Securing Healthcare in the Age of AI: A Comprehensive Review of Cybersecurity Challenges and Solutions

Abstract

AI in the context of healthcare can be defined as a process of broadening the options on the improvement of the patients’ quality of their treatment, an increase in productivity and decrease in the rates of diagnosis failure. But there are huge cyber risks, including the protection of patients’ data and information, the model it adopted in AI, and primarily the security risks of healthcare networks. This review looks at the policy, which applies to the AI healthcare cybersecurity, how health data security is addressed, how the core of the AI model is protected, the issue of access, and lastly, monitoring as the final control. Subsequently, it presents new directions and opportunities in AI-based healthcare cybersecurity such as federated learning and homomorphic encryption and block chain. In addition, the review also explains the place of explanation and ethical evaluation regarding the construction of trust and accountabilities in AI solutions. Future endeavors imply that with the advance of applications and technologies concerning artificial intelligence, healthcare of today in both its practical implementation and future research, modern care providers, AI software developers, legislators, and IT security experts must cooperate to identify existing and potentially malicious threats towards introducing artificial intelligence into the healthcare systems of the future safely, securely, and in compliance with certain ethical standards. When these firms apply IT technical professional in AI to respect the regulations and ethical conducts the fn is subjected to cyber security tests and still maintain the data of the patients require treatment while using AI.