评估生成人工智能在卫生政策识别和解释方面的局限性
Rory Wilson1, Ciara M Weets1, Amanda Rosner1
1Georgetown University Center for Global Health Science and Security, Washington, DC, United States of America.
PloS one
|December 12, 2024
概括
生成型人工智能 (GAI) 工具显示出加速卫生政策分析的潜力,但需要进一步开发以获得准确性,特别是在全球不同地区和政策解释中.
科学领域:
- 政策流行病学 政策流行病学
- 卫生政策分析 卫生政策分析
- 人工智能在公共卫生中的作用
背景情况:
- 政策流行病学依赖于人类主题专家 (中小企业) 进行系统的政策分析.
- 新出现的传染病需要各国全面评估卫生政策.
- 生成型人工智能 (GAI) 工具为政策研究减少资源提供了潜力.
研究的目的:
- 评估GAI工具在确定和解释卫生政策方面与中小企业相比的准确性和精度.
- 评估GAI在大规模卫生政策数据收集和分析项目中的实用性.
主要方法:
- 一项比较研究评估了GAI工具对中小企业分析的反应.
- 利用了两个经过验证的政策数据集:联合国会员国的紧急/儿童疫苗接种和隔离/隔离政策.
- 分析了世卫组织各地区的一致率,数据收集速度和系统性不准确性.
主要成果:
- 在疫苗接种方面,SME和GAI的一致性为78.09%,在隔离/隔离政策方面为67.01%.
- GAI显著加速了数据收集,但表现出系统的不准确性,特别是在非洲和东地中海地区.
- 在政策解释任务中,GAI与中小企业的一致性最低.
结论:
- 对于准确的全球卫生政策识别和细微的解释,GAI工具需要大量开发.
- GAI证明了其作为卫生政策确定质量保证工具的实用性.
- 需要进一步的研究来完善GAI,以便进行复杂的公共卫生政策分析.
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