Leveraging artificial intelligence for enhanced and human-centered healthcare solutions

Artificial Intelligence (AI) is increasingly recognized as a transformative force in healthcare, offering unprecedented opportunities to enhance disease diagnosis, management, and prevention. This PhD thesis is rooted in two fundamental research areas: the application of AI to health and epidemiological data for the purposes of disease prevention and monitoring, and the utilization of AI techniques for the analysis of bioelectrical signals to support clinical decision-making. The first research area delves into the sophisticated analysis of extensive health and epidemiological datasets using cutting-edge machine learning (ML) methodologies. The objective is to uncover significant patterns that can inform and improve the prevention and management of chronic diseases. By identifying these patterns, the research enables the creation of personalized intervention strategies tailored to individual patient profiles, while also optimizing disease management on a broader, population-wide scale. This approach not only contributes to the advancement of public health but also sets the stage for more proactive healthcare practices. The second research focus of this thesis explores the development and application of advanced ML and deep learning (DL) models for the interpretation of bioelectrical signals, such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It is important to point out that non-invasive technologies such as brain-computer interfaces (BCIs) were used for the analysis of EEG signals. The AI-driven models developed in this PhD thesis aim to enhance the accuracy and reliability of medical diagnostics, facilitating more precise and personalized clinical decisions. The integration of these models into clinical workflows has the potential to revolutionize patient care by providing healthcare professionals with powerful tools for diagnosis and treatment planning. The practical outcomes of this research are profound, offering novel tools and frameworks that bridge the gap between AI innovation and clinical application. By incorporating explainable Artificial Intelligence (XAI) principles, the models developed in this thesis are designed to be transparent and interpretable, ensuring that healthcare professionals can trust and effectively use these advanced technologies in their daily practice. In summary, this PhD thesis makes significant contributions to the intersection of AI and medicine, addressing key challenges in the interpretation of health and epidemiological data as well as the analysis of bioelectrical signals. The findings presented here lay a robust foundation for future advancements in personalized medicine and public health, ultimately aiming to improve patient outcomes and the overall efficacy of healthcare systems. All contributions made in this thesis are detailed in the respective chapters, providing a comprehensive overview of the research conducted and its impact on the field of AI in healthcare.