[论文解读] Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities

Jean Marie Tshimula, Mbowa R. Kalengayi+29 more|arXiv (Cornell University)|Aug 5, 2024

Data-Driven Disease Surveillance被引用 9

一句话总结

本论文综述AI如何提升非洲的公共卫生监测，强调疾病检测、预测、实时报告、案例研究、机会与实施挑战。

ABSTRACT

Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.

研究动机与目标

• 激励在资源有限的非洲卫生体系中使用AI，以加强疾病检测、预测和监测。
• 整理基于AI的在传染病和心理健康领域的应用，以供政策与实践参考。
• 识别在非洲公共卫生环境中AI应用的成功案例与实际障碍。
• 提供可操作的建议，以克服伦理、基础设施和数据质量方面的挑战。

提出的方法

• 评审并综合来自多样数据源（电子健康记录EHR、社交媒体、环境数据、基因组数据）的在非洲的现有基于AI的公共卫生监测应用。
• 将应用分为疾病检测/预测和实时监测/报告，并附带疾病特定的案例研究。
• 突出所引研究中使用的方法学方式（机器学习、深度学习、集成方法、风险评分）。
• 讨论通过AI整合改善医疗服务、资源配置和公平性的机会。
• 概述障碍与伦理考量，并提出应对策略。

Figure 1: Taxonomy of AI applications for public health.

实验结果

研究问题

• RQ1 RQ1：在资源有限地区的公共卫生中，人工智能如何改进疾病检测、预测和监测？
• RQ2 RQ2：在资源有限地区的公共卫生系统中实施AI技术面临的关键挑战和障碍有哪些？有哪些可克服这些挑战的策略？

主要发现

• AI有潜力提高非洲疾病检测和预测的准确性与时效性。
• AI驱动的方法整合多样数据（社会经济、环境、气候、基因组）以为监测和资源分配提供信息。
• 应用覆盖HIV、霍乱、埃博拉、麻疹、结核、流感、寨卡、COVID-19、疟疾、脊髓灰质炎病毒，以及心理健康。
• 案例研究表明AI可以提升HIV检测、耐药性预测、疫情预测以及自测解读。
• 实时AI系统可以支持早期疫情检测和更高效的公共卫生响应。
• 障碍包括数据质量、基础设施、专业知识、数据共享以及伦理考量，并提出了可操作的建议。

Figure 3: Performance metrics of best models per disease for Disease prediction and detection. Note that y-axis indicates Disease + Authors + Model + Metric .