Medical Domain

Research in the medical domain is rapidly advancing the application of large language models (LLMs) to improve healthcare. Current efforts focus on adapting and fine-tuning LLMs for various medical tasks, including question answering, summarization, and diagnosis support, often employing architectures like mT5 and incorporating multimodal data. These advancements aim to enhance efficiency and accuracy in clinical workflows, improve patient care, and facilitate medical research by enabling more effective analysis of large and complex datasets. However, challenges remain in ensuring model reliability, addressing biases, and establishing robust evaluation methodologies.