Medical Large Vision Language Model

Medical Large Vision-Language Models (Med-LVLMs) aim to improve medical diagnosis and report generation by integrating visual (medical images) and textual (patient records, reports) data. Current research heavily focuses on mitigating issues like hallucinations (generating factually incorrect information) and addressing data imbalances, employing techniques such as prompting strategies, retrieval-augmented generation, and chain-of-thought reasoning to enhance accuracy and reliability. These models hold significant potential to assist clinicians in various tasks, but rigorous benchmarking and evaluation of trustworthiness, including fairness and robustness, are crucial before widespread adoption.