mlsanityData to 7 Oct 2026

Utilizing ChatGPT to Enhance Clinical Trial Enrollment

arXiv 2306.02077 ↗First posted 3 Jun 2023cs.IR cs.CLcited by 4

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Abstract (arXiv)

Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 55 of its 108 references that are in the map. First posted on arXiv 3 Jun 2023.

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55 of 108 references.

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 68
  2. 2025Investigating Task Arithmetic for Zero-Shot Information Retrievalcited by 4
  3. 2024Controlled LLM-based Reasoning for Clinical Trial Retrievalcited by 3
  4. 2025A systematic review of trial-matching pipelines using large language modelscited by 0