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
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