Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management

Abstract

Background

The deployment of artificial intelligence (AI) applications in the healthcare domain has witnessed a significant and noteworthy surge. This is particularly pronounced within the fast-paced and critical realm of emergency care, where the integration of AI has manifested as a transformative force, exerting profound influence on the diagnosis of trauma-related complications.

Objective

This scholarly article aims to provide an in-depth exploration of the multifaceted applications of AI in the emergency department, elucidating its remarkable efficacy in expediting and refining the precision of diagnoses and patient management within this exigent setting.

Methods

Through a meticulous and comprehensive review of pertinent literature, this study endeavors to delineate and emphasize key AI applications, thereby illuminating their significant impact in optimizing patient outcomes and rationalizing workflows within emergency care. This scholarly exploration seeks to underscore the burgeoning potential of AI as an indispensable ally in the collective pursuit of achieving apid and accurate diagnoses, particularly in high-stakes emergency settings.

Results

Findings reveal that AI is driving a paradigm shift in emergency medicine by transforming clinical approaches to urgent cases. Its implementation has shown substantial potential in optimizing patient outcomes and streamlining clinical workflows.

Conclusion

AI stands as a promising and indispensable tool in the pursuit of rapid and accurate diagnoses in emergency care. Its continued integration is poised to significantly enhance clinical decision-making and patient care in high-stakes scenarios.

Introduction

The Emergency Department (ED) frequently faces the challenge of handling a large influx of patients, resulting in issues such as overcrowding, prolonged waiting times for patients, and delays in diagnosis and treatment. These challenges have been associated with elevated rates of morbidity and mortality, as indicated by research demonstrating a link between extended ED boarding times and patient outcomes [1].

Over the past decade, Artificial Intelligence (AI) has experienced significant growth in the field of healthcare. Notably, AI applications have been employed to extract valuable insights from clinical data, aiding healthcare providers in diverse clinical tasks, including disease diagnosis, triage or screening, risk analysis, and surgical operations [2–5]. Based on Accenture’s analysis, the global health AI market is projected to achieve a valuation of US $6.6 billion by 2021. Moreover, it holds the potential for exponential growth, with expectations of surpassing this figure by more than 10 times in the next five years [6].

The term “Artificial Intelligence” was originally coined by John McCarthy in 1955, defining it as “the science and engineering of making intelligent machines.” In simpler terms, artificial intelligence is a broad term that refers to algorithms capable of performing tasks typically requiring human intervention [7]. In the last decade, artificial intelligence has gained widespread attention, with numerous articles appearing in both technology and non-technology-focused journals covering various aspects of AI [8].

AI tools have the potential to enhance accuracy, decrease costs, and save time in comparison to traditional diagnostic methods. Moreover, they can mitigate the risk of human errors and deliver more precise results in a shorter timeframe. Looking ahead, AI technology holds promise in supporting medical decisions by offering real-time assistance and valuable insights to clinicians. Ongoing research explores various applications of AI in medical diagnosis and treatment, including the analysis of medical images such as X-rays, CT scans, and MRIs. Through machine learning techniques, AI aids in the identification of abnormalities, detection of fractures, tumors, or other conditions, and provides quantitative measurements, contributing to faster and more accurate medical diagnoses [9]. With the growing importance of medical imaging and technological advancements, there is an increasing demand on emergency radiologists to generate precise reports promptly, particularly in urgent situations [10].

Emergency and trauma radiology play a crucial role in delivering high-quality care to patients who arrive at a hospital’s emergency department [11]. The demand for medical imaging has grown significantly over the past few decades, leading to a substantial increase in imaging volumes [12–14]. The rising number and intricacy of medical images pose a potential challenge to radiologists, potentially overwhelming their capacity for interpretation. In contemporary radiologic practices, the integration of automated and intelligent image analysis and understanding has become imperative. This includes processes like image segmentation, registration, and computer-aided diagnosis and detection to enhance the efficiency and accuracy of radiological interpretation [15].

Artificial intelligence serves as a valuable ally for emergency radiologists, offering support in various tasks within the challenging and high-pressure environment of emergency care [10, 16]. In the context of emergency care, AI plays a crucial role in assisting with tasks such as patient positioning, image acquisition, and reconstruction. It further aids in prioritizing worklists, facilitates image interpretation through automatic or assisted anomaly detection, and contributes to the generation of structured reports. The integration of AI in emergency radiology not only benefits radiologists and other professionals in the emergency room but also extends advantages to patients and the entire healthcare system. This includes improvements in the quality of care, creating more satisfactory working environments and conditions, and contributing to the rationalization of healthcare spending [17].

AI algorithms play a crucial role in analyzing patient data for effective triaging, aiding in the prioritization of high-risk cases. This not only reduces waiting times but also enhances overall patient flow within the emergency department [18]. Implementing a reliable symptom assessment tool can effectively rule out alternative causes of illness, leading to a reduction in the number of unnecessary visits to the Emergency Department. AI-powered decision support systems play a crucial role in offering real-time suggestions to healthcare providers, aiding in diagnosis and treatment decisions. In the Emergency Department (ED), patients are often evaluated with limited information, and physicians must navigate probabilities when risk stratifying and making decisions. Furthermore, AI contributes to optimizing healthcare resources in the ED by predicting patient demand, optimizing therapy selection (including medication, dose, route of administration, and urgency of intervention), and suggesting the length of stay in the emergency department. While machines are not infallible, they tend to make decisions more efficiently and consistently than humans. In some instances, AI systems may even challenge human radiologists, ultimately proving to be correct [9].

