International Journal of Clinical Case Reports 2025, Vol.15 http://medscipublisher.com/index.php/ijccr © 2025 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
International Journal of Clinical Case Reports 2025, Vol.15 http://medscipublisher.com/index.php/ijccr © 2025 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. MedSci Publisher is an international Open Access publisher specializing in clinical case, clinical medicine, new variations in disease processesregistered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. Publisher MedSci Publisher Editedby Editorial Team of International Journal of Clinical Case Reports Email: edit@ijccr.medscipublisher.com Website: http://medscipublisher.com/index.php/ijccr Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada International Journal of Clinical Case Reports (ISSN 1927-579X) is an open access, peer reviewed journal published online by MedSci Publisher. The journal is considering all the latest and outstanding research articles, letters and reviews in all aspects of clinical case, containing clinical medicine which advance general medical knowledge; the event in the course of observing or treating a patient; new variations in disease processes; as well as the expands the field of clinical relating to case reports. All the articles published in International Journal of Clinical Case Reports are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MedSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
International Journal of Clinical Case Reports (online), 2025, Vol. 15, No.3 ISSN 1927-579X http://medscipublisher.com/index.php/ijccr © 2025 MedSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Study on the Application of AI-Assisted Diagnostic Systems in Emergency Care for Patients with Cerebrovascular Accidents Xiuli Ma, Lingling Qin, Chunyue He, Yeli Huang International Journal of Clinical Case Reports, 2025, Vol. 15, No. 3, 98-109 Exploring the Application of 5G-Supported Remote Care Services in Rural and Remote Areas Mengjun Yan, Qinna Mao, Yeli Huang International Journal of Clinical Case Reports, 2025, Vol. 15, No. 3, 110-119 Comprehensive Nursing Strategies for Tuberculosis Patients Lihui Xu, Keyan Fang International Journal of Clinical Case Reports, 2025, Vol. 15, No. 3, 120-129 A Study on the Effectiveness of Emergency Care Knowledge Dissemination for Cerebrovascular Accident in Home Environments and Improvement Strategies Shuiping Lou, Ning Jiang, Yeli Huang International Journal of Clinical Case Reports, 2025, Vol. 15, No. 3, 130-138 Study on the Application of Targeted Therapy Combined with Chemotherapy in Cervical Cancer Patients Liting Wang International Journal of Clinical Case Reports, 2025, Vol. 15, No. 3, 139-147
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 98 Research Insight Open Access Study on the Application of AI-Assisted Diagnostic Systems in Emergency Care for Patients with Cerebrovascular Accidents XiuliMa1, Lingling Qin1, Chunyue He1, Yeli Huang2 1 Heart Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, Beijing, China 2 Nursing Department, The Sixth Medical Center, General Hospital of People’s Liberation Army, Beijing 100048, Beijing, China Corresponding author: huangyeli88@163.com International Journal of Clinical Case Reports 2025, Vol.15, No.3 doi: 10.5376/ijccr.2025.15.0011 Received: 10 Mar., 2025 Accepted: 12 Apr., 2025 Published: 06 May, 2025 Copyright © 2025 Ma et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Ma X.L., Qin L.L., He C.Y., and Huang Y.L., 2025, Study on the application of AI-assisted diagnostic systems in emergency care for patients with cerebrovascular accidents, International Journal of Clinical Case Reports, 15(3): 98-109 (doi: 10.5376/ijccr.2025.15.0011) Abstract This study explores the application and potential of AI-assisted diagnostic systems in emergency care for cerebrovascular accidents (CVAs). The findings indicate that AI-based clinical decision support systems (AI-CDSSs) significantly improve stroke care quality and patient outcomes by providing timely and precise imaging analysis and treatment recommendations. AI and machine learning algorithms expedite the detection of cerebrovascular pathologies in emergency settings, enhancing diagnostic accuracy and prognostic evaluation. Future research should focus on large-scale randomized controlled trials, the development of advanced AI algorithms that integrate multimodal data, and the integration of AI with wearable devices to further advance the application of AI in emergency care. Keywords Artificial intelligence (AI); Cerebrovascular accidents (CVA); Emergency care; Diagnostic support systems; Personalized medicine 1 Introduction Cerebrovascular accidents (CVAs), commonly known as strokes, are among the leading causes of morbidity and mortality worldwide. They significantly contribute to long-term disability and impose a substantial burden on global healthcare systems (Sarmento et al., 2020; Gilotra et al., 2023). The high incidence of strokes necessitates effective and timely medical