Acute and Critical Care

Review Article

Neurology
Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis: Omofolarin Debellotte, Rachel Melissa Salins, Pragnya Bandari, Maria Gabriela Cerdas, Aijaz Ul Haq, Shaheen Haidrus, Misha Imtiaz, Anietom Ifechukwu Chelsea, Shaik Mohammed Yezdan Ali, Hameeda Abdul Wahab Baloch, Humza Faisal Siddiqui; Acute Crit Care. 2026;41(1):68-86. Published online November 24, 2025; DOI: https://doi.org/10.4266/acc.002200

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Acute care settings, including emergency medicine and intensive care units, comprise a substantial portion of healthcare and are essential in the prompt management of conditions that can prove fatal. Critical care conditions require timely management that can be delayed by high patient volumes and the need for complex clinical decision making. Artificial intelligence (AI) tools have been created to enhance diagnostic accuracy and optimize workflow to improve patient care. This narrative review discusses the current status of AI in acute care, with a focus on its applications in triaging and diagnosis. AI-enhanced electrocardiogram analysis, identification of myocardial infarction and acute coronary syndrome, and heart failure risk stratification led to better patient-specific management and improved results. AI models successfully determined and aided in the timely management of various acute conditions, including pneumonia, pulmonary embolism, and respiratory failure. The AI algorithms used accurately determined sepsis onset and course, superseding traditionally used clinical tools and leading to early diagnosis and reduced sepsis mortality. These models showed high sensitivity and specificity in diagnosing and triaging neurological conditions, including altered levels of consciousness, seizures, and intracranial hemorrhages. AI that involved advanced machine learning imaging software led to faster and more accurate stroke diagnosis. Diagnostic tools assisted by AI improved the detection and classification of acute pancreatitis, appendicitis, and gastrointestinal bleeding. AI has shown promising results in optimizing management in acute care settings. However, critical issues in data standardization, ethical considerations, and clinical workflow integration need to be addressed to enable clinical implementation.

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Insights into Acute Pancreatitis: Pathogenesis, Diagnosis, and Management
Silvia Carrara, Federico Cassano, Maria Terrin, Marco Spadaccini
Journal of Clinical Medicine.2026; 15(8): 2819. CrossRef

Original Articles

Rapid response system
Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea: Taeyong Sim, Eun Young Cho, Ji-hyun Kim, Kyung Hyun Lee, Kwang Joon Kim, Sangchul Hahn, Eun Yeong Ha, Eunkyeong Yun, In-Cheol Kim, Sun Hyo Park, Chi-Heum Cho, Gyeong Im Yu, Byung Eun Ahn, Yeeun Jeong, Joo-Yun Won, Hochan Cho, Ki-Byung Lee; Acute Crit Care. 2025;40(2):197-208. Published online May 30, 2025; DOI: https://doi.org/10.4266/acc.000525

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Abstract PDF Supplementary Material

Background
Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea.
Methods
Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold.
Results
Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001).
Conclusions
The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

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Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System
Taeyong Sim, Eunyoung Cho, Jihyun Kim, Ho Gwan Kim, Soo-Jeong Kim
Journal of Clinical Medicine.2025; 14(13): 4444. CrossRef
Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review
Mohamed Mustaf Ahmed, Zhinya Kawa Othman, Uthman Okikiola Adebayo, Omar Kasimieh, Olalekan John Okesanya, Shuaibu Saidu Musa, Francesco Branda, Victor C. Cañezo , Edgar G. Cue, Don Eliseo Lucero Prisno III
Patient Safety in Surgery.2025;[Epub] CrossRef

Rapid response system
Development and implementation of an artificial intelligence–enhanced care model to improve patient safety in hospital wards in Spain: Alejandro Huete-Garcia, Sara Rodriguez-Lopez; Acute Crit Care. 2024;39(4):488-498. Published online November 18, 2024; DOI: https://doi.org/10.4266/acc.2024.00759