Therefore, this review aims to provide a comprehensive yet critical overview of the transformative role of artificial intelligence in emergency care — from diagnostic imaging and triage algorithms to patient management. Unlike previous general reviews, this paper focuses specifically on the intersection of AI technology and emergency medical practice, integrating both supportive and controversial perspectives to offer a balanced understanding of current progress and future challenges.

This study explores how artificial intelligence is revolutionizing trauma diagnosis and patient management in the emergency department. By analyzing the latest advancements, technologies, and outcomes, we aim to provide insights into the transformative role of AI in expediting and enhancing diagnostic processes. This review serves as a vital resource for clinicians and researchers, offering a concise overview of current trends, Related articles and challenges for improved emergency care.

AI technologies

Artificial intelligence is not a singular technology but rather a collection of diverse technologies. Within the healthcare field, various AI technologies with immediate relevance have emerged, each serving distinct processes and tasks. The following outlines and describes some key AI technologies of notable importance in healthcare:

Machine learning

Machine Learning (ML) serves as the foundation of AI, focusing on the creation and refinement of algorithms capable of mimicking human-like learning behaviors for efficient data analysis. These algorithms exhibit a combination of performance, robustness, and speed achievable only by machines. ML encompasses various approaches that leverage an extensive array of candidate maps to deduce predictions related to a quantity of interest or higher-level parameters, meeting predefined criteria. These candidate maps represent diverse theoretical hypotheses aimed at mapping the relationship between provided input and desired output [11, 19, 20].

Machine learning offers a powerful avenue for automating the analysis and diagnosis of medical images, holding the potential to alleviate the workload of radiologists in the field of radiology. Its applications span various aspects, encompassing medical image segmentation, registration, computer-aided detection, and diagnosis systems tailored for diverse imaging modalities. Machine learning is instrumental in tasks such as brain function or activity analysis and the diagnosis of neurological diseases from functional Magnetic Resonance Imaging (fMRI) images. Furthermore, it extends to content-based image retrieval systems designed for Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) images, as well as text analysis of radiology reports through Natural Language Processing (NLP) [15].

Machine learning encompasses four main approaches: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These approaches vary in terms of data pretreatment, algorithmic strategies for mapping data relationships, and the types of problems they can effectively address. In radiology, the most commonly employed approaches are supervised learning, unsupervised learning, and semi-supervised learning, each tailored to adapt to different clinical tasks [21].

The main characteristics of different types of machine learning approaches have been shown in Table 1.

Table 1.

The main characteristics of different types of machine learning approaches.

Type of ML	Definition	Type of Data	Tasks	Examples of Algorithms
Supervised learning	It gives a training set of instances with appropriate objectives to a computer system. Taking this training set system give response accurately on given possible inputs [22].	Labeled data	Classification task Regression task	Decision Trees, Support Vector Machines (SVMs), Regressions.
Unsupervised learning	These algorithms are unsupervised because the patterns that may or may not exist in a dataset are not informed by a target and are left to be determined by the algorithm [8].	Unlabeled data	Clustering Association Anomalies detection	K-means clustering, Singular Value Decomposition, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
Semi-supervised learning	The algorithm is placed between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data) [11].	Combination of unlabeled and lableled data (mostly unlabeled data).	Transductive task Inductive tasks	Generative Model, Self-Training Model, Graph-Based Model.
Reinforcement learning	In Reinforcement learning the trained data is provided only as a response to the program’s activities in a self-motivated situation [22].	Not needing labeled data; a numerical performance score is only given as guidance.	Modeling complex-task decision-making	K-armed Bandit, Markov Decision Processes, and SARSA (State-Action-Reward-State-Action).

Deep learning

The advanced phase of machine learning mentioned refers to deep learning, a subset of machine learning that utilizes neural networks for learning and data prediction. Deep learning encompasses various algorithms designed to create complex and generalized systems capable of handling diverse problem types and providing accurate predictions. It uses the deep graph with multiple processing layers, made up of many linear and nonlinear conversions [23]. In the initial stages, neural networks were commonly limited to only a few layers (usually less than five) due to constraints in computing power and difficulties in effectively updating the weights. The term “deep learning” specifically pertains to the utilization of neural networks characterized by a substantial number of layers, typically exceeding 20 [24].

Neural networks

Utilizing neural networks for learning represents the quintessential approach in machine learning [24]. Neural networks, inspired by the structure of the human brain, are the predominant algorithms employed in contemporary image analysis. These networks consist of interconnected nodes, or neurons, organized into layers, with the potential for thousands to millions of nodes. Each node processes information from a specific pattern of other nodes, and if the received information surpasses a certain threshold, the node transmits a signal to other node groups. These outputs are weighted, emitting a weak signal with weak stimulation and a strong signal when receiving appropriate input. The overarching objective of the network is to generate accurate answers corresponding to the labels of each examined case [25]. The relationship between these technologies has been shown in Fig. 1.

Fig. 1.

The relationship between AI, deep learning, machine learning and neural networks.

Artificial intelligence is instrumental in facilitating precise and prompt diagnoses of complications and enhancing patient management within emergency rooms. According to the studies conducted in this field, the most important applications of artificial intelligence in the field of health care can be summarized as shown in Fig. 2.

