2 citations · 2 across the 3 of their papers we have counts for
4 papers
Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
Saksham Khatwani, He Cheng, Majid Afshar +2
Large language models (LLMs) show promise for diagnostic reasoning but often lack reliable, knowledge grounded inference. Knowledge graphs (KGs), such as the Unified Medical Langua…
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Maya Kruse, Majid Afshar, Saksham Khatwani +3
Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on…
Development and Validation of the Provider Documentation Summarization Quality Instrument for Large Language Models
Emma Croxford, Yanjun Gao, Nicholas Pellegrino +16
As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementatio…
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
Emma Croxford, Yanjun Gao, Nicholas Pellegrino +7
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine r…