AI applications in acute care showed potential but face significant implementation challenges.

Photo by Maskmedicare Shop / Unsplash

Frontiers in Medicine Published April 11, 2026 Medically reviewed April 25, 2026 Study authors: Tianle Gao, Tingting Fan, Wenying Leng, Shenghui Yu, Qingguo Lyu DOI ↗ By Dr. Lars van Dijk, PhD · Surgical, Procedural & Diagnostic

Key Takeaway

Note that AI implementation in acute care requires addressing data quality, validation, and ethical issues beyond predictive performance.

This narrative review evaluated the applications, clinical benefits, and implementation challenges of artificial intelligence (AI) within acute and critical care settings. The evidence base comprised 281 English and 136 Chinese studies involving patients in emergency departments and intensive care units. Conditions examined included sepsis, chest pain, stroke, acute coronary syndrome, ARDS, and cardiogenic shock.

The review found that AI showed potential across multiple clinical scenarios. However, the translation of these technologies into real-world practice was limited by major challenges. These obstacles included poor data quality, heterogeneity and fragmentation of clinical data, and limited model explainability. Furthermore, insufficient prospective validation and workflow integration barriers were identified as significant hurdles.

Additional limitations noted in the review included clinician training gaps and unresolved ethical and regulatory issues. Safety data, adverse events, and tolerability were not reported in the source material. The authors emphasize that safe and effective implementation requires trustworthy systems, stronger data infrastructure, interdisciplinary collaboration, and clearer ethical and regulatory oversight.

Given the heterogeneity of the included studies and the nature of the narrative review, the certainty of these findings is limited. The practice relevance suggests that while AI offers promise, its adoption must address foundational issues beyond simple predictive accuracy to ensure safety and efficacy in complex clinical environments.

Study Details

Study typeCohort

EvidenceLevel 3

PublishedApr 2026

View Original Abstract ↓

The Emergency Department (ED) and Intensive Care Unit (ICU) are high-acuity environments where rapid decision-making and clinical precision are fundamental to patient survival. Artificial Intelligence (AI) offers transformative potential by providing real-time data synthesis, advanced pattern recognition, and personalized decision support—capabilities essential for optimizing clinical efficiency and patient outcomes. This narrative review synthesized the applications, clinical benefits, and implementation challenges of AI within acute and critical care. A comprehensive literature search was conducted across international and Chinese databases, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data, yielding 281 English and 136 Chinese studies. Furthermore, official policy documents from provincial Health Commissions were analyzed to evaluate the regulatory landscape for AI deployment. AI showed potential across multiple acute and critical care scenarios, including early warning of sepsis, chest pain assessment, stroke imaging, electrocardiogram interpretation for acute coronary syndrome, ARDS subphenotyping, cardiogenic shock risk stratification, treatment support, and workflow coordination. However, translation into real-world practice remained limited by major challenges, including poor data quality, heterogeneity and fragmentation of clinical data, limited model explainability, insufficient prospective validation, workflow integration barriers, clinician training gaps, and unresolved ethical and regulatory issues. Medical AI held substantial promise for improving decision-making efficiency, workflow optimization, and patient outcomes in acute and critical care. However, its safe and effective implementation required more than predictive performance alone. Future progress depended on trustworthy, explainable, workflow-integrated, and prospectively validated systems supported by stronger data infrastructure, interdisciplinary collaboration, and clearer ethical and regulatory oversight. This review proposed a translational framework and a general workflow for data-driven AI development in acute and critical care.