<p>The integration of Artificial Intelligence (AI) has become essential in life sciences research. Scientific literature actively explores AI technologies, their strengths, ethical considerations, application fields, and future developments. In this framework, this paper analyzes academic literature on AI in healthcare, pursuing four research directions: a) providing a comprehensive overview of AI advancements in healthcare; b) identifying key themes related to technologies and application areas; c) classifying scientific literature based on the semantic labels from point b); d) determining the sustainability implications of AI in healthcare. A corpus of abstracts published between 2000 and 2023 in the Web of Science database was analyzed. A methodological framework using natural language processing (NLP) was implemented through a topic model in embedding spaces. The trend shifted from image analysis for diagnosis and treatment in the early 2000&#xa0;s to predictive models for fields such as ophthalmology, internal medicine, and oncology. New technological systems have emerged to support computer-assisted tools, including chatbots, telemedicine, and AI-driven administrative tasks. Sustainability implications were detected across technological, environmental, and social dimensions. Machine learning and deep learning optimize hospital management, diagnostics, and resource allocation, reducing costs and waste. Robotics enhances surgical precision and patient care, while NLP improves data analysis and decision-making. Environmentally, AI-driven telemedicine reduces the ecological footprint, supporting climate goals. Socially, AI fosters healthcare equity by personalizing treatments and addressing ethical concerns related to data transparency. The categorization of the literature was evaluated by comparing two machine learning models, Support Vector Machine and Random Forest, providing insights into the current and future directions of AI in healthcare.</p>