AI in Healthcare: Governance and Security Challenges

AI has been implemented within healthcare to perform tasks such as medical image analysis, disease progression prediction, patient care communications, and administrative support. They may increase efficiency and reliability for a particular task; however, they are prone to regulatory issues due to privacy concerns with patient data; they can also generate mistakes that are not immediately obvious. The paper discusses issues related to management and safety concerns for AI healthcare systems during all phases of their existence. Privacy issues, data security concerns, fairness issues, transparency problems, accountability problems, cyber threats, and operational monitoring. Based on these issues, a five-stage governance framework is proposed: identification and risk classification, assessment before deployment, establishment of controls, operational monitoring, and review or retirement. Responsibilities are assigned to clinical staff, technical staff, privacy officers, legal advisors, compliance officers, management personnel, and patients’ care providers. This article states that approval for deployment is inadequate. The healthcare AI needs to be monitored for changes to information, algorithms, programs, processes, and medical applications. Thus, it is a managerial task that involves patient care and security issues; it also includes suspending/withdrawing AI systems if needed.