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Quantifying cancer clinical trial eligibility using artificial Intelligence-Based matching
- Truvace record
- TRV-2026-2470raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 3faaaa952cfb…check it
Purpose: To develop and validate an artificial intelligence-enabled platform that combines efficient screening with explicit eligibility logic by converting unstructured cancer trial criteria into reusable structured queries, and illustrates potential eligibility using synthetic patients. Methods: We downloaded actively recruiting US interventional treatment trials for advanced/metastatic breast cancer, colon cancer, and non-small cell lung cancer from ClinicalTrials.gov. Medical oncologists created 24 synthetic…
53Gain side
How the two sides scoreGain scores higher
46Problem side
The gain
In a validation using 24 synthetic patients, an AI tool matched each patient against 746 cancer trials in a median 0.459 seconds and agreed with physician consensus on 230 of 240 patient-trial pairs (95.8%).
The problem
The automated matching missed some eligible pairs, with sensitivity of 83.3% against physician consensus, and was tested only on synthetic vignettes that left out laboratory results and comorbidities.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
Matching was evaluated only on 24 synthetic patient vignettes that did not include laboratory results or comorbidities, and the authors say missed matches mean further validation is needed.
Sources
1 on file- JNCI Cancer Spectrumdoi:10.1093/jncics/pkag120
Cite this record
v1Truvace Impact Record TRV-2026-2470, v1: “Quantifying cancer clinical trial eligibility using artificial Intelligence-Based matching.” Truvace, 2026-11-11. /record/TRV-2026-2470 (accessed on the day you copy it). sha256 3faaaa952cfbf04e…
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