interventions to mitigate their impact on patients' health and quality of life (Tarnutzer et al., 2017; Li et al., 2023). Timely diagnosis and treatment are critical in the management of cerebrovascular accidents. Rapid and accurate identification of stroke symptoms can significantly improve patient outcomes by enabling prompt initiation of appropriate therapeutic interventions (Tarnutzer et al., 2017; Deshpande et al., 2023). Delays in diagnosis and treatment are associated with increased complications, prolonged hospital stays, and higher mortality rates (Tarnutzer et al., 2017; Deshpande et al., 2023). Therefore, enhancing diagnostic accuracy and reducing time to treatment in emergency settings are paramount for improving stroke care (Deshpande et al., 2023; Gilotra et al., 2023). Artificial intelligence (AI) has emerged as a transformative technology in medical diagnostics, offering the potential to enhance the accuracy and efficiency of clinical decision-making processes. AI-assisted diagnostic systems leverage machine learning algorithms to analyze complex medical data, providing clinicians with valuable insights and recommendations (Chee et al., 2021; Gilotra et al., 2023). In the context of cerebrovascular accidents, AI applications have shown promise in improving the detection and management of strokes through advanced imaging analysis, automated triage, and prognostication (Deshpande et al., 2023; Li et al., 2023; Gan et al., 2023). The integration of AI in emergency care settings holds the potential to revolutionize stroke diagnosis and treatment, ultimately improving patient outcomes (Chee et al., 2021; Gilotra et al., 2023; Wang, 2024). This review explored the application of the AI-assisted diagnostic system in the emergency care of patients with cerebrovascular accidents. It will examine the current state of AI technology in stroke diagnosis, evaluate its impact on clinical outcomes, and discuss future prospects for its integration into routine clinical practice. By
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 99 synthesizing findings from recent studies, this review seeks to provide a comprehensive understanding of the benefits and challenges associated with AI applications in stroke care, highlighting areas for further research and development. 2 Overview of Cerebrovascular Accidents and Emergency Care 2.1 The pathophysiological basis and main types of cerebrovascular accidents Cerebrovascular accidents (CVAs), commonly known as strokes, are a leading cause of morbidity and mortality worldwide. They are primarily classified into two main types: ischemic and hemorrhagic strokes. Ischemic strokes, which account for approximately 87% of all strokes, occur due to an obstruction within a blood vessel supplying blood to the brain, often caused by atherosclerosis or embolism (Karataş et al., 2022; Antón-Munárriz et al., 2023; Gilotra et al., 2023). Hemorrhagic strokes, on the other hand, result from the rupture of a blood vessel, leading to bleeding within or around the brain. This type of stroke is less common but associated with higher mortality and poorer prognosis (Chennareddy et al., 2021; Deshpande et al., 2023). The pathophysiological basis of ischemic strokes involves the interruption of cerebral blood flow, leading to a cascade of cellular events that result in neuronal injury and death. Hemorrhagic strokes involve the extravasation of blood, which can cause direct damage to brain tissue, increased intracranial pressure, and secondary ischemic injury due to reduced perfusion (Chennareddy et al., 2021; Deshpande et al., 2023; Gilotra et al., 2023). 2.2 Standards of emergency care Emergency care for cerebrovascular accidents is time-sensitive and aims to restore cerebral perfusion and minimize brain damage. The initial assessment includes a rapid clinical evaluation and neuroimaging to determine the type of stroke and appropriate treatment (Tarnutzer et al., 2017; Gilotra et al., 2023; Li et al., 2023). For ischemic strokes, the primary treatment involves thrombolytic therapy with tissue plasminogen activator (tPA) if administered within a specific time window from symptom onset. Mechanical thrombectomy may also be considered for eligible patients with large vessel occlusions (Deshpande et al., 2023; Gilotra et al., 2023). For hemorrhagic strokes, management focuses on controlling bleeding, reducing intracranial pressure, and preventing complications such as rebleeding and vasospasm. Surgical interventions may be necessary in cases of significant hematoma or aneurysm rupture (Tarnutzer et al., 2017; Chennareddy et al., 2021). The use of AI-based clinical decision support systems (AI-CDSS) has shown promise in improving the quality of stroke care by providing guideline-based treatment recommendations and assisting in the rapid interpretation of imaging studies (Figure 1) (Karataş et al., 2022; Li et al., 2023). Figure 1 The framework for cerebrovascular disease clinical decision support system (CDSS) (Adopted from Li et al., 2023) Image caption: EMR, Electronic Medical Record; HIS, Hospital Information System; LIS, Laboratory Information Management System; PACS, Picture Archiving and Communication System; TOAST, Trial of Org 10172 in Acute Stroke Treatment; CISS, Chinese Ischemic Stroke Subclassification (Adopted from Li et al., 2023)
