Real World Healthcare

Real-world healthcare research focuses on integrating artificial intelligence, particularly large language models (LLMs) and transformer-based architectures, to improve various aspects of medical practice. Current efforts concentrate on developing and validating these models for tasks such as medical education, diagnosis support, patient record analysis (including privacy-preserving redaction), and efficient query routing to specialists. This research aims to enhance the accuracy, efficiency, and accessibility of healthcare while addressing crucial challenges like data bias, model generalizability, and ethical considerations related to AI deployment in clinical settings. The ultimate goal is to improve patient care and streamline healthcare workflows through responsible and effective AI integration.