ArticleJMIR AI2026
Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.