Patient Data

Patient data analysis is undergoing a transformation driven by the application of large language models (LLMs) and other machine learning techniques to improve clinical decision-making and patient care. Current research focuses on developing and validating LLMs for tasks such as risk prediction, diagnosis support, and report generation, often incorporating diverse data sources like electronic health records (EHRs), imaging data, and wearables, while simultaneously addressing crucial privacy concerns through methods like differential privacy and federated learning. This work aims to enhance diagnostic accuracy, personalize treatment, and improve efficiency in healthcare, but also highlights the need for rigorous validation, fairness considerations, and careful ethical handling of sensitive patient information.