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Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology

arXiv 2308.02180 ↗First posted 4 Aug 2023cs.CL cs.LGcited by 24

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Abstract (arXiv)

Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network. Initial findings are promising: out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 4 Aug 2023.

Builds on

8 of 28 references.

References · 8

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  4. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  5. 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
  6. 2020COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matchingcited by 37
  7. 2018Learning Eligibility in Cancer Clinical Trials using Deep Neural Networkscited by 14
  8. 2020Trialstreamer: Mapping and Browsing Medical Evidence in Real-Timecited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 11; 2025: 9; 2026: 2.

24 papers.

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  1. 2024MedCalc-Bench: Evaluating Large Language Models for Medical Calculationscited by 64
  2. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questionscited by 54
  3. 2024Zero-Shot Clinical Trial Patient Matching with LLMscited by 35
  4. 2024ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notescited by 22
  5. 2023Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Modelscited by 20
  6. 2024Panacea: A foundation model for clinical trial search, summarization, design, and recruitmentcited by 10
  7. 2024A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challengescited by 8
  8. 2023TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Modelscited by 7
  9. 2025Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scalecited by 6
  10. 2024PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Modelscited by 4
  11. 2025Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generationcited by 4
  12. 2024A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classificationcited by 3
12 more
  1. 2024Entry-level guide to the use of large language models for medical researchcited by 3
  2. 2024AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionalscited by 3
  3. 2024ACR: A Benchmark for Automatic Cohort Retrievalcited by 2
  4. 2025Clinical trial cohort selection using Large Language Models on n2c2 Challengescited by 2
  5. 2025LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generationcited by 2
  6. 2025Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR datacited by 2
  7. 2025A Survey on LLM-Assisted Clinical Trial Recruitmentcited by 2
  8. 2026Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attributioncited by 2
  9. 2025OphthBench: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Ophthalmologycited by 1
  10. 2025CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMscited by 0
  11. 2025A systematic review of trial-matching pipelines using large language modelscited by 0
  12. 2026Neurosymbolic Clinical Trial Matching via LLM-Driven Abduction and Logical Verificationcited by 0