Fig. 2.

The most important types of AI applications in healthcare.

Applications of AI in healthcare

AI in image acquisition

Effective interpretation of medical imaging is contingent upon appropriate image acquisition. Machine learning techniques in this realm have demonstrated the capacity to minimize radiation exposure, diminish scan durations, lower rates of false-positive results, and curtail unnecessary repeat imaging, all while preserving image quality [26].

The manual positioning and centering of patients represent a time-consuming and radiotechnologist-dependent task that is crucial not only for acquiring high-quality diagnostic images but also for minimizing radiation exposure [11].

Numerous studies have assessed the feasibility and performance of autopositioning software, which relies on anatomical references and scout information to identify key surface landmarks and determine the start and finish positions [11].

Booji and Saltybaeva each conducted an assessment of automated patient positioning using a 3D camera guided by an AI-based algorithm. The algorithm was trained with a dataset of images from other subjects, enabling it to recognize the patient’s surface and automatically adjust its position on the tables [27, 28]. In both studies, there was a substantial improvement in patient centering compared to manual positioning, with less extreme standard deviations from the ideal isocenter of the patients.

In a recent study conducted by Gang et al., the implementation of automated positioning resulted in a notable 16% reduction in the dosage administered to the patient, accompanied by a significant time-saving of 28% [29].

Dose reduction

The accurate selection of the CT study protocol is crucial for minimizing patient irradiation and ensuring that crucial information is not overlooked. Currently, the responsibility for choosing the CT protocol lies with the radiologist, consuming valuable reporting time. There is a pressing need for an AI-based system that can integrate patient anamnestic data, the purpose of the examination, and usage conditions to suggest the most suitable protocol option [11].

The global increase in the utilization of CT and PET scans raises concerns about the radiation exposure incurred by patients undergoing frequent examinations. Radiology departments face the challenge of striking a balance between maintaining image quality and minimizing radiation dose, adhering to the principle of “as low as reasonably achievable” to prevent undue exposure [30]. The conventional approach to mitigate CT radiation dose involves reducing the tube current; however, this strategy results in increased noise levels and diminished diagnostic confidence [31]. Nonetheless, recent advancements in machine learning techniques for image reconstruction have shown promising results, producing higher-quality images compared to traditional methods while concurrently preserving lower radiation doses [32, 33].

A recent meta-analysis (2025) reviewed five clinical validation studies (n = 929 participants) comparing AI-based interventions vs. conventional CT. All included studies used deep learning–based AI algorithms for image reconstruction or analysis. The mean difference in image quality favored AI (0.70, 95% CI 0.43–0.96; P < 0.001). Dose reduction showed a positive trend but was not statistically significant (mean difference 0.47, 95% CI − 0.21 to 1.15; P = 0.18) [34].

In the domain of PET imaging, efforts to reduce radiotracer dose have focused on models capable of reconstructing low-dose examinations to closely resemble full-dose examinations through the utilization of noise-reduction algorithms. A notable commercial entity has successfully employed this approach, employing as little as one 200th of the standard tracer dose and reducing scan time by up to 75%, all while achieving image quality comparable to the industry standard, thanks to the application of encoder-decoder residual deep learning networks [30, 35, 36]. Generative adversarial networks (GANs) have been effectively employed in the reconstruction of PET images acquired with significantly reduced radiotracer doses, ranging from 1% to 25% of the standard dosage. Remarkably, the quality of these reconstructed images remains comparable to that of PET images acquired with normal-dose radiotracers [37, 38].

Image reconstruction

The process of image reconstruction is pivotal in medical imaging, aiming to generate diagnostic images of high quality while considering factors such as cost, reconstruction time, and minimizing risk to the patient [39, 40]. Significant research endeavors have been directed towards leveraging machine learning techniques for enhancing image reconstruction in various medical imaging modalities such as CT, MRI, and PET. The objectives of improvement encompass areas like noise reduction, artifact suppression, motion compensation, expedited image acquisition, and multimodal image registration. These objectives are intricately connected, allowing the potential reduction of both radiation and contrast agent doses through effective image reconstruction techniques [17].

Achieving optimal image quality in medical imaging often involves a delicate balance between the radiation dose in CT and the duration of scans for MRI. Traditional methods, such as filtered back projection [41, 42] and iterative reconstruction [43, 44], including newer model-based approaches, operate by filtering raw sensor data or incorporating considerations of noise statistics, optics, physics, and scanner parameters [44].

Various subtypes of convolutional neural networks have been designed specifically for the denoising of CT and MR images, aiming to reduce noise without sacrificing technical details [30]. A technique that merges deep learning methodologies with traditional filtered back projection principles has been developed to generate high-quality images with minimal noise, even when the CT input data is reduced by a factor of 20 [45]. A CT solution, independent of the vendor, has achieved superior spatial resolution compared to filtered back projection and model-based iterative reconstruction when processing low-dose CT. This solution, known as ClariCT.AI or ClariPi, has obtained clearance from the U.S. Food and Drug Administration. Additionally, another company has introduced a CT reconstruction product based on deep learning, delivering quality comparable to model-based iterative reconstruction but with a remarkable three- to fourfold reduction in reconstruction time [17].

In magnetic resonance imaging (MRI), longer acquisition times have the potential to enhance image quality, but they also elevate the risk of motion artifacts. Consequently, various machine learning approaches have been developed to address MRI noise reduction and artifact suppression [46]. While most of these applications are still in the research phase, a few denoising products, agnostic to vendors, have received approval from the U.S. Food and Drug Administration. These approved products contribute to reducing MRI acquisition times by 30%–40% [17].

