Abstract: The rapid evolution of digital health technologies has laid the foundation for a transformative shift toward artificial intelligence (AI)–driven healthcare systems. While early digital health solutions primarily focused on data digitization, connectivity, and remote care delivery, recent advancements in AI have enabled more intelligent, adaptive, and predictive models of care. This transition represents a paradigm shift from reactive and standardized healthcare toward proactive, personalized, and precision-driven interventions. AI-driven care leverages machine learning algorithms, big data analytics, and real-time clinical decision support systems to enhance diagnostic accuracy, optimize treatment pathways, and improve patient outcomes. However, the integration of AI into healthcare ecosystems also introduces significant challenges related to data quality, interoperability, algorithmic bias, explainability, ethics, and governance. This paper examines the progression from traditional digital health technologies to AI-enabled care models, highlighting key technological enablers, clinical applications, and system-level impacts. Furthermore, it explores the implications for healthcare professionals, patients, and policymakers, emphasizing the need for robust regulatory frameworks and human-centered AI design. Understanding this transition is critical for ensuring that AI-driven care augments clinical expertise, enhances patient trust, and delivers equitable and sustainable healthcare solutions.

Keywords: Digital health, Artificial intelligence in healthcare, AI-driven care, Clinical decision support systems, Machine learning, Predictive analytics, Personalized medicine, Precision healthcare, Health informatics, Electronic health records (EHRs), Big data in healthcare, Remote patient monitoring, Telemedicine, Automation in healthcare, Ethical AI, Explainable AI, Healthcare transformation, Patient-centered care, Interoperability, Data governance.

Downloads: | DOI: 10.17148/IJIREEICE.2022.101221