-rank adaptation (LoRA) to manage the complexity of multiple tasks and modalities (e.g., images, text, genomics). This field is significant because it promises more comprehensive and efficient diagnostic and prognostic tools, potentially leading to improved patient care and accelerating biomedical discovery by leveraging the interconnectedness of various medical data sources.