Clinical Machine Learning

Clinical machine learning (ML) focuses on developing and deploying ML models to improve healthcare, primarily aiming to enhance diagnostic accuracy, risk stratification, and treatment decisions. Current research emphasizes improving model interpretability (e.g., using post-hoc and model-based methods) and addressing challenges like data heterogeneity across institutions and the need for robust model validation beyond traditional external validation approaches (e.g., through recurrent local validation). These efforts are crucial for building trustworthy and reliable ML tools that can be safely integrated into clinical workflows, ultimately leading to improved patient care and more efficient healthcare resource allocation.