Image quality control

A company has pioneered the development of algorithms targeting image quality concerns in radiography, ultrasound (US), and conventional angiography (ContextVision). Their product line aims to mitigate issues such as over- or underexposure and metal artifacts in radiography, suppress noise to enhance contrast and tissue differentiation in ultrasound, and reduce noise and motion artifacts to improve visibility of stents and catheter tips in coronary artery angiography [17].

Computer-aided diagnosis (CAD)

In China, the annual growth rate of medical imaging data is currently at approximately 30%, significantly surpassing the annual growth rate of radiologists, which stands at only 4.1%. This stark contrast places a substantial burden on radiologists, necessitating the processing of an increasing volume of image data [47, 48]. Given the heavy workload, manual interpretation reliant on physicians’ experience becomes error-prone, resulting in a high rate of misdiagnosis and missed diagnosis [49, 50]. A study by Banaste et al. revealed a missed injury rate of 8.8% (530 out of 5979) during the initial reading of whole-body CT scans in patients with multiple traumas [51].

Computer-aided diagnosis (CAD) systems play a crucial role in aiding emergency services by providing an initial assessment of a patient’s condition and facilitating a prompt response to a large number of cases. Notably, deep learning-based computer-aided diagnostic (DL-CAD) systems in medical imaging have demonstrated significant success in image recognition. This approach proves to be effective in addressing the existing shortage of radiologists, enhancing diagnostic confidence, and improving overall productivity in emergency scenarios [52]. Several factors have contributed to the evolution of computer-aided diagnosis in the medical field. These factors encompass the inherent complexity of the medical diagnosis process, the abundance of intricate clinical data pertaining to various diseases and conditions, the substantial reservoir of diagnostic knowledge (such as diagnostic rules), and the continual advancements in computer science, particularly within the realms of artificial intelligence, data mining, and machine learning [53].

In 1998, the inaugural commercial Computer-Aided Diagnosis (CAD) system for mammography, known as the ImageChecker system, obtained approval from the United States Food and Drug Administration (FDA). Subsequently, over the ensuing years, various commercial CAD systems designed for the analysis of mammography, breast Magnetic Resonance Imaging (MRI), and medical imaging of the lung, colon, and heart received FDA approvals. Presently, CAD systems serve as diagnostic aids, assisting physicians in making more informed medical decisions [54].

Bojsen et al. (2024) conducted a systematic review and meta-analysis of 33 studies evaluating AI algorithms for MRI-based stroke detection. Their findings demonstrated high diagnostic accuracy for ischemic stroke (sensitivity and specificity ~ 93%), while evidence for hemorrhagic stroke was limited. The study highlighted variability in methodology and reporting, with few CE-marked algorithms, emphasizing the need for standardization and clinical validation. These results indicate that AI has strong potential to support rapid stroke diagnosis and emergency triage, although further research is needed to confirm its effectiveness across all stroke subtypes [55].

Yuejuan Zhan (2023) meta-analyzed 61 studies evaluating AI for pulmonary tuberculosis detection from medical imaging. Pooled sensitivity reached ~ 91% and specificity ~ 65% in clinical trials, and ~ 94% and ~ 95% in model-development studies, respectively. The results demonstrate strong diagnostic performance, particularly for detecting pulmonary pathology in high-burden settings [56].

Predictive modeling

Artificial Intelligence (AI) models have the capability to predict outcomes and potential complications by analyzing patient data, vital signs, and historical information. This functionality empowers healthcare providers to anticipate issues proactively and plan suitable interventions for improved patient care.

Presently, Emergency Department (ED) triage predominantly relies on semi-subjective scale-based systems, and notable examples include the Emergency Severity Index (ESI) [57] and the Canadian Triage and Acuity Scale (CTAS) [58]. These scale-based protocols employ a combination of qualitative and quantitative metrics to assist healthcare practitioners in categorizing patients according to their required level of care. While these systems have been extensively adopted and demonstrated utility, their accuracy is heavily reliant on the experience of the triaging doctors or nurses [59]. Recent years have witnessed the development of diverse prediction models for Emergency Department (ED) patients. These models serve as potential complements to subjective scale-based triage processes, offering opportunities for further optimization in the management of patients within the ED [60, 61]. These models are commonly constructed using real-world data and leverage a variety of statistical and machine learning methodologies, spanning from conventional regression models to advanced neural networks [62]. Specific instances of such models include those predicting in-hospital mortality [63], and forecasting intensive care unit (ICU) admission or readmission [64].

A meta-analysis by Jian Zhang et al. investigated AI (deep and non-deep learning) for predicting microvascular invasion (MVI) from imaging data (oncology context). From 16 studies (4,759 cases), deep learning models achieved pooled sensitivity ~ 0.84 (0.75–0.90), specificity ~ 0.84 (0.77–0.89), and AUC ~ 0.90 (0.87–0.93); non-deep learning sensitivity ~ 0.77, specificity ~ 0.77, AUC ~ 0.82 [65].

