Radanliev Petar, De Roure David
Department of Engineering Sciences, University of Oxford, OX1 3QG Oxford, England, UK.
Health Technol (Berl). 2022;12(5):923-929. doi: 10.1007/s12553-022-00691-6. Epub 2022 Aug 12.
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms - i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
本文增进了关于教学和培训新人工智能算法的知识，以保障、准备并使医疗保健系统适应未来大流行。核心目标是开发一个由自主人工智能支持的概念性医疗保健系统，该系统可使用具有实时数据的边缘健康设备。本文构建了两个案例场景，用于将网络安全与自主人工智能应用于：（1）在X疾病事件（即未定义的未来大流行）期间对医疗保健系统故障进行自我优化的预测性网络风险分析，以及（2）对未来大流行期间医疗生产和供应链瓶颈进行自适应预测。为构建这两个测试场景，本文以新冠疫情为例为算法合成数据——即为预期的X疾病优化和保障数字医疗保健系统。测试场景旨在通过优化算法应对疫苗分发复杂生产和供应链的物流挑战及中断情况。
