Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients
Abstract
Artificial intelligence (AI) is now rapidly impacting the medical industry to bring precise medicine from large data analysis for treatment. Over time, AI integration is better at defining the trajectory of treatment based on individual aspects of a patient, including genetic predispositions, habits, and illness history. Nevertheless, the new paradigm for applying AI generates security risks that should be resolved to protect patients and their information. This article presents the imperative terms for safeguarding AI systems in medicine, such as data security and the inviolability of algorithms' AI. Available patient data is very vulnerable to breaches, competitive incursions, as well as unfair suffering from algorithmic bias. To ensure that Personal Health Information is not misused especially where artificial intelligence is being adopted in the health care sector, AI models have to be protected against these threats. Furthermore, as discussed with the advancement of AI technologies in the field, it is necessary to work on safe setups that mean accurate data and confidentiality in patients' data simultaneously with emerging technologies that contribute to developing personalized medical systems. This is done through the science of layout of privacy regulations, ethical standards, and top-of-the-line security measures to prevent the rampant use of AI in healthcare organizations. Thus, security concerns that are tightly associated with the use of artificial intelligence can be centrally controlled, and healthcare by profiting from the opportunities that are presented by artificial intelligence can offer individual treatment and fine tune medical therapy for the patients to the extent that it would optimize the efficiency of medical research. Autonomous cars, smart homes, and embedded systems require security architecture to ensure that patient data is protected and that the data yielded by intelligent algorithms is trustworthy. Future possibilities, current regulatory barriers, and addressing such barriers using AI-secured technologies while improving patient-specific treatments are also examined in this article.
Keywords
Artificial intelligence, Personalized Medicine, Patient Data, Machine Learning, Drug Discovery
Citations
IRE Journals:
Oladoyin Akinsuli
"Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients" Iconic Research And Engineering Journals Volume 8 Issue 2 2024 Page 925-941
IEEE:
Oladoyin Akinsuli
"Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients" Iconic Research And Engineering Journals, 8(2)
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