The increasing availability of large-scale healthcare data and the rapid development of artificial intelligence (AI) techniques are reshaping clinical practice, with particular relevance for acute and emergency care settings. Big data derived from electronic health records, imaging, monitoring systems, and administrative databases provide the foundation for AI-driven models that support clinical prediction, prognosis, early warning systems, and decision-making under time pressure. When appropriately designed and governed, these tools have the potential to enhance communication among healthcare professionals, optimize care pathways, and improve patient outcomes. However, the effective and responsible implementation of AI based on big data requires robust data management strategies, appropriate healthcare infrastructure, and interdisciplinary collaboration. Challenges related to data quality, heterogeneity, bias, model interpretability, statistical limitations, and generalizability must be addressed to ensure clinical reliability and safety. In parallel, the adoption of AI in healthcare raises important ethical considerations, including patient autonomy, fairness and equity, transparency, data privacy, and accountability. This chapter provides an educational overview of the sources and management of big data in healthcare, the fundamental principles of AI modeling, and representative clinical applications, while critically discussing the technical, ethical, legal, and organizational challenges associated with their use. Emphasis is placed on fostering a transparent, ethical, and sustainable digital healthcare ecosystem in which multidisciplinary collaboration and continuous learning are essential to translate AI innovations into meaningful improvements in patient care.