Cybersecurity and Intraoperative Artificial Intelligence Decision Support

Abstract

Every aspect of healthcare has been touched by the digitization of care and delivery; cybersecurity is a critical concern in this regard, and many factors are responsible for the vulnerability of hospital systems to cyber-attacks. Forms of cyber-attacks such as ransomware attacks disable EMR (electronic medical report) databases and workstations. In many cases, the criminals offer the restoration with a payment, and many have paid the criminals. Advances in the medical field with the use of robotics and artificial intelligence are then introduced to the equipment and instrumentations used. However, a major drawback here is human error and mishandling during surgery, which leads to serious consequences later. In automated case retrieval, help is provided using real-time surgical video aid in intraoperative warnings and recommendations. Throughout the healthcare system, methods for keeping EHRs has proliferated and large, diverse datasets produced by the resulting EHR databases present new prospects for developing better healthcare systems. Despite the tremendous potential of AI techniques to improve patient care, several significant barriers to clinical adoption still exist. Many data-driven pharmaceutical dosing models have come into existence, but their capacity to recognize inter-individual variations and calculate customized doses is restricted. Recently, reinforcement learning and deep reinforcement learning have been applied in many clinical settings, such as the best timing for interventions, the best drug dosage, and the best individual target laboratory values. Numerous guidelines and principles are developed for the use of AI in both the public and private sector and research facilities, but no consensus can be found in its definition and therefore health regulatory authorities are responsible for giving the warrant in safety, efficacy, and proper use of AI in healthcare and therapeutic development. The ethical principles upon which AI are based is on inherent worth of humanity and dignity so as to guide developers, regulators, and users in considering them and improving such technologies. AI should be used by health professionals responsibly as it is the responsibility of humans to ensure that AI technologies perform at their optimum level. Also, when the context of healthcare arises, it should result that humans are in full control of medical decisions and healthcare systems. Duties for protecting confidentiality and privacy and to provide informed valid consent within the existing legal framework of data protection falls under the domain of respect for autonomy. The responsiveness of AI technologies can only be achieved when developers, designers, and users continuously in a systematic and transparent way scrutinize for accurate, appropriate responses by the AI and to validate if the response received is at par with the requirement and expectations and in the purpose for which it was used. The gap of “digital divide” between and within the countries so that equitable access to AI is made has to be ensured by government and industries. A continuous review is required for the security aspect in AI application in the medical industry. In the case of AI, doctors must consider the causes and effects of medical issues as well as the methods and models employed to aid in the decision-making process. In the future, when updated with more advanced technology and given access to more full data, AI technologies will be able to detect a variety of additional ailments. In this chapter, these ethical aspects, government role, operative decision-making along with the challenges which arise as a result of using AI in healthcare are discussed.

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