The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis

Background: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity, mortality, and annual healthcare costs exceeding $81 billion. Communication barriers between sedated or mechanically ventilated patients, their families, and multidisciplinary teams frequently delay recognition and impair management of delirium. This systematic review examines how AI-driven communication technologies can address these barriers, enhance early detection, and promote more integrated, patient- and family-centered delirium care. Methods: A systematic review of literature published between 2015 and 2025 was conducted across five electronic databases: Scopus, PubMed, Web of Science, Embase, and IEEE Xplore. The search strategy employed keywords as “delirium,” “intensive care,” “artificial intelligence,” “AI-driven communication technologies”, “natural language processing”, “computer vision”, “multidisciplinary clinical collaboration”, and “family engagement”. Studies were eligible for inclusion if they focused on AI-enhanced communication in ICU delirium care. The included studies were analyzed using an inductive thematic synthesis approach. Results: From 87 screened records, 16 studies demonstrated AI’s significant benefits across three clinical domains: 1) Prevention using AI-driven tools; 2) Early Detection via multimodal AI systems; and 3) Patient Care through Natural Language Processing (NLP)-powered support. An inductive thematic synthesis of these findings further delineated six core thematic domains: (1) inherent communication barriers; (2) AI as a multidirectional interface; (3) passive AI listening for early detection; (4) AI-enhanced family engagement; (5) AI-structured handovers for teamwork; and (6) ethical-regulatory-practical challenges. Conclusion: AI-driven communication tools effectively bridge critical gaps in ICU delirium care, facilitating early detection, prevention, and patient-centered management. By enabling proactive interventions and fostering a collaborative care environment, these technologies demonstrate direct potential to reduce delirium duration, decrease antipsychotic use, improve long-term cognitive outcomes, and alleviate the substantial economic burden on healthcare systems. These findings validate AI’s role in transforming delirium care through enhanced multidirectional communication. Implications for Clinical Practice: ICU nurses are pivotal in utilizing AI tools through interpreting NLP-generated alerts, calibrating computer vision outputs, and facilitating family engagement to translate AI insights into empathetic, tailored bedside interventions, thereby reinforcing human-AI collaboration.