Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 2,400
- 试验地点
- 1
- 主要终点
- 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
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 75 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •No medical background
- •Aged from 18 to 75 years old
排除标准
- •Had a medical background
- •Exceeds the age criteria
- •Failed to comply with the survey requirements
研究组 & 干预措施
AI-Assisted Group
Participants completed the questionnaire using AI-driven tools for content generation and information retrieval.
干预措施: AI (Other)
Internet-Based Group
Participants completed the questionnaire using standard internet search engines for information retrieval.
结局指标
主要结局
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
时间窗: From enrollment to the end of test at 1 hour.
次要结局
- 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(From enrollment to the end of test at 1 hour.)
- Time (in seconds) participants spend per questionnaire between the two study arms.(From enrollment to the end of test at 1 hour.)
研究者
Jianxing He
Professor
The First Affiliated Hospital of Guangzhou Medical University
