Large Language ModelsNCT07304908
Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine
A study testing Perception-based interventions in people with Large Language Models and Acceptability of Health Care, run by Peking University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Large Language Models, Acceptability of Health Care, Perception, Self
- What is being tested?
- Perception-based interventions
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 25 Nov 2025to 31 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- ≥18 years
- Capable of completing an online survey
- Agree to sign an informed consent form
Who cannot join
- Unable to answer questions or communicate
- Not willing to participate in this study
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Perception-based Interventions Affect Public Acceptance of Using Large Language Models in Medicine: Randomized Controlled Trial
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.
Read the detailed description
Owing to rapid advances in artificial intelligence, large language models (LLMs) are increasingly being used in a variety of clinical settings such as triage, disease diagnosis, treatment planning, and self-monitoring. Despite their potential, the use of LLMs remains restricted within healthcare settings due to lack of accuracy, coherence, and transparency and ethical concerns. Public perceptions such as perceived usefulness and risks play a crucial role in shaping their attitudes towards artificial intelligence that can either facilitate or hinder its adoption. Yet, to our knowledge, there is lack of awareness about perception-driven interventions in health care and no previous studies have examined whether public perceptions play a role in the acceptability of medical applications of LLMs. Hence, 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.
- Being tested
- Perception-based interventions
- Keywords
- Large language model, Artificial intelligence, Perception-based interventions, Public acceptance
- Medical subject headings
- Patient Acceptance of Health Care
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Perception-based interventionsOther
Participants allocated to the intervention group received perception-based interventions. Interventions for Groups 1-3 were perceived benefits of LLMs in medicine, perceived racial bias in LLMs in medicine, and perceived ethical conflicts in LLMs in medicine, respectively.
| Study group | Role | What they receive |
|---|---|---|
| Perceived benefits of large language models in medicine | Experimental | Participants were asked to read "In April 2023, Massachusetts General Hospital launched a pilot program utilizing medical LLMs to assist with emergency department triage and initial diagnosis and observed a reduction in patient wait times and an improvement in clinical efficiency." Receives: Other: Perception-based interventions |
| Perceived racial bias in large language models in medicine | Experimental | Participants were asked to read "In November 2022, a research team from the University of California, San Francisco found that cutting-edge medical LLMs exhibited racial bias when recommending treatment plans." Receives: Other: Perception-based interventions |
| Perceived ethical conflicts in large language models in medicine | Experimental | Participants were required to read "In February 2023, a major European hospital network inadvertently leaked partially anonymized but still sensitive patient data during the testing of medical LLMs due to a system configuration error. Although no direct patient harm occurred, this increased public concerns regarding data privacy and security and compelled relevant institutions to conduct urgent reviews of their data protection measures." Receives: Other: Perception-based interventions |
| Control | No intervention | No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of participants who will change their attitudes towards medical applications of large language models
Public acceptance of applying large language models to medicine will be categorized into yes, not sure, and no, which will be collected before perception-based interventions and after interventions.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Dec 2025
- Study start25 Nov 2025
- Primary completion31 Oct 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Large Language ModelsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University
- Sponsor type
- University, hospital or other
- Collaborators
- Peking University Third Hospital
- Responsible party
- Principal investigator, Liu Jue, Peking University
- Investigators
- Jue Liu, Principal investigator, Peking University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07304908
- Sponsor's protocol ID
- NNSF72474005
- Status last verified
- Dec 2025
- First submitted
- 1 Dec 2025
- Last updated
- 26 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Perception-based Interventions Affect Public Acceptance of Using Large Language Models in Medicine: Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07304908
- Acronym / sponsor ID
- NNSF72474005
- Keywords
- Large language model, Artificial intelligence, Perception-based interventions, Public acceptance
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Peking UniversityUniversity, hospital or other
- Collaborators
- Peking University Third Hospital
- Study start
- 25 Nov 2025 (actual)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 26 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Owing to rapid advances in artificial intelligence, large language models (LLMs) are increasingly being used in a variety of clinical settings such as triage, disease diagnosis, treatment planning, and self-monitoring. Despite their potential, the use of LLMs remains restricted within healthcare settings due to lack of accuracy, coherence, and transparency and ethical concerns. Public perceptions such as perceived usefulness and risks play a crucial role in shaping their attitudes towards artificial intelligence that can either facilitate or hinder its adoption. Yet, to our knowledge, there is lack of awareness about perception-driven interventions in health care and no previous studies have examined whether public perceptions play a role in the acceptability of medical applications of LLMs. Hence, 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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Perceived benefits of large language models in medicine | Experimental | Other: Perception-based interventionsParticipants were asked to read "In April 2023, Massachusetts General Hospital launched a pilot program utilizing medical LLMs to assist with emergency department triage and initial diagnosis and observed a reduction in patient wait times and an improvement in clinical efficiency." |
| Perceived racial bias in large language models in medicine | Experimental | Other: Perception-based interventionsParticipants were asked to read "In November 2022, a research team from the University of California, San Francisco found that cutting-edge medical LLMs exhibited racial bias when recommending treatment plans." |
| Perceived ethical conflicts in large language models in medicine | Experimental | Other: Perception-based interventionsParticipants were required to read "In February 2023, a major European hospital network inadvertently leaked partially anonymized but still sensitive patient data during the testing of medical LLMs due to a system configuration error. Although no direct patient harm occurred, this increased public concerns regarding data privacy and security and compelled relevant institutions to conduct urgent reviews of their data protection measures." |
| Control | No intervention | –No intervention |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- ≥18 years
- Capable of completing an online survey
- Agree to sign an informed consent form
Key exclusion criteria (2 listed)
- Unable to answer questions or communicate
- Not willing to participate in this study
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Perception-based interventionsParticipants allocated to the intervention group received perception-based interventions. Interventions for Groups 1-3 were perceived benefits of LLMs in medicine, perceived racial bias in LLMs in medicine, and perceived ethical conflicts in LLMs in medicine, respectively. | Other | Perceived benefits of large language models in medicine, Perceived ethical conflicts in large language models in medicine, Perceived racial bias in large language models in medicine |
Primary outcome
Number of participants who will change their attitudes towards medical applications of large language models
Time frame: Through study completion, an average of 1 year
Public acceptance of applying large language models to medicine will be categorized into yes, not sure, and no, which will be collected before perception-based interventions and after interventions.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Dec 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jue Liu principal investigator
Peking University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Perception-based interventions in people with Large Language Models and Acceptability of Health Care, run by Peking University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 26 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of participants who will change their attitudes towards medical applications of large language models (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Dec 2025.