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Abstract PDF

Background
Early detection of critical events in hospitalized patients improves clinical outcomes and reduces mortality rates. Traditional early warning score systems, such as the National Early Warning Score 2 (NEWS2), effectively identify at-risk patients. Integrating artificial intelligence (AI) could enhance the predictive accuracy and operational efficiency of such systems. The study describes the development and implementation of an AI-enhanced early warning system based on a modified NEWS2 scale with laboratory parameters (mNEWS2-Lab) and evaluates its ability to improve patient safety in hospital wards.
Methods
For this retrospective cohort study of 3,790 adults admitted to hospital wards, data were collected before and after implementing the mNEWS2-Lab protocol with and without AI enhancement. The study used a multivariate prediction model with statistical analyses such as Fisher's chi-square test, relative risk (RR), RR reduction, and various AI models (logistic regression, decision trees, neural networks). The economic cost of the intervention was also analyzed.
Results
The mNEWS2-Lab reduced critical events from 6.15% to 2.15% (RR, 0.35; P<0.001), representing a 65% risk reduction. AI integration further reduced events to 1.59% (RR, 0.26; P<0.001) indicating a 10% additional risk reduction and enhancing early warning accuracy by 15%. The intervention was cost-effective, resulting in substantial savings by reducing critical events in hospitalized patients.
Conclusions
The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients.

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Rafay Ullah Khan, Nabiah Shakeel, Mohammed Hammad Jaber Amin
Annals of Medicine & Surgery.2026; 88(1): 1104. CrossRef
Contemporary clinical incident analysis methods used within acute care settings: a scoping review
Kathryn Kynoch, Xianliang Liu, Jing-Yu (Benjamin) Tan, Judeil Krlan Teus, Mary-Anne Ramis
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Jieji Hu, Rupesh Raina
Frontiers in Nephrology.2025;[Epub] CrossRef

Basic science and research
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months: Mehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, Amin Niakan, Hossein Parsaei; Acute Crit Care. 2022;37(1):45-52. Published online January 21, 2022; DOI: https://doi.org/10.4266/acc.2021.00486

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Abstract PDF

Background
Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings.
Methods
In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices.
Results
Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.”
Conclusions
Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.

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THE USE OF ARTIFICIAL INTELLIGENCE IN TRAUMATOLOGY: A SYSTEMATIC REVIEW AND RECOMMENDATIONS FOR CLINICAL PRACTICE
V. V. Savgachev, L. B. Shubin
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Winnie L. Liu, Lidan Zhang, Soussan Djamasbi, Bengisu Tulu, Susanne Muehlschlegel
Neurotherapeutics.2025; 22(1): e00503. CrossRef
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Dougho Park, Daeyoung Hong, Suntak Jin, Jong Hun Kim, Hyoung Seop Kim
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Armaan K. Malhotra, Husain Shakil, Christopher W. Smith, Yu Qing Huang, Jethro C. C. Kwong, Kevin E. Thorpe, Christopher D. Witiw, Abhaya V. Kulkarni, Jefferson R. Wilson, Avery B. Nathens
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Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning
Rujirutana Srikanchana, David Samuel, Jacob Powell, Treven Pickett, Thomas DeGraba, Chandler Sours Rhodes
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Amirmohammad Farrokhi, Mahtab Jalali, Mohamed Sobhi Jabal, Saeed Abdollahifard, Reza Taheri, Omid Yousefi, Amin Niakan, Hosseinali Khalili
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Nataša Keleman, Dragana Dragičević-Cvjetković, Aleksandra Mikov, Dragomir Radošević, Ðula Ðilvesi, Vladimir Mrđa, Rastislava Krasnik
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Pierre Singer, Eyal Robinson, Orit Raphaeli
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BMC Medical Informatics and Decision Making.2023;[Epub] CrossRef
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Helena Van Deynse, Wilfried Cools, Viktor-Jan De Deken, Bart Depreitere, Ives Hubloue, Eva Kimpe, Maarten Moens, Karen Pien, Ellen Tisseghem, Griet Van Belleghem, Koen Putman
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International Journal of Molecular Sciences.2023; 24(22): 16267. CrossRef
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Review Article

Rapid response system
Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning: Youngnam Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hugh Cho, Jinsik Park; Acute Crit Care. 2018;33(3):117-120. Published online August 31, 2018; DOI: https://doi.org/10.4266/acc.2018.00290

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Abstract PDF

With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.

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