2026· International Conference on Language Resources and Evaluation· pp. 515-523· 0 citations· 36 references
Computer Science
TL;DR
It is demonstrated that modern LLMs with reasoning capabilities can effectively support real-time clinical evidence synthesis without task-specific fine-tuning, offering a pathway toward scalable automated systems for clinical trial interpretation that could substantially reduce the evidence-to-practice gap in medical decision-making.
Large Language Models (LLMs) achieve competitive results on Natural Language Inference when applied to clinical trials; however, it is not yet clear which type of inference LLMs perform well or poorly on. We address this by proposing new supplementary annotations for the existing NLI4CT dataset on the types of inferenc...
Mathilde Aguiar, Pierre Zweigenbaum, Nona Naderi· International Conference on...· 0 citations
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets used in medical LLM tasks. These datasets span multiple modalities including t...
L. K. Yan, Qian Niu, Ming Li et al.· Medicine Advances· 32 citations· ⚡1
LLM agents perform reliably for question generation and SAP drafting but require expert verification of formula composition, cohort boundary logic, and concordance computation before results are reported.
Yi-Lan Wu, D. J. Fu, Yu-Kun Zhou et al.· Journal of Medical Internet...· 0 citations
This study compares the performance of three LLMs in extracting, analyzing, and synthesizing regulatory and clinical information from FDA drug reviews, guidance for the industry, and drug labels as accessed through their standard user interfaces, using antibiotics approved for complicated urinary tract infections betwe...
Khulud Bukhari, R. Rodriguez-Monguio, B. Lopez-Bermudez et al.· Discover Artificial Intellig...· 0 citations
This article aims to provide a pragmatic basis for designing and executing rigorous evaluations of healthcare LLMs by describing underlying concepts and potential pitfalls, while emphasizing the importance of aligning evaluation methods with the research question.
S. E. McKinney, P. Vu, S. Justice et al.· 0 citations
A suite of automated tools for automated batch processing that provide decision rationales and evidence enhances transparency and allows for human verification of AI decisions and provides a suite of automated tools for key SR tasks.
Yi-Ran Liu, Xi-Ling Wang, Zi-Xuan Zhou et al.· Journal of Evaluation In Cli...· 0 citations
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