4 citations · 4 across the 6 of their papers we have counts for
6 papers
Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models
Soroush Seifi, Vaggelis Dorovatas, Lin Li +2
Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical settings, yet estimating when their answers can be trusted remains underexplored. We systemati…
CaseWeaver: A Multi-Agent Framework for Multimodal Virtual Clinical Case Generation
Jierui Qu, Jiachuan Peng, Lin Li +2
Clinical diagnosis relies on consistent multimodal data collected from the same patient throughout the disease course, yet such data are difficult to acquire at scale because of co…
PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL
Sheng Wong, Ravi Shankar, Beth Albert +5
Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes…
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
Boqi Chen, Xudong Liu, Jiachuan Peng +5
Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introdu…
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…
Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy
Jahan C. Penny-Dimri, Magdalena Bachmann, William R. Cooke +7
Large language models (LLMs) hold substantial promise for clinical decision support. However, their widespread adoption in medicine, particularly in healthcare, is hindered by thei…