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Artificial intelligence can improve diagnostic accuracy and decision-making, but data fragmentation and privacy constraints hinder robust model development. The review presents a perspective on collaborative AI to overcome data access barriers, enable cross-institutional training, and advance diagnostic equity through federated learning and privacy-preserving computation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/collaborative-artificial-intelligence-for-the-diagnosis-and-management-of-acute-ischemic-stroke/449415/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/collaborative-artificial-intelligence-for-the-diagnosis-and-management-of-acute-ischemic-stroke/449415.png","ImageObject",300,407,{"name":92,"@type":93},"Alexandar","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is timely and accurate diagnosis critical in acute ischemic stroke?","Question",{"text":112,"@type":113},"AIS requires immediate intervention because brain cells begin to perish within minutes, and treatment benefits decrease rapidly when therapy is delayed. 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Timely and accurate diagnosis is essential for effective reperfusion therapies such as intravenous thrombolysis and mechanical thrombectomy, whose clinical benefits rapidly diminish with treatment delays. Artificial Intelligence (AI) offers promising potential to enhance diagnostic accuracy and clinical decision-making in AIS. However, data fragmentation and strict privacy regulations limit the development of robust AI systems. Objectives: We aim to provide a perspective-style review that explores how collaborative AI can reshape AIS diagnostics by overcoming data access barriers, fostering cross-institutional model development, and improving diagnostic equity.  \nMethods: We analysed current challenges in developing AIS-related AI tools, particularly the limitations caused by restricted data sharing across healthcare institutions. The study highlights collaborative AI approaches, such as federated learning and privacy-preserving computation, which enable decentralised model training while maintaining patient confidentiality. Relevant literature and recent developments in clinical AI collaboration were reviewed.  \nResults: Collaborative AI enables multiple institutions to contribute to model training without exposing raw patient data. This approach improves data diversity, model generalizability, and fairness across healthcare settings. Evidence from multi-centre studies suggests that collaborative AI frameworks can produce more accurate and ethically compliant diagnostic models compared to isolated development efforts. Conclusions: Collaborative AI presents a transformative pathway for AIS management by balancing data utility and privacy protection. It supports the creation of trustworthy, scalable, and inclusive diagnostic systems. As healthcare systems increasingly adopt digital solutions, collaborative AI provides a foundation for equitable and privacyconscious innovation in stroke care.  \nIntroduction  \nARTICLE HISTORY  \nReceived 17 July 2025 Accepted 16 November 2025  \nKEYWORDS  \nAcute ischemic stroke; artificial intelligence; trustworthy healthcare; deep learning; federated learning  \nAcute ischemic stroke (AIS), accounting for approximately 80% of all stroke cases, represents a critical challenge to global health as a leading cause of mortality, disability, and economic burden worldwide [1] . This condition, caused by an abrupt cessation of blood flow to the brain, demands immediate medical intervention due to its rapid and devastating impact on neuronal health. Within just five minutes of ischemia, brain cells begin to perish, with estimates suggesting that each minute of delayed treatment results in the loss of approximately 1.9 million neurons and 14 billion synapses [2] . Accurate and timely diagnosis is therefore vital for administering thrombolysis and thrombectomy, the two main treatment strategies for AIS, both of which are highly time-sensitive and depend on precise imaging-based  \nCONTACT Qian chen  [chenqian20b@ict.ac.cn](chenqian20b@ict.ac.cn)  Research cen","cbCaiclJAljpiyWw","https://ap.wps.com/l/cbCaiclJAljpiyWw","pdf",1136449,"English","# Abstract\n# Introduction\n# Article history\n# Keywords","[{\"question\":\"Why is timely and accurate diagnosis critical in acute ischemic stroke?\",\"answer\":\"AIS requires immediate intervention because brain cells begin to perish within minutes, and treatment benefits decrease rapidly when therapy is delayed. Accurate imaging-based decision-making is essential for effective thrombolysis and thrombectomy.\"},{\"question\":\"What barriers limit development of AI systems for AIS?\",\"answer\":\"Data fragmentation across healthcare institutions and strict privacy regulations restrict sharing needed to build robust AI models. These constraints limit access to sufficiently diverse training data.\"},{\"question\":\"How does collaborative AI address privacy while improving model performance?\",\"answer\":\"Collaborative AI lets multiple institutions contribute to training without exposing raw patient data. Approaches such as federated learning and privacy-preserving computation improve data diversity, generalizability, and fairness across healthcare settings.\"}]","Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke | PDF",1790730170]