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 100 2.3 The advantages and limitations of traditional technologies like CT and MRI Computed tomography (CT) and magnetic resonance imaging (MRI) are the cornerstone imaging modalities for the diagnosis of cerebrovascular accidents. Non-contrast CT (NCCT) is often the first-line imaging technique due to its widespread availability, speed, and ability to quickly rule out hemorrhage (Gilotra et al., 2023; Antón-Munárriz et al., 2023). However, its sensitivity for detecting early ischemic changes is limited, which can lead to diagnostic challenges in the acute setting (Tarnutzer et al., 2017; Antón-Munárriz et al., 2023). MRI, particularly diffusion-weighted imaging (DWI), offers superior sensitivity and specificity for detecting acute ischemic strokes and can provide detailed information about the extent and location of brain injury (Chennareddy et al., 2021; Gilotra et al., 2023). However, MRI is less accessible, more time-consuming, and may not be suitable for all patients, particularly those with contraindications such as implanted medical devices (Chennareddy et al., 2021; Deshpande et al., 2023). The integration of AI algorithms with these imaging modalities has shown potential in enhancing diagnostic accuracy and efficiency. AI-driven software can assist in the rapid detection of intracranial hemorrhages and ischemic changes, thereby expediting treatment decisions and improving patient outcomes (Chennareddy et al., 2021; Karataş et al., 2022; Deshpande et al., 2023; Gilotra et al., 2023). Despite these advancements, further studies are needed to validate the long-term benefits and feasibility of AI-assisted diagnostic systems in routine clinical practice (Chennareddy et al., 2021; Gilotra et al., 2023; Li et al., 2023). 3 Concept and Development of AI-Assisted Diagnostic Systems 3.1 Fundamentals of AI technology Artificial intelligence (AI) has become a pivotal technology in medical diagnostics, particularly through the use of machine learning (ML) and deep learning (DL) techniques. These technologies have shown significant promise in the field of medical imaging, where they are used to analyze complex imaging data and assist in the diagnosis of various conditions. For instance, deep learning, a subset of machine learning, has been widely applied in cardiovascular imaging to classify, segment, and detect abnormalities, demonstrating its potential to enhance diagnostic accuracy and efficiency (Wong et al., 2020; Wang et al., 2021). Similarly, AI algorithms, such as convolutional neural networks (CNNs), have been employed to detect large vessel occlusions (LVOs) in ischemic stroke patients, showing higher sensitivity compared to traditional methods (Murray et al., 2019). Despite these advancements, the practical application of DL in clinical settings remains limited due to several challenges. One major issue is the "black box" nature of DL models, which makes it difficult to understand their internal workings and ensure their reliability and interpretability (Wong et al., 2020). Additionally, the development of robust AI models requires large, high-quality datasets and rigorous validation to ensure their generalizability and effectiveness in real-world clinical scenarios (Hadjiiski et al., 2022). Addressing these challenges is crucial for the successful integration of AI technologies into routine medical practice. 3.2 System architecture and working principles AI-assisted diagnostic systems typically consist of several core components, including data acquisition, preprocessing, model training, and inference. The data acquisition phase involves collecting medical images from various modalities such as CT, MRI, and X-rays. These images are then preprocessed to enhance their quality and remove any artifacts that may interfere with the analysis (Zhu et al., 2019). The preprocessed images are used to train AI models, which learn to identify patterns and features associated with specific medical conditions. For example, CNNs are trained to detect LVOs in stroke patients by analyzing CT angiography images (Murray et al., 2019). Once trained, these models can be deployed in clinical settings to assist healthcare professionals in diagnosing and treating patients. The inference phase involves using the trained model to analyze new medical images and provide diagnostic recommendations. Some AI systems also include features for highlighting regions of interest in the images, making the diagnosis more transparent and interpretable for clinicians (Kermany et al., 2018). Additionally, AI-assisted systems can be integrated with hospital information systems to streamline workflows and improve the efficiency of emergency care (Murray et al., 2019).
