Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)

TL;DR

This paper proposes autonomous AI-driven cybersecurity solutions for healthcare systems, focusing on real-time risk analytics and adaptive supply chain forecasting to better prepare for future pandemics like Disease X.

Contribution

It introduces novel autonomous AI algorithms for predictive cyber risk analysis and adaptive supply chain management in healthcare during pandemics.

Findings

Successful simulation of risk analytics during Disease X scenarios

Effective forecasting of supply chain bottlenecks using AI algorithms

Enhanced preparedness for future pandemics through autonomous systems

Abstract

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.