Health AI Central
Share:
Peer-Reviewed Publication
J Am Med Inform Assoc2024;31(9):1953-1963.September 1, 2024Journal Article

Distilling large language models for matching patients to clinical trials.

Mauro Nievas1, Aditya Basu2, Yanshan Wang3, Hrituraj Singh1
1Triomics Research, Triomics, Inc., San Francisco, CA 94105, United States.
2Triomics Research, Triomics, Inc., Bengaluru, Karnataka 560102, India.
3Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA 15260, United States.
PubMed PMC DOI PDF

Abstract

OBJECTIVE: The objective of this study is to systematically examine the efficacy of both proprietary (GPT-3.5, GPT-4) and open-source large language models (LLMs) (LLAMA 7B, 13B, 70B) in the context of matching patients to clinical trials in healthcare. MATERIALS AND METHODS: The study employs a multifaceted evaluation framework, incorporating extensive automated and human-centric assessments alo…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.