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 101 3.3 Datasets and algorithms needed for the development and training of AI models The development of effective AI-assisted diagnostic systems relies heavily on the availability of large, annotated datasets and advanced algorithms. High-quality datasets are essential for training AI models to recognize and interpret medical images accurately. These datasets often include a diverse range of images from different patient populations and imaging modalities, ensuring that the models can generalize well to new data (Hadjiiski et al., 2022). For instance, the Alberta Stroke Program Early CT Score (ASPECTS) dataset is commonly used to train models for stroke detection, utilizing random forest learning (RFL) and CNNs to achieve high sensitivity and specificity (Murray et al., 2019). In addition to datasets, the choice of algorithms plays a crucial role in the performance of AI models. Deep learning algorithms, such as CNNs, have been particularly successful in medical imaging due to their ability to automatically extract relevant features from raw image data (Chan et al., 2015; Murray et al., 2019). Transfer learning, which involves pre-training a model on a large dataset and then fine-tuning it on a smaller, task-specific dataset, is another technique that has proven effective in medical diagnostics (Kermany et al., 2018). This approach allows for the development of robust models even when limited annotated data is available, making it a valuable tool in the field of AI-assisted diagnostics. 4 Application of AI-Assisted Diagnostic Systems in Emergency Care for Cerebrovascular Accidents 4.1 Real-time detection and diagnostic capabilities AI-assisted diagnostic systems have shown significant promise in the real-time detection and diagnosis of cerebrovascular accidents, particularly in the context of acute ischemic strokes. These systems leverage advanced machine learning algorithms, such as convolutional neural networks (CNNs), to rapidly identify large vessel occlusions (LVOs) from imaging data. For instance, the use of CNNs has demonstrated a sensitivity of 85% in detecting LVOs, which is notably higher than the 68% sensitivity achieved by traditional random forest learning methods (Murray et al., 2019). This rapid assessment capability is crucial in emergency settings where timely intervention can significantly impact patient outcomes. Moreover, AI systems like Viz.ai and Brainomix have been integrated into clinical workflows to expedite the detection and treatment of strokes. These platforms automatically analyze imaging data to identify LVOs and activate emergency stroke treatment protocols, thereby reducing the time to treatment and improving the chances of patient recovery (Murray et al., 2019; Gilotra et al., 2023). The ability of these systems to provide real-time, accurate diagnoses helps mitigate the delays and variability associated with human interpretation, ultimately enhancing the efficiency of emergency care for stroke patients. 4.2 Clinical trials and case studies Several clinical trials and case studies have been conducted to evaluate the effectiveness of AI-assisted diagnostic systems in improving stroke care. The GOLDEN BRIDGE II trial, for example, is a multicenter, cluster-randomized study in China that investigates the impact of an AI-based clinical decision support system (AI-CDSS) on stroke outcomes. This trial involves 80 hospitals and aims to assess whether the AI-CDSS can reduce the incidence of new vascular events by providing AI-assisted imaging analysis and guideline-based treatment recommendations (Li et al., 2023). Preliminary results suggest that the AI-CDSS could significantly enhance stroke care quality and patient outcomes. In another study, the real-world performance of the Viz LVO software was evaluated across a multihospital stroke network. The study found that the AI software achieved a sensitivity of 93.8% and a specificity of 91.1% in detecting LVOs, demonstrating its utility as an adjunct tool in stroke diagnostics (Matsoukas et al., 2022). These findings underscore the potential of AI systems to improve diagnostic accuracy and streamline the management of cerebrovascular accidents in emergency settings. 4.3 Diagnostic accuracy of AI systems Comparative studies have highlighted the superior diagnostic accuracy of AI systems over traditional methods in detecting cerebrovascular accidents. For instance, a systematic review of AI applications in stroke diagnosis