A meta-analysis assessed AI models predicting ED outcomes (admission, critical care, mortality). Included 88 articles and 117 AI models. For admission: sensitivity ~ 0.81 (CI 0.74–0.86), specificity ~ 0.87 (CI 0.81–0.91), AUROC ~ 0.87 (CI 0.84–0.93). For critical care: sensitivity ~ 0.86, specificity ~ 0.89, AUROC ~ 0.93. For mortality: sensitivity ~ 0.85, specificity ~ 0.94, AUROC ~ 0.93 [66].

Robotics in surgery

The integration of AI-powered robots is becoming more prevalent in trauma surgery, offering crucial support to surgeons through real-time information provision, heightened precision, and the facilitation of minimally invasive procedures. Robotics stands out as a cutting-edge approach in the realm of minimally invasive surgery (MIS), gaining widespread acceptance in elective surgical fields such as urology, gynecology, digestive, and hepato-bilio-pancreatic surgery. However, the exploration of robotic surgery in emergency settings is relatively nascent, with limited reported experiences in the existing literature [67].

Decision support systems

Artificial Intelligence (AI)- powered decision support systems play a pivotal role in assisting clinicians in making well-informed decisions regarding the diagnosis and treatment of trauma-related complications. These sophisticated systems take into account a myriad of factors, including patient history and established medical guidelines, contributing to a more comprehensive and data-driven approach in clinical decision-making.

A Clinical Decision Support System (CDSS) is designed to elevate healthcare delivery by augmenting medical decisions through the integration of targeted clinical knowledge, patient information, and other pertinent health data [68, 69]. The widespread adoption of Machine Learning in CDSS is attributed to its efficacy in areas such as diagnosis, prognosis, pattern recognition, and imaging classification. The inherent benefits of ML include rapid data processing and the versatility of analytic methods [70].

The surge in global emergency department visits has resulted in resource saturation and overcrowding, impacting both healthcare providers and patients. The congestion in frequently crowded EDs leads to delayed workflows, posing a risk to patients in need of time-critical interventions and potentially resulting in unfavorable outcomes. Therefore, it becomes imperative for Clinical Decision Support Systems (CDSS) to support ED physicians in making timely decisions and interventions under these challenging circumstances [71, 72].

Clinical Decision Support Systems (CDSS) are commonly categorized as knowledge-based or non-knowledge based. In knowledge-based systems, rules (IF-THEN statements) are formulated, where the system retrieves data to assess the rule and generates an action or output. On the other hand, non-knowledge based CDSS still necessitate a data source, but the decision-making process involves artificial intelligence (AI), machine learning (ML), or statistical pattern recognition, rather than being explicitly programmed with expert medical knowledge [73].

There is significant interest in non-knowledge-based Clinical Decision Support Systems (CDSS) for advanced imaging and precision radiology, often referred to as ‘radiomics’ [74, 75]. As medical data increasingly involves images that require extensive manual interpretation, there is a need for technologies to assist in extracting, visualizing, and interpreting this information [69]. AI technologies, particularly deep learning (DL), are demonstrating the capability to provide insights beyond human capacity [76]. Pioneering companies such as IBM Watson Health, DeepMind, and Google are developing products for applications like tumor detection, medical imaging interpretation, diabetic retinopathy diagnosis, Alzheimer’s diagnosis through multimodal feature learning, and more. For instance, IBM Watson’s ‘Eyes of Watson’ combines image recognition of a brain scan with text recognition of case descriptions to offer comprehensive decision support, described by IBM as a ‘cognitive assistant’ [69].

Telemedicine and remote monitoring

Telemedicine, as defined by the World Health Organization, involves the utilization of information and communication technologies to deliver healthcare services, particularly in situations where geographical distance poses a significant factor [77]. This comprehensive definition of telemedicine includes a range of activities, from diagnosing, treating, and preventing diseases and injuries to conducting research and delivering ongoing education to healthcare providers. The overarching goal is to advance the health of individuals and communities [78].

AI technologies play a crucial role in supporting remote monitoring of trauma patients and are integral to telemedicine initiatives. Remote AI-based monitoring systems offer continuous data analysis and generate alerts for potential complications.

Within the realm of telemedicine, AI involves the integration of artificial intelligence, machine learning algorithms, and advanced data analytics into telehealth platforms. This fusion allows healthcare professionals to make precise diagnoses, predict outcomes, and tailor treatment plans while delivering patient care remotely. The incorporation of AI empowers healthcare providers with data-driven insights and decision-making capabilities, enhancing the efficiency and personalization of care delivery.

Recent developments in telemedicine have demonstrated the transformative role of remote monitoring systems in preventive and emergency care. For instance, Chiu et al. (2022) reported that AI-integrated cardiac monitoring devices used in patients with implantable cardioverter-defibrillators (ICDs) significantly improved clinical outcomes by enabling early detection of arrhythmia and heart failure decompensation. Early identification of these anomalies facilitated timely clinical intervention and reduced the need for emergency department (ED) visits and hospital readmissions [79]. Moreover, Chiu and Meine (2025) emphasized that while remote cardiac monitoring has become an essential component of modern cardiology, optimizing alert thresholds and data management remains crucial to prevent “alert fatigue” and ensure that these technologies continue to alleviate, rather than contribute to, the strain on emergency services [80]. Together, these findings illustrate how AI-driven remote monitoring systems can play a critical role in proactive healthcare delivery and ED load reduction.

