33 citations · 90 across the 12 of their papers we have counts for
12 papers
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…
A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
Ting Fang Tan, Kabilan Elangovan, Jasmine Ong +10
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose…
Answering real-world clinical questions using large language model based systems
Yen Sia Low, Michael L. Jackson, Rebecca J. Hyde +24
Evidence to guide healthcare decisions is often limited by a lack of relevant and trustworthy literature as well as difficulty in contextualizing existing research for a specific p…
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Alison Callahan, Duncan McElfresh, Juan M. Banda +21
The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that…
Zero-Shot Clinical Trial Patient Matching with LLMs
Michael Wornow, Alejandro Lozano, Dev Dash +3
Matching patients to clinical trials is a key unsolved challenge in bringing new drugs to market. Today, identifying patients who meet a trial's eligibility criteria is highly manu…
INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis
Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg +6
Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus…