4 papers
Evaluating the Presence of Sex Bias in Clinical Reasoning by Large Language Models
Isabel Tsintsiper, Sheng Wong, Beth Albert +2
Large language models (LLMs) are increasingly embedded in healthcare workflows for documentation, education, and clinical decision support. However, these systems are trained on la…
Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder
John Tolladay, Beth Albert, Gabriel Davis Jones
Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpre…
Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
Ravi Shankar, Sheng Wong, Lin Li +4
Reliable abstention is critical for retrieval-augmented generation (RAG) systems, particularly in safety-critical domains such as women's health, where incorrect answers can lead t…
Large language models surpass domain-specific architectures for antepartum electronic fetal monitoring analysis
Sheng Wong, Ravi Shankar, Beth Albert +1
Foundation models (FMs) and large language models (LLMs) have demonstrated promising generalization across diverse domains for time-series analysis, yet their potential for electro…