AI in emergency rooms

Trauma represents a substantial global public health concern, resulting in almost 6 million deaths annually [81]. Despite notable progress in trauma care, particularly with the implementation of comprehensive damage control strategies, traumatic injuries persist as the primary cause of death worldwide among individuals aged 18–39 years [82, 83]. In this section, we delve into the prevalent complications encountered in emergency departments and provide an overview of recent literature pertaining to the application of artificial intelligence in diagnosing these complications.

Musculoskeletal trauma and bone fractures

Musculoskeletal emergencies represent common reasons for seeking access to emergency departments, with injuries to the extremities contributing to 50% of all nonfatal injury costs worldwide [84]; nevertheless, experienced radiologists exhibit an estimated mistake reporting rate of approximately 4%, leading to delayed diagnoses and heightened morbidity [85]. AI emerges as a valuable support tool for radiologists, potentially enhancing diagnostic accuracy even under increased workload [11]. Some examples of the most recent articles in the field of incorporating artificial intelligence into the workflow of diagnosing these complications are given in Table 2.

Table 2.

A review of studies conducted in the field of automatic diagnosis of musculoskeletal trauma and bone fractures

Authors	Aim of study	Model	Dataset	Finding
Krogue et al. [86]	Automatic identification of hip fractures using deep learning on radiography	DenseNet	1118 studies	Accuracy = 93.7% Sensitivity = 93.2% Specificity = 94.2%
Minamoto et al. [87]	Automatic detection of anterior cruciate ligament tears using a deep convolutional neural network on MRI.	CNN model	100 sagittal MR images	Sensitivity = 91% Specificity = 86% Accuracy = 88.5%
Awan et al. [88]	Efficient detection of knee anterior cruciate ligament using deep learning on MRI.	ResNet-14	917 knees sagittal MRI	Accuracy = 92% Sensitivity = 91.6% Specificity = 94.6% Precision = 91.6% F1 score = 92.3%
Bien et al. [89]	Diagnosis for knee magnetic resonance imaging using deep learning.	MRNet	1,370 knee MRI	For Anterior Cruciate Ligament lesion and Meniscal tears, AUC = 96.5%
Couteaux et al. [90]	Automatic knee meniscus tear detection using convolutional neural network on MRI.	R-CNN (Region-based Convolutional Neural Network)	1828 images	AUC = 90.6%
Liu et al. [91]	Detection of cartilage lesions using deep learning on MRI.	CNN	175 patients	Sensitivity and specificity of 84.1% and 85.2%, respectively, for evaluation 1, and of 80.5% and 87.9%, respectively, for evaluation 2. AUC were 91.7% and 91.4% for evaluations 1 and 2, respectively
Roblot et al. [92]	Detecting and characterizing the presence of a meniscus tear on MRI of the knee using convolutional neural network	Fast-region CNN and faster-region CNN	1823 MR images	AUC = 92% for the detection of the position of the two meniscal horns, AUC = 94% for the presence of a meniscal tear and AUC = 83% for determining the orientation of the tear
Chang et al. [93]	Detection of complete anterior cruciate ligament tear using deep learning	CNN	260 patients	AUC = 97.1% Sensitivity = 96.7% Specificity = 100%
Cheng et al. [94]	Detection and visualization of hip fractures on plain pelvic radiographs using deep learning.	DCNN (Deep Convolutional Neural Network)	3605 PXRs	For identifying hip fractures: Accuracy = 91% Sensitivity = 98% AUC = 98% For lesion identification: AUC = 95.9%
Jones et al. [95]	Fracture detection in musculoskeletal radiographs using deep learning.	DCNN	715,343 radiographs	AUC = 97.4% Sensitivity = 95.2% Specificity = 81.3%

Abdominal emergencies

In contrast to other domains, such as the identification of skeletal fractures, the interpretation of abdominal imaging poses distinct challenges due to the intricate variability of abdominal anatomy and the complexity of imaging characteristics. Consequently, the utilization of AI in the emergency setting is in its early stages, marked by initial investigations exploring its potential application. However, there is a scarcity of robust literature to substantiate its comprehensive integration into clinical practice at present [96]. Some examples of the most recent articles in the field of incorporating artificial intelligence into the workflow of diagnosing these complications are given in Table 3.

Table 3.

A review of studies conducted in the field of automatic diagnosis of abdominal emergencies.

Authors	Aim of study	Model	Dataset	Finding
Cheng et al. [97]	Detection of abdominal free fluid in Morison’s Pouch using deep learning.	ResNet50-V2	396 patients	Accuracy = 96.1% Sensitivity = 97.6% Specificity = 94.7%
Dreizin et al. [98]	Predicting major arterial injury after blunt hepatic trauma using deep learning.	attentional networks and dilated convolutional neural networks	73 patients	Accuracy = 84%
Kim et al. [99]	Identification of small bowel obstruction on plain abdominal radiographs using deep learning.	VGG16, Densenet121, NasNetLarge, InceptionV3, and Xception	990 plain abdominal radiographs	AUC = 96.1% Sensitivity = 91% Specificity = 93%
Goyal et al. [100]	Developing a prediction closed-loop small bowel occlusion using machine learning on CT scan.	Random Forest	223 patients	AUC = 73% Sensitivity = 72% Specificity = 80% Accuracy = 73%
Kwon et al. [101]	Detecting and visualising intussusception on plain abdominal radiography using deep learning.	Deep CNN	11,384 images	Mean AUC = 93.5% Highest accuracy = 95.2%

Chest emergencies

Chest imaging holds paramount significance in emergency radiology, and the integration of AI stands as a pivotal element in assisting radiologists in promptly and accurately diagnosing conditions. Predominantly, existing studies have concentrated on two primary areas—pulmonary embolism and pneumonia [11]. Some examples of the most recent articles in the field of incorporating artificial intelligence into the workflow of diagnosing these complications are given in Table 4.

