Respiratory DiseasesNCT06931782
AI in Respiratory Disease Prevention, Diagnosis, and Triage
A study testing AI in people with Respiratory Diseases and Artificial Intelligence (AI), run by The First Affiliated Hospital of Guangzhou Medical University at 1 site in China.
Trial summary
This study will evaluate the impact of using the GPT-4o compared to traditional online tools in the field of respiratory disease prevention, focusing on the dissemination of knowledge and behavior changes among the general public. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Respiratory Diseases, Artificial Intelligence (AI), Randomized Controlled Trial
- What is being tested?
- AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,400 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2025to 30 Jun 2025
- 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 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- No medical background
- Aged from 18 to 75 years old
Who cannot join
- Had a medical background
- Exceeds the age criteria
- Failed to comply with the survey requirements
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120
Guangzhou, Guangdong, 510120
About this study
Official title: Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
This study will evaluate the impact of using the GPT-4o compared to traditional online tools in the field of respiratory disease prevention, focusing on the dissemination of knowledge and behavior changes among the general public. We will explore the effectiveness of GPT-4o in enhancing public awareness and management capabilities regarding respiratory diseases and promoting appropriate preventive behaviors.
Read the detailed description
Artificial intelligence (AI) technologies, particularly advanced large language models like GPT-4o developed by OpenAI, hold immense potential in enhancing public health education and preventive behaviors. Although GPT-4o was not specifically designed for respiratory disease prevention, it has shown promising prospects in numerous healthcare-related applications, such as providing health information, responding to public inquiries, and supporting health education efforts. However, its effectiveness in improving public awareness and management capabilities regarding respiratory diseases remains to be further explored.
Understanding and managing respiratory diseases involve complex processes, including symptom recognition, application of preventive knowledge, and informed decision-making. Integrating AI tools like GPT-4o into public health education could potentially enhance knowledge dissemination, reduce misinformation, and encourage appropriate preventive behaviors among the general population. Nevertheless, GPT-4o has not been specifically validated for respiratory disease prevention and carries the risk of generating misleading or inaccurate information, which could confuse users. Improper use of such tools may fail to raise awareness and could even lead to counterproductive behaviors. Therefore, studying how large language models like GPT-4o can effectively support public education and behavior change in this context is of critical importance.
In this study, participants will be randomly divided into two groups: one group will have access to Fine-turned GPT-4o, while the other will rely solely on traditional online tools. They will be presented with scenarios related to respiratory diseases and asked to explain their identification of high-risk factors, understanding of diagnoses, and proposed triage actions for each scenario. Each scenario was developed by a panel of three experts in respiratory health, who also established standardized answers. Responses will be evaluated by two independent groups of reviewers unaware of the participants' group assignments. These experts independently created initial scoring criteria and resolved discrepancies through multiple rounds of discussion to ensure consistency and accuracy.
- Being tested
- AI
- Medical subject headings
- Respiratory Tract Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIOther
GPT-4o fine-tuned with the Lungdiag database
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Group | Experimental | Participants completed the questionnaire using AI-driven tools for content generation and information retrieval. Receives: Other: AI |
| Internet-Based Group | No intervention | Participants completed the questionnaire using standard internet search engines for information retrieval. |
What is being measured
The primary outcome is the main question the study is designed to answer.
the accuracy of participants in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey
Measured: From enrollment to the end of test at 1 hour.
Secondary outcomes (2)
the accuracy of different subgroups in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey
Measured: From enrollment to the end of test at 1 hour.
Time (in seconds) participants spend per questionnaire between the two study arms.
Measured: From enrollment to the end of test at 1 hour.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2025
- Study start1 Jan 2025
- Primary completion31 May 2025 (estimated)
- Study completion30 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Respiratory DiseasesStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital of Guangzhou Medical UniversityThe sponsor's other registered studies.
- 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
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 2,400 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jianxing He, The First Affiliated Hospital of Guangzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06931782
- Sponsor's protocol ID
- AI-RD-PDT-250323-001
- Status last verified
- Apr 2025
- First submitted
- 26 Mar 2025
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: This study aims to evaluate the effectiveness of artificial intelligence (AI) in the prevention, diagnosis, and triage of respiratory diseases, utilizing a multicenter, randomized controlled trial design. A total of 2400 participants aged 18 to 75 without a medical background will be recruited and randomly assigned to two groups: one group will complete surveys with the assistance of AI tools, while the other group will use standard internet resources to fill out the surveys. The primary outcome measures will include triage accuracy rates, preliminary diagnosis compliance rates, and completeness of risk factor identification, while secondary outcomes will focus on variations in performance across different regions and the lifestyle habits and health indicators of participants. To ensure data quality, training will be conducted at each center, with real-time data entry and auditing processes established. The study plan also includes emergency response protocols and data security manage
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
- ClinicalTrials.gov ID
- NCT06931782
- Acronym / sponsor ID
- AI-RD-PDT-250323-001
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 2,400 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Guangzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 31 May 2025 (estimated)
- Study completion
- 30 Jun 2025 (estimated)
- First posted
- 17 Apr 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.
