Surrogate Marker

Surrogate markers are indirect measures used to estimate a difficult-to-obtain or invasive variable, aiming to improve efficiency and reduce risk in various fields. Current research focuses on developing and validating surrogate markers across diverse applications, employing machine learning techniques like random forests and deep learning models, as well as exploring the use of large language models for generating surrogate labels in data annotation. The development of accurate and reliable surrogate markers holds significant promise for improving diagnostic accuracy, accelerating research processes, and enabling more efficient and less invasive clinical practices.