Table 4.

A review of studies conducted in the field of automatic diagnosis of chest emergencies

Authors	Aim of study	Model	Dataset	Finding
Cheikh et al. [102]	Evaluating and comparing the diagnostic performances of a an artificial intelligence algorithm for diagnosing pulmonary embolism on CT pulmonary angiogram with those of emergency radiologists.	Commercialized artificial intelligence algorithm	1202 patients	For AI algorithm: Sensitivity = 92.6% Specificity = 95.8% Accuracy = 95.3% For radiologists: Sensitivity = 90% Specificity = 99.1% Accuracy = 97.7%
Btra et al. [103]	Detection of Incidental pulmonary embolism (iPE) on conventional contrast-enhanced chest CT.	Commercial AI algorithm	2555 patients	AI had high NPV1 and moderate PPV2 for iPE detection, detecting some iPEs missed by radiologists.
Soda et al.[104]	Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-x-rays and clinical data.	CNN	820 CXR examinations	Accuracy = 74.8% Sensitivity = 74.5% Specificity = 75.1%
Bai et al. [105]	Establishing and evaluating an artificial intelligence system for differentiating COVID-19 and other pneumonia at chest CT.	EfficientNet B4	1186 patients	Accuracy = 96% Sensitivity = 95% Specificity = 96% AUC = 95%

¹Negative Predictive Value

²Positive Predictive Value

Challenges

Artificial intelligence harbors the potential to revolutionize clinical practices; however, several challenges must be addressed to unlock its full capabilities. Emergency departments serve as a crucial testing ground for assessing the applicability and advantages of AI-powered tools. Within the emergency context, these tools have the capacity to aid radiologists in protracted and repetitive tasks, mitigating diagnostic errors in scenarios where the workload, expectations, and risk of errors are inherently high [106]. The successful integration of AI has the potential to liberate resources, allowing for their redirection toward other vital activities, such as patient communication—a facet that has, at times, been compromised due to increased workloads [107].

Recent meta-analyses [55, 108, 109] have underscored not only AI’s diagnostic potential but also the heterogeneity in study quality, lack of standardized reporting, and limited real-world validation. Incorporating these findings into clinical discussions provides a stronger evidence-based framework for evaluating AI’s role in emergency care.

Although AI-based techniques have marked their significance in healthcare, there are still many challenges faced by the researchers that need to be addressed:

• i.Data Privacy and Security:

  The protection of healthcare data, particularly patient records, holds paramount importance due to its highly sensitive nature. Ensuring the privacy and security of this data when integrating AI into healthcare practices is essential for regulatory compliance and upholding patient trust [110]. One significant factor that poses a potential threat to patient data, the smooth functioning of critical healthcare operations, and patient safety in the context of AI implementation is the escalation of cyberattacks [111]. The application of predictive algorithms can prove instrumental in identifying and preventing such cyber threats. In order to fortify data privacy and uphold the integrity of healthcare systems, a comprehensive exploration into the realm of cybersecurity and the cyber risk landscape within healthcare is imperative [111, 112]. High patient throughput and rapid decision-making in the ED amplify concerns regarding data privacy. AI systems must ensure secure handling of sensitive patient information while maintaining timely access for clinical decision support. Breaches or lapses could have immediate and severe consequences in this high-stakes environment [113].

• ii.Interoperability and Integration:

  The integration of AI solutions into healthcare systems encounters a notable challenge stemming from the heterogeneous nature of technologies and data formats in use [114]. Healthcare systems commonly employ diverse data formats and standards across medical records, imaging, and other patient information. An essential requirement for AI systems is the ability to comprehend and process data presented in various formats. The absence of standardized data formats poses a hindrance to achieving seamless interoperability. Successful integration of AI into clinical workflows is pivotal for its optimal utilization. Any disruption or significant alterations to existing workflows prompted by AI tools may encounter resistance during adoption. Therefore, ensuring that AI enhances, rather than impedes, established processes constitutes a crucial challenge in the integration process [115].

  EDs often rely on heterogeneous systems for electronic health records, imaging, and monitoring. Integrating AI solutions seamlessly into these workflows is challenging, especially under conditions of high patient influx. Effective interoperability is crucial to avoid workflow disruption and ensure that AI recommendations are delivered reliably in real time [116].

• iii.Ethical Concerns and Bias:

  Guaranteeing ethical AI practices and mitigating biases in algorithms are imperative considerations. Biased algorithms have the potential to introduce disparities in healthcare, potentially impacting the quality and equity of patient care. It is essential to implement robust mechanisms for identifying and rectifying biases within AI systems to ensure fairness and unbiased decision-making in healthcare settings [117].

  AI systems learn from historical data, and if the data employed for training carries biases, the AI system may perpetuate or even amplify those biases. In cases where specific demographic groups are underrepresented in the training data, the AI may not comprehensively grasp or address their healthcare requirements. This situation can result in disparities in diagnosis and treatment. It is imperative to regularly monitor and assess AI systems in healthcare to identify and rectify any biases or errors that may arise over time [118].

