English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Physicians considering clinical trials for their patients are met with thelaborious process of checking many text based eligibility criteria. LargeLanguage Models (LLMs) have shown to perform well for clinical informationextraction and clinical reasoning, including medical tests, but not yet inreal-world scenarios. This paper investigates the use of InstructGPT to assistphysicians in determining eligibility for clinical trials based on a patient'ssummarised medical profile. Using a prompting strategy combining one-shot,selection-inference and chain-of-thought techniques, we investigate theperformance of LLMs on 10 synthetically created patient profiles. Performanceis evaluated at four levels: ability to identify screenable eligibilitycriteria from a trial given a medical profile; ability to classify for eachindividual criterion whether the patient qualifies; the overall classificationwhether a patient is eligible for a clinical trial and the percentage ofcriteria to be screened by physician. We evaluated against 146 clinical trialsand a total of 4,135 eligibility criteria. The LLM was able to correctlyidentify the screenability of 72% (2,994/4,135) of the criteria. Additionally,72% (341/471) of the screenable criteria were evaluated correctly. Theresulting trial level classification as eligible or ineligible resulted in arecall of 0.5. By leveraging LLMs with a physician-in-the-loop, a recall of 1.0and precision of 0.71 on clinical trial level can be achieved while reducingthe amount of criteria to be checked by an estimated 90%. LLMs can be used toassist physicians with pre-screening of patients for clinical trials. Byforcing instruction-tuned LLMs to produce chain-of-thought responses, thereasoning can be made transparent to and the decision process becomes amenableby physicians, thereby making such a system feasible for use in real-worldscenarios.

Improving Patient Pre-screening for Clinical Trials: Assisting Physicians with Large Language Models