Artificial Intelligence in the Intensive Care Unit: Present and Future
DOI:
https://doi.org/10.56294/mw2025464Keywords:
Artificial Intelligence, Intensive Care Unit, Critical Medicine, Machine Learning, Precision MedicineAbstract
Introduction: Artificial intelligence (AI) is significantly transforming critical medicine and intensive care. Its ability to process large volumes of data and generate accurate predictions has improved medical decision-making, optimizing diagnosis, treatment, and reducing the workload of healthcare personnel. Methodology: A literature review was conducted between November 2024 and February 2025, consulting databases such as SciELO, LILACS, Scopus, PubMed-MedLine, Google Scholar, and ClinicalKeys. Original articles, case reports, and open-access systematic reviews from the last 5 years were selected, using descriptors in Health Sciences (DeCS) and Boolean operators for the search. Development: Current applications of AI in the ICU include: Monitoring and early detection of adverse events using sensors and machine learning algorithms; diagnosis and prognosis through deep neural networks for medical image interpretation; treatment optimization, including adjustments in mechanical ventilation and pharmacogenomics; efficient management of hospital resources. The future of AI in critical care is oriented towards more explanatory and transparent systems, personalized precision medicine, integration with emerging technologies and automation of clinical processes. Conclusions: Artificial intelligence (AI) is redefining care in intensive care units, improving diagnostic accuracy, optimizing treatments, improving clinical decision-making and thus allowing more efficient hospital management. However, as advanced as it is, it will never replace the empathy and clinical judgment of healthcare professionals. By integrating AI responsibly, we not only save more lives, but we also humanize critical patient care, always remembering that, at the heart of intensive medicine, there is compassion and commitment to each patient.
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Copyright (c) 2025 Jhossmar Cristians Auza-Santivañez, Ariel Sosa Remón, Freddy Ednildon Bautista-Vanegas , Ingrid Neysa Cabezas-Soliz, Ismael Vargas Gallego, Blas Apaza-Huanca, Jorge Márquez-Molina, Daniel Ramiro Elías Vallejos-Rejas (Author)
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