Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine

Large language models (LLMs) are being increasingly incorporated into clinical workflows due to their ability to synthesize medical knowledge and support diagnosis and treatment planning. However, their opaque internal decision-making processes limit trust, reliability, and safe clinical adoption. Mechanistic interpretability seeks to address this challenge by revealing how LLMs transform inputs into outputs. This paper explores the use of sparse autoencoders (SAEs) as a promising approach to improving mechanistic interpretability of LLMs in medicine. We discuss how SAE-based analyses can illuminate model reasoning, detect potential failure modes, and complement existing interpretability frameworks. Improving mechanistic interpretability through SAEs may be essential for safely deploying LLMs as trustworthy cognitive aids in clinical medicine.