Overview

The Medical Informatics research area studies data-driven, intelligent healthcare information technologies that standardize, curate, and analyze clinical and biomedical data to support diagnosis, prognosis prediction, and clinical decision-making. Through causal inference and machine learning– and deep learning–based predictive modeling, we derive clinically meaningful evidence from large-scale medical data, while establishing a reliable analytical foundation through data standardization and imputation of missing data. We further develop clinical natural language processing—including clinical information extraction, summarization, and medical speech recognition—together with technologies that span imaging and text, such as medical image captioning, pathology image platforms (PAIP), and multimodal prediction, to integrate heterogeneous healthcare data. Ultimately, this research area aims to generate clinically verifiable evidence by applying data science and artificial intelligence to medicine, contributing to precision medicine and data-driven healthcare innovation.