Clinical AI

4 items

RESEARCHarXiv CS.LG·25d ago

Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders

This paper explores the mechanistic interpretability of EEG foundation models by applying TopK Sparse Autoencoders (SAEs) to extract sparse feature dictionaries from their embeddings. It benchmarks monosemanticity and entanglement across different EEG transformer architectures, grounds these features in a clinical taxonomy, and introduces concept steering to quantify selectivity and expose representational failures.

Clinical AI AI interpretability Foundation Models Sparse autoencoders

RESEARCHarXiv CS.CL·5/7/2026

Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

This research presents a locally deployable framework enabling small language models to extract privacy-sensitive clinical entities from unstructured dental notes through self-generated and refined prompts. The study evaluated open-weight models, achieving high F1 scores with Qwen2.5-14B-Instruct and Llama-3.1-8B-Instruct after supervised fine-tuning and direct preference optimization.

Clinical AI prompt-engineering Information Extraction security

RESEARCHarXiv CS.LG·12d ago

Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility

Liquid Neural Networks (LNNs) model hidden state evolution as a continuous differential equation, addressing the limitations of discrete-time RNNs and LSTMs in capturing fluid temporal dynamics. This paper benchmarks LNNs against LSTMs across four sequential modalities, revealing LNNs' superior parameter efficiency and robustness, especially in native temporal domains and clinical environments.

neural networks Clinical AI deep learning machine learning

NEWSDEV.to AI·4/15/2026

AWS Speed Boosts, Agentic Limits, and Clinical AI Advances

AWS is optimizing LLM inference with speculative decoding on Trainium and vLLM, and the Spring AI SDK for Bedrock AgentCore is now generally available. New research also explores agentic system failures, CNN uncertainty quantification, and LLMs' role in clinical reasoning.

Clinical AI AWS LLM inference Agentic AI