Clinical trial NCT06931782: AI in Respiratory Disease Prevention, Diagnosis, and Triage
Official title: Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Respiratory Diseases
Artificial Intelligence (AI)
Randomized Controlled Trial
Lead sponsor: The First Affiliated Hospital of Guangzhou Medical University
Sponsor class: OTHER
First posted: 2025-04-17
Enrollment: 2400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief 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. 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.

Eligibility criteria: 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

Primary outcomes:
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
timeFrame: From enrollment to the end of test at 1 hour.
Secondary outcomes:
measure: 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
timeFrame: From enrollment to the end of test at 1 hour.
measure: Time (in seconds) participants spend per questionnaire between the two study arms.
timeFrame: From enrollment to the end of test at 1 hour.
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120
state: Guangdong
Study officials:
References: