AI in Personalized Medicines: Opportunities and Challenges

Abstract

The integration of Artificial Intelligence (AI) in personalized medicine has revolutionized healthcare by enabling precise, data-driven, and patient-specific treatment strategies. Artificial intelligence (AI) has the potential to revolutionize healthcare by enabling personalized medicine and improving disease diagnosis. AI-powered algorithms, particularly those leveraging machine learning (ML) and deep learning (DL), have enhanced the ability to analyse vast datasets, uncover hidden patterns, and generate predictive models that facilitate early disease detection, drug discovery, and customized treatment regimens. Emerging technologies such as explainable AI (XAI) aim to enhance transparency in decision-making, allowing physicians and patients to better understand AI-generated recommendations. The convergence of AI with other innovations, such as blockchain for secure data management and the Internet of Medical Things (IoMT) for real-time patient monitoring, further strengthens its role in personalized medicine. In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote “next-generation” medicine in 2030 more firmly centred on the individual’s personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. This includes engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy. This paper explores the transformative potential of AI in personalized medicine, analysing its key applications, limitations, and future prospects. A thorough examination of current AI-driven methodologies, case studies, and policy considerations will provide a holistic understanding of the evolving landscape.