aceLarge Language Model-Assisted Review of Clinical Trial Informed Consent for Data Reuse: Retrieval-Augmented Generation Approach
Background: Regulatory frameworks such as the Belmont Report, the Common Rule, and the Declaration of Helsinki require informed consent to ensure participants understand a study's purpose and can make voluntary decisions about their involvement. Regulations, including the General Data Protection Regulation (Regulation [EU] 2016/679), further emphasize that consent must be freely given and revocable without disadvantage. Although informed consent forms (ICFs) are intended to be clear and accessible, they have bec…
A retrieval-augmented GPT-4.1 workflow extracted data reuse information from clinical trial consent forms across languages, reaching 92.1% accuracy on a holdout set after iterative prompt optimization.
The source notes that LLM hallucinations limit reliability in high-stakes settings, and the best reported accuracy was 92.1%, so a meaningful share of outputs were still not correct.
Evidence
- Peer-reviewedJMIR Medical Informatics2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1539, v1: “Large Language Model-Assisted Review of Clinical Trial Informed Consent for Data Reuse: Retrieval-Augmented Generation Approach.” Truvace, 2026-10-16. /record/TRV-2026-1539 (accessed at citation time). sha256 1daa9b2c9fa2b0b3…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1539 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.