Real World Clinical

Real-world clinical applications of artificial intelligence, particularly large language models (LLMs) and vision-language models (VLMs), are a rapidly evolving field focused on improving healthcare efficiency and accuracy. Current research emphasizes robust evaluation benchmarks that assess model performance across diverse clinical tasks, including diagnosis, treatment planning, and report generation, often using federated learning to address data privacy and heterogeneity. These efforts aim to bridge the gap between promising AI capabilities and safe, reliable deployment in actual clinical settings, ultimately impacting patient care and clinical workflow.