10 citations · 24 across the 15 of their papers we have counts for
6 papers · 1 filter
CARE: A Conformal Safety Layer for Medical Summarization
Suhana Bedi, Bridget Lin, Anson Y. Zhou +5
Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing…
Quantifying and Mitigating Premature Closure in Frontier LLMs
Rebecca Handler, Suhana Bedi, Nigam Shah
Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remains underexamined in large lang…
Structured Prompts Improve Evaluation of Language Models
Asad Aali, Muhammad Ahmed Mohsin, Vasiliki Bikia +15
As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks are essential for guiding deployment decisions. In practice, however, framewo…
Disentangling Reasoning and Knowledge in Medical Large Language Models
Rahul Thapa, Qingyang Wu, Kevin Wu +11
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reaso…
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi, Hejie Cui, Miguel Fuentes +78
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinica…
Distilling Large Language Models for Efficient Clinical Information Extraction
Karthik S. Vedula, Annika Gupta, Akshay Swaminathan +3
Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferrin…