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Related Experiment Video

Updated: Mar 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Matching Patients With Cell Surface-Targeted Clinical Trials Using Large Language Models.

S Carson Callahan¹, Matthew R Chrostek¹, Nicholas Rydzewski^1,2

¹Department of Human Oncology, University of Wisconsin, Madison, WI.

JCO Precision Oncology

|March 5, 2026

Summary

This summary is machine-generated.

A new large language model (LLM) pipeline accurately identifies cell surface-targeted therapy (CST) clinical trials. This technology improves patient access to novel cancer treatments by automating trial matching.

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Area of Science:

Oncology
Biotechnology
Artificial Intelligence

Background:

Cell surface-targeted therapies (CSTs) offer precise cancer treatment with reduced toxicity.
Patient matching to CST clinical trials is hindered by complex criteria and fragmented databases, limiting access and accrual.

Purpose of the Study:

To develop and evaluate a large language model (LLM)-driven pipeline for automated identification and annotation of CST clinical trials.
To create an up-to-date database of open CST trials and their targets.

Main Methods:

Utilized a two-pronged LLM approach to extract target information from ClinicalTrials.gov and the National Cancer Institute Drug Database.
Benchmarked eight LLMs, including GPT-4o, against manually curated data of 814 CST and 814 non-CST trials.
Evaluated model performance at target and trial levels, analyzing error sources.

Main Results:

GPT-4o demonstrated high accuracy in identifying CST trials (96.5%) and targets (89.5%).
Performance improved with combined data sources and later trial phases; errors were mainly due to vague descriptions.
The model successfully matched a high percentage of global trials, with Gemma 3:27b and MedLlama3 showing promise in cell surface target prediction.

Conclusions:

The LLM-based approach facilitates real-time, automated patient-trial matching for CSTs, enhancing accessibility.
This method addresses key barriers to clinical trial enrollment and success.
Continued LLM evolution is expected to further optimize patient-trial matching for improved outcomes.