Patient Level Label

Patient-level labels, representing overall patient diagnoses or clinical information, are increasingly used in machine learning for healthcare applications, aiming to improve model accuracy and efficiency while addressing data scarcity and annotation challenges. Current research focuses on leveraging these labels in various ways, including contrastive learning, knowledge distillation, and natural language processing techniques to refine model predictions and mitigate biases, particularly in image analysis and report generation. This approach holds significant promise for improving diagnostic accuracy, automating tasks like report generation and bias detection in electronic health records, and ultimately enhancing patient care.