This open access book offers a comprehensive exploration of digital health and care, addressing the profound transformations driven by digital technologies in the healthcare sector. It serves as a vital resource for understanding the evolution, impact and future directions of digital health innovations, providing readers with in-depth knowledge of regulatory frameworks, adoption strategies and patient-centric approaches. Drawing on perspectives from hospital care, ambulatory services, and long-term care, the book offers practical insights into implementation strategies, organizational change, and sector-specific challenges.

Digital health technologies are advancing rapidly and are beginning to reshape how care is accessed, delivered, and financed, creating new possibilities for more timely, patient-centered, and resilient health systems. At the same time, their large-scale adoption is constrained by persistent barriers such as unequal access and digital literacy, unresolved questions around data privacy, security and ownership, limited interoperability between fragmented systems, cultural resistance within organizations, and uncertain reimbursement and sustainability models. This chapter offers a concise map of this landscape by outlining key challenges, including the digital divide, governance of health data, integration and interoperability, change management, “pilotitis,” and ethical risks, as well as major opportunities, from AI-enabled decision support and personalized medicine to real-time analytics, value-based care, and gamified approaches to engagement. It also highlights how these issues intersect across regulatory frameworks, implementation strategies, equity considerations, and emerging innovation paradigms discussed in later chapters. It argues that the same technologies that can deepen fragmentation and inequity, if poorly governed, can also be harnessed through deliberate policy, design, and business models to generate durable, system-wide benefits.

This chapter addresses the ethical considerations essential for the responsible use of AI in healthcare, with a focus on marginalized populations such as ethnic minorities, people with disabilities, and underserved communities. It highlights key dimensions including data equity, algorithmic fairness, explainability, participatory design, and governance for accountability. The chapter emphasizes that AI systems must be trained on diverse and representative data to avoid perpetuating biases that disproportionately harm marginalized groups. Algorithmic fairness requires ongoing bias detection and mitigation, combined with transparent and interpretable AI models that build trust. Inclusive development through community participation ensures AI tools reflect the needs and values of those they serve. Strong governance frameworks, aligned with regulatory standards like the EU AI Act, are necessary to enforce accountability and protect vulnerable populations. When ethically implemented, AI can improve access, personalize care, and support proactive health management for underserved groups. However, the chapter cautions that these benefits depend on continuous commitment to equity, collaboration among stakeholders, and ongoing dialogue with affected communities to prevent exacerbating existing health disparities.