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 102 revealed that AI algorithms, particularly those using CNNs, have higher sensitivity and specificity in identifying acute ischemic strokes and LVOs compared to conventional diagnostic approaches (Murray et al., 2019; Kundeti et al., 2021). This enhanced accuracy is attributed to the AI's ability to process large volumes of imaging data quickly and identify subtle patterns that may be missed by human observers. Furthermore, a critical evaluation of commercially available AI diagnostic tools for stroke imaging found that these systems not only provide rapid and accurate diagnoses but also reduce the variability associated with human interpretation (Wardlaw et al., 2022). The study emphasized the need for standardized performance assessment metrics to ensure the reliability and clinical applicability of AI diagnostic tools. Overall, the integration of AI into stroke diagnostics holds promise for improving diagnostic accuracy and patient outcomes in emergency care settings. 5 Performance and Reliability Analysis of AI Systems 5.1 Sensitivity and specificity The sensitivity and specificity of AI-assisted diagnostic systems are critical metrics for evaluating their performance in emergency care for cerebrovascular accidents. For instance, an AI model integrated into a scanner for detecting intracranial hemorrhages demonstrated a sensitivity of 98.1% and a specificity of 89.7% in a clinical setting, indicating high diagnostic accuracy (Kiefer et al., 2023). Similarly, a systematic review and meta-analysis of AI algorithms for detecting cerebral aneurysms reported pooled sensitivity and specificity values of 91.2% and 83.5%, respectively, underscoring the potential of AI in enhancing diagnostic precision (Din et al., 2022). Moreover, AI systems have shown promising results in the detection of acute ischemic stroke (AIS). A deep learning-based triage application for AIS on brain MRI achieved a sensitivity of 90% and a specificity of 89%, highlighting its efficacy in emergency room settings (Kim et al., 2023). These high sensitivity and specificity values suggest that AI systems can reliably identify cerebrovascular events, thereby facilitating timely and accurate clinical decision-making. 5.2 System stability and consistency The stability and consistency of AI systems are paramount for their reliable application in emergency care. In a study evaluating an AI tool for detecting large vessel occlusion (LVO) stroke, the system demonstrated consistent performance across different hospital settings, with a sensitivity of 93.8% and a specificity of 91.1% for internal carotid artery terminus and middle cerebral artery occlusions (Matsoukas et al., 2022). This consistency across various conditions indicates the robustness of the AI system in real-world clinical environments. Additionally, the H-system, an AI-based tool for intracerebral hemorrhage (ICH) management, showed high reliability in processing medical text data and generating treatment plans. The system achieved an accuracy of 88.55%, with a sensitivity of 91.83% and a specificity of 85.71%, demonstrating its stable performance in handling emergency cases (Deng et al., 2022). These findings underscore the potential of AI systems to maintain high performance levels under diverse clinical conditions, ensuring dependable support for healthcare professionals. 5.3 Clinical validation data Clinical validation of AI systems is essential to establish their efficacy and reliability in real-world settings. For example, an AI model for detecting intracranial hemorrhages was validated using 435 non-contrast head CT scans, achieving a diagnostic accuracy of 99% and a negative predictive value of 99.7% (Kiefer et al., 2023). This high level of accuracy and predictive value provides strong evidence for the clinical utility of the AI system in emergency care. Furthermore, a study assessing the diagnostic performance of AI software for detecting ICH on non-contrast CT images reported an overall diagnostic accuracy of 93.0%, with a sensitivity of 87.2% and a negative predictive value of 97.8% (Figure 2) (Seyam et al., 2022). These metrics, derived from a large dataset of 4 450 CT examinations, reinforce the reliability of AI systems in clinical practice. The consistent performance of these AI
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 103 tools across various studies and datasets highlights their potential to enhance diagnostic accuracy and improve patient outcomes in emergency care settings. Figure 2 Examples of false-positive and false-negative findings on CT images (Adopted from Seyam et al., 2022) Image caption: AI=artificial intelligence, SAH = subarachnoid hemorrhage, SDH = subdural hemorrhage (Adopted from Seyam et al., 2022) 6 Challenges in Clinical Application of AI Systems 6.1 Ethics and data privacy The integration of AI systems in emergency care for cerebrovascular accidents raises significant ethical and data privacy concerns. One of the primary issues is the protection of patient data, which is often sensitive and personal. The use of large datasets to train AI models necessitates stringent data governance frameworks to ensure patient privacy and security. However, current practices often fall short, leading to potential breaches of confidentiality and misuse of data (Tat and Rabbat, 2021). The ethical implications extend beyond mere data protection; they also encompass the need for transparency in AI decision-making processes to maintain trust between patients and healthcare providers (Hosseini et al., 2023). Moreover, the ethical landscape is complicated by the lack of comprehensive guidelines and policies governing the use of AI in healthcare. The rapid advancement of AI technologies has outpaced the development of regulatory frameworks, leaving many ethical questions unanswered. For instance, the potential for algorithmic biases to exacerbate existing health disparities is a significant concern. These biases can arise from the data used to train the models, which may not be representative of the diverse patient populations they are meant to serve (Levi et al., 2022). Addressing these ethical challenges requires a multi-faceted approach, including the development of robust ethical guidelines and continuous monitoring of AI systems in clinical practice (Petersson et al., 2023). 6.2 Technical limitations AI systems in emergency care are not without their technical limitations. One of the primary challenges is the inherent constraints of the algorithms themselves. Many AI models, particularly those based on machine learning, require large amounts of high-quality data to function effectively. In emergency settings, where data can be incomplete or noisy, the performance of these models can be significantly compromised (Chee et al., 2021). Additionally, the "black box" nature of many AI algorithms makes it difficult for clinicians to understand how decisions are being made, which can hinder their trust and willingness to adopt these technologies (Tat and Rabbat, 2021).
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 104 Model biases present another significant technical challenge. These biases can stem from various sources, including the data used for training and the design of the algorithms. For example, if the training data is not representative of the patient population, the AI system may produce biased results that could negatively impact patient care. This issue is particularly concerning in the context of cerebrovascular accidents, where timely and accurate diagnosis is critical. Biases in AI models can lead to misdiagnoses or delayed treatment, ultimately affecting patient outcomes (Kelly et al., 2019). Addressing these technical limitations requires ongoing research and development to improve the robustness and generalizability of AI models (Gilotra et al., 2023). 6.3 Implementation issues The implementation of AI systems in emergency care settings also faces several practical challenges. One of the most significant barriers is the cost associated with developing, deploying, and maintaining these systems. High initial investment costs can be prohibitive for many healthcare institutions, particularly those with limited resources. Additionally, the ongoing costs of maintaining and updating AI systems to ensure they remain effective and compliant with regulatory standards can be substantial (Abedi et al., 2020). These financial constraints can limit the widespread adoption of AI technologies in emergency care (Kelly et al., 2019). Adaptability is another critical issue. AI systems must be flexible enough to integrate seamlessly with existing healthcare infrastructure and workflows. However, many current AI solutions are not easily adaptable, requiring significant changes to established practices and systems. This lack of adaptability can lead to resistance from healthcare providers, who may be reluctant to adopt new technologies that disrupt their routine operations (Biller-Andorno et al., 2020). To overcome these implementation challenges, it is essential to develop cost-effective and adaptable AI solutions that can be easily integrated into existing healthcare systems (Hosseini et al., 2023). 7 Multidisciplinary Collaboration and Training for AI-Assisted Diagnosis 7.1 Training medical staff: preparing healthcare professionals to adapt to and use new technologies The integration of AI-assisted diagnostic systems in emergency care for cerebrovascular accidents necessitates comprehensive training programs for medical staff. These programs should focus on familiarizing healthcare professionals with the functionalities and benefits of AI tools, such as AI-based clinical decision support systems (AI-CDSS). For instance, the GOLDEN BRIDGE II trial emphasizes the importance of training medical staff to effectively utilize AI-CDSS for improved stroke care outcomes (Li et al., 2023) Training should cover the interpretation of AI-generated data, the operation of AI software, and the integration of AI recommendations into clinical decision-making processes. Moreover, continuous education and hands-on workshops can help bridge the knowledge gap between traditional diagnostic methods and AI-enhanced techniques. This approach ensures that healthcare professionals remain updated on the latest advancements and are proficient in using AI tools to enhance diagnostic accuracy and patient care. The American Association of Physicists in Medicine (AAPM) also highlights the necessity of rigorous training and validation of AI systems to ensure their reliability and generalizability in clinical settings (Hadjiiski et al., 2022). 7.2 Collaboration models between IT experts and medical teams Effective implementation of AI-assisted diagnostic systems requires robust collaboration between IT experts and medical teams. This interdisciplinary teamwork is crucial for the development, deployment, and maintenance of AI tools. IT experts play a vital role in designing and optimizing AI algorithms, while medical professionals provide clinical insights to ensure the relevance and accuracy of these tools. For example, the integration of AI in transcranial doppler (TCD) ultrasonography for cerebrovascular disease diagnosis involves collaboration between computer scientists and healthcare providers to enhance the system's diagnostic capabilities (Gan et al., 2023). Collaboration models should include regular interdisciplinary meetings, joint training sessions, and the establishment of a feedback loop where medical staff can report issues and suggest improvements to IT teams. This collaborative approach not only enhances the functionality of AI systems but also fosters a culture of
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 105 continuous improvement and innovation. The successful implementation of AI tools like RapidAI and Viz.AI in stroke centers demonstrates the effectiveness of such interdisciplinary collaboration in improving diagnostic efficiency and patient outcomes (Gilotra et al., 2023). 7.3 Optimizing medical processes Integrating AI into existing medical processes can significantly enhance the efficiency and effectiveness of emergency care for cerebrovascular accidents. AI tools can streamline diagnostic workflows, reduce the time to diagnosis, and improve the accuracy of clinical evaluations. For instance, AI-based systems for analyzing brain CT images can quickly differentiate between ischemic and hemorrhagic strokes, enabling faster and more accurate treatment decisions (Karataş et al., 2022). This integration can alleviate the burden on specialists and improve patient recovery rates To optimize medical processes, healthcare institutions should focus on seamless integration of AI tools into their existing IT infrastructure. This includes ensuring compatibility with electronic health records (EHR) systems, establishing protocols for AI data interpretation, and training staff on the use of AI-enhanced diagnostic tools. The implementation of AI in clinical practice, as seen with the use of transfer learning techniques for early detection of cerebral ischemia, highlights the potential for AI to transform emergency care by providing timely and precise diagnostic information (Antón-Munárriz et al., 2023). 8 Future Research Directions and Technological Prospects 8.1 Exploring personalized medicine The integration of personalized medicine with AI-assisted diagnostic systems holds significant promise for improving patient outcomes in emergency care for cerebrovascular accidents. By combining patient history data with AI analysis, healthcare providers can develop tailored treatment plans that address the unique needs of each patient. This approach can enhance the accuracy of diagnoses and the effectiveness of interventions, ultimately leading to better prognoses for patients with cerebrovascular diseases (Gilotra et al., 2023; Li et al., 2023). For instance, AI algorithms can analyze historical data to predict the likelihood of stroke recurrence and recommend personalized preventive measures, thereby reducing the risk of future cerebrovascular events (Elbagoury et al., 2023). Moreover, personalized medicine can facilitate more precise and timely interventions in emergency settings. AI systems can quickly process vast amounts of patient data, including genetic information, lifestyle factors, and previous medical history, to identify the most appropriate treatment options. This capability is particularly valuable in acute stroke management, where rapid decision-making is crucial for minimizing brain damage and improving recovery outcomes (Tran et al., 2019; Alaya et al., 2021). As AI technologies continue to evolve, their integration with personalized medicine will likely become a cornerstone of advanced cerebrovascular care. 8.2 The potential of federated learning and augmented reality in emergency diagnosis Federated learning and augmented reality (AR) are two emerging technologies with the potential to revolutionize emergency diagnosis for cerebrovascular accidents. Federated learning enables the development of AI models using data from multiple sources without compromising patient privacy. This decentralized approach allows for the creation of robust and generalizable models that can be applied across different healthcare settings (Sharmi et al., 2020; Abedi et al., 2021). By leveraging federated learning, healthcare providers can access a broader range of data, leading to more accurate and reliable diagnostic tools for cerebrovascular diseases (Yang et al., 2022). Augmented reality, on the other hand, offers innovative ways to enhance the diagnostic process in emergency care. AR can provide real-time, interactive visualizations of patient data, helping clinicians to quickly identify and assess cerebrovascular conditions. For example, AR can overlay imaging data onto a patient's anatomy, allowing for more precise localization of brain lesions and better planning of surgical interventions (Stewart et al., 2018). The combination of AR with AI-driven diagnostic systems can significantly improve the speed and accuracy of emergency diagnoses, ultimately leading to better patient outcomes (Alaya et al., 2021; Gilotra et al., 2023).