This study will evaluate the impact of using the GPT-4o compared to traditional online tools in the field of respiratory disease prevention, focusing on the dissemination of knowledge and behavior changes among the general public. We will explore the effectiveness of GPT-4o in enhancing public awareness and management capabilities regarding respiratory diseases and promoting appropriate preventive behaviors.
Detailed description
Artificial intelligence (AI) technologies, particularly advanced large language models like GPT-4o developed by OpenAI, hold immense potential in enhancing public health education and preventive behaviors. Although GPT-4o was not specifically designed for respiratory disease prevention, it has shown promising prospects in numerous healthcare-related applications, such as providing health information, responding to public inquiries, and supporting health education efforts. However, its effectiveness in improving public awareness and management capabilities regarding respiratory diseases remains to be further explored.
Understanding and managing respiratory diseases involve complex processes, including symptom recognition, application of preventive knowledge, and informed decision-making. Integrating AI tools like GPT-4o into public health education could potentially enhance knowledge dissemination, reduce misinformation, and encourage appropriate preventive behaviors among the general population. Nevertheless, GPT-4o has not been specifically validated for respiratory disease prevention and carries the risk of generating misleading or inaccurate information, which could confuse users. Improper use of such tools may fail to raise awareness and could even lead to counterproductive behaviors. Therefore, studying how large language models like GPT-4o can effectively support public education and behavior change in this context is of critical importance.
In this study, participants will be randomly divided into two groups: one group will have access to Fine-turned GPT-4o, while the other will rely solely on traditional online tools. They will be presented with scenarios related to respiratory diseases and asked to explain their identification of high-risk factors, understanding of diagnoses, and proposed triage actions for each scenario. Each scenario was developed by a panel of three experts in respiratory health, who also established standardized answers. Responses will be evaluated by two independent groups of reviewers unaware of the participants' group assignments. These experts independently created initial scoring criteria and resolved discrepancies through multiple rounds of discussion to ensure consistency and accuracy.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Group | Experimental | Other: AIParticipants completed the questionnaire using AI-driven tools for content generation and information retrieval. |
| Internet-Based Group | No intervention | –Participants completed the questionnaire using standard internet search engines for information retrieval. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- No medical background
- Aged from 18 to 75 years old
Key exclusion criteria (3 listed)
- Had a medical background
- Exceeds the age criteria
- Failed to comply with the survey requirements
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 |
|---|---|---|
| AIGPT-4o fine-tuned with the Lungdiag database | Other | AI-Assisted Group |
Primary outcome
the accuracy of participants in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey
Time frame: From enrollment to the end of test at 1 hour.
2 secondary outcomes
the accuracy of different subgroups in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey
Time frame: From enrollment to the end of test at 1 hour.
Time (in seconds) participants spend per questionnaire between the two study arms.
Time frame: From enrollment to the end of test at 1 hour.
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 17 Apr 2025.
- Individual participant dataSharing planned: Yes. This study aims to evaluate the effectiveness of artificial intelligence (AI) in the prevention, diagnosis, and triage of respiratory diseases, utilizing a multicenter, randomized controlled trial design. A total of 2400 participants aged 18 to 75 without a medical background will be recruited and randomly assigned to two groups: one group will complete surveys with the assistance of AI tools, while the other group will use standard internet resources to fill out the surveys. The primary outcome measures will include triage accuracy rates, preliminary diagnosis compliance rates, and completeness of risk factor identification, while secondary outcomes will focus on variations in performance across different regions and the lifestyle habits and health indicators of participants. To ensure data quality, training will be conducted at each center, with real-time data entry and auditing processes established. The study plan also includes emergency response protocols and data security manage
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI in people with Respiratory Diseases and Artificial Intelligence (AI), run by The First Affiliated Hospital of Guangzhou Medical 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 2 inclusion and 3 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: enrolling by invitation, as of the sponsor's last update on 17 Apr 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?
the accuracy of participants in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey (time frame: From enrollment to the end of test at 1 hour.). 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 17 Apr 2025.