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2023.04.29湖北
图1.AI在传染病监测中的各种功能
AI在疾病监测中的应用
预警
图2.HealthMap使用自然语言处理方法分类传染病病例报告的范例
病原体分类
图3.应用AI测量抗生素敏感性的手机应用程序范例
源头识别
风险评估
图4.通过强化学习方法进行边境COVID-19监测范例
扩展应用
图5.AI和机器学习将个人行为转化为人群健康信息
多种（未列举全部）AI和机器学习算法（此处分成监督分类算法或人工神经网络）及人工方法可在假设的呼吸道病毒暴发中应用。个体事件汇总之后，可产生信号，提示人群可能发生传染病。检测到的信号用于确定可采取的监测措施。每种方法各有优缺点，相互结合之后，这些算法构成了检测和应对疾病暴发的系统。CCTV表示闭路电视。
监测工作的障碍和未来方向
数据的质和量
数据源的代表性
隐私
AI的极限
传染病监测工作的未来将运用新兴技术形式，包括不限于生物传感器、量子计算和增强智能。大型语言模型的最新进展（如GPT-4[基于转换器的生成式预训练模型]）为传染病监测工作的未来展现出广阔前景，因为这些模型可以处理和分析大量非结构化文本，并且可能增强我们简化劳动密集型流程和发现隐藏趋势的能力。那些尚未发明的新技术也必将在未来有所作为。然而，在COVID-19疫情期间，我们的现有方法受到了考验，而它们的性能差异很大。AI驱动的下一代监测工具要取得成功，很大程度上将取决于我们是否有能力解决当前算法的缺点、判断哪些成就可以推广，以及将许多经验教训融入未来行动。
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