Summary of End-to-end Clinical Trial Matching with Large Language Models, by Dyke Ferber et al.
End-To-End Clinical Trial Matching with Large Language Models
by Dyke Ferber, Lars Hilgers, Isabella C. Wiest, Marie-Elisabeth Leßmann, Jan Clusmann, Peter Neidlinger, Jiefu Zhu, Georg Wölflein, Jacqueline Lammert, Maximilian Tschochohei, Heiko Böhme, Dirk Jäger, Mihaela Aldea, Daniel Truhn, Christiane Höper, Jakob Nikolas Kather
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether Large Language Models (LLMs) can automate the entire process of matching cancer patients to clinical trials. The authors used GPT-4o and 51 synthetic Electronic Health Records (EHRs) to demonstrate that their approach identifies relevant candidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0%. They also found that refining their human baseline using LLM feedback improved model accuracy to 92.7%. The authors present an end-to-end pipeline for clinical trial matching using LLMs, showing high precision in screening and matching trials to individual patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to match cancer patients with the right clinical trials more easily and quickly. Right now, it’s hard for doctors to find the best trials because of inconsistent medical records and complicated criteria. The authors used special computer models called Large Language Models (LLMs) to see if they could make this process easier. They tested their method using fake patient records and found that it worked well, identifying relevant trial matches in most cases. This new approach can work on its own or with human help, and it’s not just for cancer patients – it can be used for people with many different health conditions. |
Keywords
» Artificial intelligence » Gpt » Precision