  There is a risk of overstating the capabilities of AI in healthcare, potentially fostering unrealistic expectations. Excessive reliance on AI without the necessary human oversight can have significant consequences [119].

  Patients should be educated about the incorporation of AI in their healthcare and granted informed consent. Transparency regarding the utilization of AI in aspects like diagnosis and treatment planning is paramount. It is essential to ensure that the integration of AI does not compromise the autonomy of patients or healthcare professionals [120].

  The ED serves a highly diverse and unpredictable patient population. AI models trained on limited or non-representative datasets risk biased outputs, potentially exacerbating disparities in care. Ethical considerations, including transparency, fairness, and accountability, are intensified in this context due to the urgency and critical nature of decisions [121].

• iv.Model Generalizability:

  Research should pivot towards enhancing the generalizability of AI models. Currently, many studies propose prediction models validated on a single site, necessitating validation across multiple sites to enhance the model’s generalizability [122, 123].

  Rapid decision-making in emergency medicine necessitates models that are both accurate and interpretable. Highly complex “black-box” models may provide high performance but risk being mistrusted by clinicians if their outputs cannot be quickly understood. Generalizability across different patient populations and institutions is also essential to avoid errors in real-world ED deployment [124].

• v.Human-Machine Collaboration:

  Ensuring successful collaboration between healthcare professionals and AI systems is paramount. Striking a balance in the roles of humans and machines and addressing concerns related to trust and transparency pose significant challenges. The development of AI-based tools demands multidisciplinary expertise, extending beyond the domain of a single radiologist. This can present a hurdle for smaller groups and research institutions with limited financial resources to exclusively recruit non-medical professionals for research or support endeavors [11]. Effective collaboration between clinicians and AI systems is critical in the ED. AI should support, not replace, clinical judgment, enabling faster and more informed decisions. Workflow integration, clear role definitions, and user-friendly interfaces are key to successful adoption [125].

• vi.Data Quality and Availability:

  AI models rely on high-quality, timely data, but accessing accurate data in emergency situations can be challenging, potentially affecting the performance of AI systems [126]. The availability of substantial and diverse datasets is a critical factor in the development of machine learning models. These models need extensive training, validation, and testing on large datasets. Fortunately, the rise of open-source image repositories in recent years has begun to alleviate this constraint. This development encourages collaborative research across different institutions and serves as a catalyst for standardizing acquisition protocols [127].

  The challenge of data quality poses a significant obstacle to the advancement of AI-based tools, primarily stemming from the diversity in image acquisition protocols, manufacturers, and post-processing algorithms employed in clinical practice. To ensure high-quality results, it is crucial to have images of uniform excellence, ideally obtained using the same machinery and with a high signal-to-noise ratio. Poor data quality directly translates to suboptimal model performance. To address this issue, various approaches for data harmonization have been devised, holding the potential to greatly enhance the reproducibility of radiomic features [11].

  ED data are often incomplete, noisy, or rapidly collected, posing challenges for AI model training and real-time decision support. Ensuring high-quality, reliable data is necessary to maintain model performance and patient safety [128].

• vii.Cost and Resource Allocation:

  Deploying and sustaining AI systems in healthcare demands significant financial commitments. Hospitals and healthcare establishments must earmark resources for technology acquisition, training programs, and continual support [129]. Numerous healthcare institutions may grapple with constraints such as limited budgets and outdated IT infrastructure. Therefore, the successful implementation of AI systems necessitates an approach that aligns with existing resources and infrastructure, ensuring efficiency and effectiveness. Resource constraints in emergency settings can limit AI adoption. Cost-effective solutions that optimize staff time, reduce unnecessary tests, and improve patient throughput are essential. Investments must balance technological benefits with operational feasibility [130].

  In summary, the swift progress of artificial intelligence in clinical and biomedical domains is widely acknowledged as a promising avenue to enhance healthcare professionals’ capabilities. However, despite the significant potential and strides made in the medical and healthcare sectors, this accomplishment has introduced new ethical considerations. It is crucial to recognize that the drawbacks of AI in healthcare may surpass its advantages. Addressing these challenges requires careful consideration of the ethical implications surrounding the integration of AI in healthcare, emphasizing the need for expert attention to navigate these complexities.

Conclusion

In conclusion, artificial intelligence (AI) is progressively reshaping emergency care, demonstrating significant potential in diagnostic imaging, triage, patient management, and remote monitoring. Meta-analytic and systematic evidence indicates that AI can achieve high diagnostic accuracy, enhance workflow efficiency, and support timely decision-making in emergency settings. However, several critical challenges remain unresolved. These include algorithmic bias, limited generalizability across diverse patient populations, insufficient standardization of reporting, ethical and legal considerations, and variability in data quality and clinical validation. To address these issues, future research should prioritize large-scale, multi-center studies; rigorous external validation of AI models; harmonized reporting standards; and assessment of clinical impact on both workflow and patient outcomes. By tackling these challenges, AI can be safely and effectively integrated into emergency medicine, ultimately improving patient care, optimizing resource utilization, and guiding informed clinical decision-making.

Author contributions

Writing – Original Draft Preparation: Hanieh Alimiri Dehbaghi, Review & Editing: Karim Khoshgard.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

This is a review article and data will not be required for this article.

Declarations

Ethical approval

This is a review article and does not require ethics committee approval or patient consent.

Consent to publish

Not applicable.

Consent to participate

Not applicable.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing interests

The authors declare no competing interests.