This study analyzes the application of artificial intelligence (AI) in improving hospital quality management through a systematic review of 31 scientific articles indexed in Scopus. An exploratory methodology was used with selection criteria based on recency, thematic relevance, and methodological rigor. The research identifies the main applications of AI in the automation of clinical and administrative processes, clinical decision support, triage optimization, and detection of adverse events. The most widely used technologies include machine learning, deep neural networks, expert systems, and natural language processing. The results show measurable improvements in operational efficiency, diagnostic accuracy, patient safety, and strategic hospital planning. However, significant challenges remain regarding system interoperability, data quality, staff training, and ethical implications of automated decision-making. The study concludes that AI is a key tool for advancing toward more intelligent and quality-focused hospital models, although its adoption requires comprehensive strategies that address technical and regulatory barriers to ensure ethical, safe, and sustainable implementation.
Ricardo García García (Sun,) studied this question.