Clinical Language Model

Clinical language models (CLMs) are AI systems designed to process and understand unstructured clinical text data, such as electronic health records, aiming to improve healthcare decision-making and efficiency. Current research focuses on developing CLMs with enhanced capabilities for handling longitudinal data, mitigating security vulnerabilities like backdoor attacks, and improving performance in low-resource languages through techniques like cross-lingual transfer and synthetic data training. These models, often based on transformer architectures like BERT, show promise in various applications, including predicting patient outcomes (e.g., acute kidney injury, psychiatric conditions), extracting information from clinical notes, and supporting clinical workflows.