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 106 8.3 Global collaboration and standardization Global collaboration and standardization are essential for advancing the application of AI-assisted diagnostic systems in emergency care for cerebrovascular accidents. International research cooperation can facilitate the sharing of knowledge, data, and best practices, accelerating the development and implementation of AI technologies in healthcare (Tran et al., 2019; Li et al., 2023). Collaborative efforts can also help address common challenges, such as data privacy concerns and the need for large, diverse datasets to train AI models (Abedi et al., 2021). By working together, researchers and healthcare providers can develop more effective and equitable AI solutions for cerebrovascular care. Standard-setting is another critical aspect of advancing AI-assisted diagnostic systems. Establishing clear guidelines and protocols for the use of AI in emergency care can ensure consistency, reliability, and safety across different healthcare settings (Sharmi et al., 2020; Yang et al., 2022). Standardization can also promote the interoperability of AI systems, enabling seamless integration with existing medical infrastructure and facilitating the widespread adoption of these technologies (Stewart et al., 2018; Zhou et al., 2024). As AI continues to transform cerebrovascular care, global collaboration and standardization will play a pivotal role in maximizing its benefits and ensuring its responsible use. 9 Concluding Remarks AI-assisted diagnostic systems have demonstrated significant potential in enhancing the accuracy and efficiency of diagnosing cerebrovascular accidents (CVAs) in emergency care settings. Studies have shown that AI-based clinical decision support systems (AI-CDSSs) can improve stroke care quality and outcomes by providing timely and precise imaging analysis, stroke etiology, and treatment recommendations. AI and machine learning (ML) algorithms have been successfully integrated into clinical practice, expediting the detection of intracranial pathologies such as ischemic and hemorrhagic strokes, and improving prognostication for various cerebrovascular conditions. Additionally, AI tools have been shown to enhance diagnostic accuracy when used alongside traditional physician assessments, reducing complications and hospital stay lengths. The implementation of AI in emergency departments has also improved the sensitivity and specificity of detecting cerebrovascular events, thereby reducing diagnostic errors. Future research should focus on several key areas to advance the technology and clinical applications of AI-assisted diagnostic systems in emergency care. Firstly, there is a need for large-scale, multicenter randomized controlled trials to validate the long-term effectiveness and feasibility of AI-CDSSs in diverse clinical settings. Secondly, the development of more sophisticated AI algorithms that can integrate multimodal data, including imaging, clinical, and physiological parameters, will enhance diagnostic accuracy and patient outcomes. Research should also explore the integration of AI with wearable and remote monitoring devices to facilitate continuous, real-time assessment of patients at risk of cerebrovascular events. Additionally, studies should investigate the impact of AI on clinical workflows and its acceptance among healthcare providers to ensure seamless integration into routine practice. The prospects for broader use of AI systems in emergency care are promising, given the demonstrated benefits in diagnostic accuracy, efficiency, and patient outcomes. AI-assisted diagnostic tools are likely to become integral components of emergency care protocols, particularly for conditions requiring rapid and precise intervention, such as cerebrovascular accidents. The integration of AI with existing hospital information systems and imaging modalities will facilitate real-time decision-making and improve the overall quality of care. Moreover, the adoption of AI in emergency departments can alleviate the workload of healthcare providers, allowing for more focused and effective patient management. As AI technology continues to evolve, its application in emergency care will likely expand, leading to more personalized and predictive healthcare solutions. Acknowledgments We would like to thank Medsci Publisher continuous support throughout the development of this study.
International Journal of Clinical Case Reports, 2025, Vol.15, No.3, 98-109 http://medscipublisher.com/index.php/ijccr 107 Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. 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