This thesis critically examines the evolving role of artificial intelligence (AI) and machine learning (ML) in shaping personalized approaches to the prevention,diagnosis, and management of chronic diseases such as hypertension (HTN), chronickidney disease (CKD), and type 2 diabetes mellitus (T2DM). By utilizing diverse MLmodels and large-scale clinical and omics datasets, the study aims to highlight thepossibilities of further integration of AI in healthcare patient stratification, riskprediction, and tailored treatment strategies. Emphasis is placed on continuousmonitoring of biometric data using AI-enabled wearables, patient phenotypeclassification, and the prediction of disease progression, all of which contribute tmoreprecise, data-driven and individualized interventions The study further explores how AI-enabled wearables and approaches can enhance real-time monitoring and proactive management of these diseases. Ethical, legal, and socialimplications, including data privacy, bias, transparency, and patient autonomy, are also critically examined to ensure responsible deployment of AI in clinical settings. Ultimately, this thesis highlights the transformative potential of artificial intelligence inenabling personalized and effective healthcare approaches, while also maintaining acritical awareness of the significant bioethical risks and challenges involved.
Μαρία Παρθένα Ι. Ξενιτοπούλου (2025) studied this question.