AI in Respiratory Disease Prevention, Diagnosis, and Triage
Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
Study Overview
Status
Intervention / Treatment
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 Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510120
- the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- No medical background
- Aged from 18 to 75 years old
Exclusion Criteria:
- Had a medical background
- Exceeds the age criteria
- Failed to comply with the survey requirements
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Prevention
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: AI-Assisted Group
Participants completed the questionnaire using AI-driven tools for content generation and information retrieval.
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GPT-4o fine-tuned with the Lungdiag database
|
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No Intervention: Internet-Based Group
Participants completed the questionnaire using standard internet search engines for information retrieval.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
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.
|
From enrollment to the end of test at 1 hour.
|
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
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.
|
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.
|
From enrollment to the end of test at 1 hour.
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AI-RD-PDT-250323-001
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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