Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)
This addresses cybersecurity and logistical challenges in healthcare systems during pandemics, but appears incremental as it builds on existing concepts and data.
The paper tackles securing healthcare systems for future pandemics by developing autonomous AI concepts using real-time edge device data, constructing two case scenarios for predictive cyber risk analytics and supply chain forecasting based on COVID-19 data.
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.