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
RkJQdWJsaXNoZXIy MjQ4ODYzNA==