Integrating Invasive and Noninvasive Imaging for Coronary Atherosclerosis: A Systematic Review With Pragmatic Algorithms From Anatomy to Physiology.

The intensive care unit (ICU) represents one of the most data-intensive environments within healthcare systems, characterized by continuous monitoring and the generation of vast volumes of heterogeneous clinical data. This unique setting offers unparalleled opportunities for the application of artificial intelligence (AI) technologies aimed at enhancing patient care. Recent bibliometric analyses have documented a steep increase in AI-related research in intensive care medicine, particularly since 2018, with the United States and China leading contributions and a focus on neural networks, decision support systems, machine learning, and deep learning techniques (). The ICU’s complex and dynamic clinical scenarios, involving rapid changes in patient status and multifaceted interventions, create a fertile ground for AI to support diagnosis, monitoring, prognostication, and workflow optimization. However, despite the proliferation of AI research, the translation of these advances into routine clinical practice remains limited, underscoring the need for comprehensive evaluations of AI integration in critical care (). AI applications in the ICU have evolved beyond early warning systems and sepsis prediction to encompass a broad spectrum of clinical domains. These include prediction models for mechanical ventilation weaning, acute kidney injury alerts, and personalized treatment recommendations tailored to individual patient physiology and disease trajectories. For instance, machine learning models such as the Hemodynamic Stability Index (HSI) have demonstrated superior predictive performance for hemodynamic instability compared to traditional single-parameter indicators, enabling earlier identification of patients at risk and potentially guiding timely interventions. Similarly, AI-driven models have been developed for real-time acuity assessment and prediction of life-sustaining therapy requirements, integrating diverse data streams including vital signs, laboratory results, and medication profiles, thereby enhancing clinical decision-making in rapidly evolving ICU contexts (). Moreover, AI has shown promise in specialized ICU subfields such as cardiac critical care, where its capacity to analyze large datasets in real-time can assist in managing complex cardiovascular conditions (). These advancements illustrate the expanding scope and sophistication of AI tools in critical care, moving toward comprehensive, multimodal data integration and personalized medicine. Despite encouraging retrospective validation results, the widespread clinical adoption of AI in the ICU faces significant challenges. Many AI models remain at the experimental stage, with limited prospective multicenter validation and heterogeneous performance reporting. Issues such as data quality, interoperability, and the inherent “black-box” nature of many AI algorithms hinder clinician trust and acceptance. Furthermore, the ICU environment poses unique obstacles, including the need for real-time data access, integration into clinical workflows, and adaptation to individual patient responses. Ethical and legal considerations, such as fairness, transparency, and accountability, are paramount given the high-stakes nature of critical care decisions (). Additionally, the lack of standardized endpoints and calibration metrics complicates the assessment and comparison of AI tools, while logistical barriers related to data sharing and governance limit the generalizability and robustness of predictive models. These multifaceted challenges necessitate a systematic and multidisciplinary approach to AI development, validation, and implementation in the ICU.