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NCT07304908

Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine

Active, enrolled NA Last updated 26 December 2025
What this trial tests

NA trial testing Perception-based interventions in Large Language Models in 3,000 participants. Participants enrolled and being followed up; not accepting new ones.

Timeline
25 November 2025
Primary endpoint
31 October 2026
31 December 2026

Quick facts

Lead sponsorPeking University
PhaseNA
StatusActive, enrolled
Study typeINTERVENTIONAL
Allocationrandomized
Designparallel
Maskingsingle
Primary purposeother
Enrollment3,000
Start date25 November 2025
Primary completion31 October 2026
Estimated completion31 December 2026
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

Peking University

Who can join

18 and older, any sex, with Large Language Models or Acceptability of Health Care. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Large language models (LLMs) show promise in medicine, but concerns about their accuracy, coherence, transparency, and ethics remain. To date, public perceptions on using LLMs in medicine and whether they play a role in the acceptability of health care applications of LLMs are not yet fully understood. This study aims to investigate public perceptions on using LLMs in medicine and if interventions for perceptions affect the acceptability of health care applications of LLMs.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other recruiting trials for Large Language Models

Currently open trials in the same condition.

Other Peking University trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07304908.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing