2 citations · 3 across the 10 of their papers we have counts for
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What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Medical RAG needs evidence-grounded claims, so plugging a claim-level NLI checker into retrieval-augmented RL is intuitive. \textbf{We find that the checker's \emph{output distribu…
MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Biomedical retrieval-augmented generation (RAG) can ground LLM answers in medical literature, yet long-form outputs often contain isolated unsupported or contradictory claims with…
Automated Extraction of Fluoropyrimidine Treatment and Treatment-Related Toxicities from Clinical Notes Using Natural Language Processing
Xizhi Wu, Madeline S. Kreider, Philip E. Empey +2
Objective: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxici…
Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
Yuelyu Ji, Wenhe Ma, Sonish Sivarajkumar +6
Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translationa…
SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)
Bernardo Consoli, Xizhi Wu, Song Wang +10
Extracting social determinants of health (SDoH) from unstructured medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusabi…
A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review
Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor +12
With generative artificial intelligence (AI), particularly large language models (LLMs), continuing to make inroads in healthcare, it is critical to supplement traditional automate…