The Ideal Human Care in Green ICU: An integrated AI framework for future ICU care

Background: Intensive Care Units (ICUs) present a high-stakes environment where timely decision-making is critical for managing patients with life-threatening conditions. The continuous influx of complex data often challenges clinicians, increasing the risk of errors. Artificial Intelligence (AI) offers transformative potential to enhance ICU care by supporting data analysis, decision-making, and workflow efficiency. Objective: This review aims to explore current applications of AI in ICUs, assess their impact on clinical outcomes, workflow optimization, and ethical considerations, and propose an Integrated AI Framework for enhanced critical care delivery. Methods: A literature search was conducted across PubMed, Scopus, and Web of Science, focusing on studies published between 2014 and 2024. The data were synthesized using an inductive thematic analysis approach to evaluate AI's impact on clinical outcomes and to identify key barriers to its integration. Results: AI has demonstrated significant advancements in ICU care, including early detection of sepsis, prediction of cardiac arrest, and workflow optimization through decision support systems. Predictive models reduced sepsis-related mortality by up to 20%, while workflow enhancements improved medication accuracy by 30% and reduced adverse events by 25%. Advanced techniques such as natural language processing (NLP), large language models (LLMs), and multimodal data integration have further streamlined ICU operations. However, challenges remain, including algorithmic bias, data privacy concerns, and integration barriers. Proposed Model: The Ideal Human Care in Green ICU model integrates advanced AI technologies with multidisciplinary collaboration to provide personalized, evidence-based, and patient-centered care. This model emphasizes ethical AI practices, transparency, and family engagement to ensure responsible implementation. Conclusion: AI can transform ICU care by improving outcomes and workflows, but ethical, practical, and explainable challenges must be addressed through diverse, validated research. Implications for clinical practice: AI integration in ICUs improves patient outcomes and workflows by enabling early detection and precise treatment, but ethical issues and real-world validation are crucial.