医療情報学連合大会

This presentation explores the evolution of research methodologies in hospitals, transitioning from traditional double-blind randomized control trials to data-driven approaches utilizing electronic medical records (EMRs). With the advent of machine learning, deep learning, and data mining, hospitals have been leveraging their vast datasets for clinical research. More recently, large language models (LLMs) have emerged as powerful tools for improving predictions and operational efficiency. Studies, such as those conducted at NYU, have demonstrated that LLMs outperform traditional models in clinical outcome predictions. As hospitals move towards automating routine processes and care pathways, we must also address challenges in AI auditing, regulation, and transparency. This presentation will discuss the role of AI in the future of hospitals, the need for defining automation levels similar to autonomous driving, and the importance of AI's safety in patient care. Finally, the implications of these technological advancements on medical education and training will be explored, particularly in preparing the next generation of healthcare professionals to master both clinical knowledge and emerging AI technologies.