Scaling Clinical Trial Matching Using Large Language Models: A Case
  Study in Oncology
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.
64d0597a97ca59bcf728ef76
Cliff Wong
extracted_confirmed
2023-08-07T05:13:04.398Z
64b668e8575d40032dd3005e
/avatars/c4eeef2f911589fa93a427353da7cec7.svg
Cliff Wong
user
cliffwong
64d0597a97ca59bcf728ef77
Sheng Zheng
claimed_verified
2023-08-08T01:20:27.246Z
6234c11b7d5de9839bc44163
/avatars/3ca569596f9c7134e8d4b560a06ee1e7.svg
Sheng Zhang
user
shengz
64d0597a97ca59bcf728ef78
Yu Gu
64d0597a97ca59bcf728ef79
Christine Moung
64d0597a97ca59bcf728ef7a
Jacob Abel
64d0597a97ca59bcf728ef7b
Naoto Usuyama
claimed_verified
2023-08-07T16:30:35.614Z
5f243561e923d665e616263f
https://cdn-avatars.huggingface.co/v1/production/uploads/1596208460315-noauth.jpeg
Naoto Usuyama
user
naotous
64d0597a97ca59bcf728ef7c
Roshanthi Weerasinghe
64d0597a97ca59bcf728ef7d
Brian Piening
64d0597a97ca59bcf728ef7e
Tristan Naumann
extracted_confirmed
2023-08-07T16:27:22.250Z
5e5870466bc35159a08ca572
https://cdn-avatars.huggingface.co/v1/production/uploads/5e5870466bc35159a08ca572/pT6gEEs8RLRJGeM-faNWj.jpeg
Tristan Naumann
user
tnaumann
64d0597a97ca59bcf728ef7f
Carlo Bifulco
extracted_pending
2024-03-22T03:55:10.159Z
65fcfdba7bdbd2233074483f
/avatars/f3a128bda5f2c8729abd6d756c0ebc07.svg
carlo b bifulco
user
carlob
64d0597a97ca59bcf728ef80
Hoifung Poon
extracted_confirmed
2025-02-08T18:13:04.294Z
664d07456083f276c4feb1a4
/avatars/1bfa6d8f82e9223b47630cefd79d7d0e.svg
Hoifung Poon
user
hoifung
Scaling Clinical Trial Matching Using Large Language Models: A Case
  Study in Oncology
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.
Large language models, like GPT-4, show promise in structuring clinical trial eligibility criteria and extracting complex matching logic, improving the efficiency and accuracy of clinical trial matching in oncology.
large language models LLMs GPT-4 clinical trial matching eligibility criteria complex matching logic nested AND/OR/NOT triage patient-trial candidates context limitation accuracy longitudinal medical records