The Role of Artificial Intelligence in Stroke
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Abstract
Artificial Intelligence (AI) is transforming healthcare, particularly in stroke diagnosis, management, and rehabilitation. Stroke, a leading cause of mortality and disability, requires rapid intervention for improved outcomes. AI’s ability to process vast amounts of data and detect patterns has revolutionized stroke care. Machine learning (ML) algorithms are now used to analyze Computerized Tomography (CT) scans and Magnetic Resonance Imaging (MRI) scans for early stroke detection, helping to determine the type, location, and severity of brain damage. AI is also integrated into predictive modeling systems, assessing stroke risk factors for better prevention management.
AI enhances clinical decision-making by providing personalized treatment recommendations, such as optimal therapies like thrombolysis or thrombectomy. Additionally, AI supports post-stroke rehabilitation by enabling adaptive learning in robotic-assisted therapy and virtual reality, offering personalized recovery plans. The rapid growth of AI in stroke care has the potential to improve patient outcomes, reduce diagnosis time, and provide continuous monitoring.
However, challenges like data privacy, model interpretability, and regulatory approval remain significant barriers. Future research should focus on enhancing AI system accuracy, ensuring generalization across diverse populations, and improving the integration of AI tools into clinical workflows to optimize stroke management.
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Copyright (c) 2025 Dr Saumya Harsh Mittal, Dr Salony Mittal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons License All articles published in Annals of Medicine and Medical Sciences are licensed under a Creative Commons Attribution 4.0 International License.
[1] Kim, B. S., & Yoon, W. (2020). Role of AI in stroke: Challenges and opportunities. Journal of Stroke and Cerebrovascular Diseases, 29(11), 104847.
[2] Nascimento, M. A., & Freitas, D. M. (2021). Artificial intelligence in stroke management: A review. Journal of Medical Systems, 45(5), 52.
[3] Chen, W., et al. (2022). Deep learning for stroke detection: A review of recent advances. Neural Networks, 148, 1-13.
[4] Ren, H., et al. (2020). AI-driven stroke rehabilitation and recovery: An overview. Frontiers in Neurology, 11, 589208.
[5] van der Worp, H. B., &Majoie, C. B. (2021). Artificial intelligence in stroke management and care: Implications for clinical practice. Lancet Neurology, 20(4), 234-247.
[6] Yang, G., et al. (2022). Artificial intelligence in stroke imaging: A review. Journal of Stroke, 24(1), 1-16.
[7] Smith, R. E., & Patel, R. (2023). Integration of AI for improving stroke diagnosis: A review. Stroke and Vascular Neurology, 8(2), 126-133.
[8] Lee, C. Y., et al. (2020). Telemedicine and AI in stroke diagnosis: Current trends and future perspectives. Journal of Telemedicine and Telecare, 26(7-8), 482-491.
[9] Ghai, S., et al. (2021). The role of robotic exoskeletons in stroke rehabilitation: A review of AI-driven advancements. Journal of Rehabilitation Research and Development, 58(3), 213-224.
[10] Laver, K. E., et al. (2020). Virtual reality for stroke rehabilitation: A systematic review and meta-analysis. Clinical Rehabilitation, 34(6), 721-735.
[11] Huang, H., et al. (2021). AI-powered personalized rehabilitation: Advancements in adaptive exercise programs for stroke recovery. Stroke Rehabilitation Journal, 28(1), 58-72.
[12] Narayana, P. A., et al. (2021). AI in tele-rehabilitation for stroke recovery: Enhancing access and outcomes. Telemedicine and e-Health, 27(8), 839-847.
[13] Alvarez, I., et al. (2020). Cognitive rehabilitation after stroke using AI-driven training programs: A pilot study. Neuropsychological Rehabilitation, 30(5), 988-1001.
[14] Kallio, J., et al. (2021). Speech abnormalities in stroke patients: Detecting subtle impairments using AI. IEEE Transactions on Biomedical Engineering, 68(6), 1867-1874.
[15] Moser, T., et al. (2019). AI-powered assessment tools for speech therapy in stroke patients. Journal of Speech, Language, and Hearing Research, 62(10), 3924-3933.
[16] Lee, S. W., et al. (2017). Development of an AI-based speech therapy app for stroke patients. Journal of Artificial Intelligence in Medicine, 81, 101-111.
[17] Fridriksson, J., et al. (2020). Challenges in applying AI to speech rehabilitation. Speech Communication, 122, 40-45.
[18] Smith, M., & Jensen, K. (2019). Ethical considerations in AI-driven healthcare applications. Journal of Medical Ethics, 45(7), 474-480.
[19] Rajan, P., et al. (2021). Data requirements for AI applications in healthcare: Challenges and considerations. IEEE Journal of Biomedical and Health Informatics, 25(3), 900-907.
[20] Pawelec, P., & Hilsenbeck, S. (2019). Machine learning in stroke rehabilitation: Pitfalls and promises. Neurorehabilitation and Neural Repair, 33(9), 795-803.
[21] Schmitter-Edgecombe, M., & Anderson, S. (2018). Cognitive rehabilitation in stroke recovery: Enhancing engagement and adherence through AI. Journal of Cognitive Enhancement, 2(3), 253-267.
[22] De Masi, F., et al. (2020). Clinical integration of AI in stroke rehabilitation: Barriers and opportunities. Journal of Stroke and Cerebrovascular Diseases, 29(12), 105-113.
[23] Rehg, J. M., et al. (2021). Ethical implications of AI in healthcare: Risks and safeguards. Journal of Medical Ethics, 47(5), 312-318.
[24] Desautels, T., et al. (2016). Prediction of sepsis in the ICU: A machine learning approach. Computational Biology and Medicine, 68, 24-32.
[25] Choi, E., et al. (2017). A machine learning approach to predicting heart failure using electronic health records. Journal of the American Medical Informatics Association, 24(6), 1112-1120.
[26] Rajpurkar, P., et al. (2017). Pneumonia detection in chest X-rays with deep learning. Scientific Reports, 7, 1-11.
[27] Tiwari, A., et al. (2019). Remote monitoring and telemedicine applications in chronic disease management. Journal of Medical Systems, 43(6), 1-8.
[28] Reddy, S., et al. (2020). Wearable ECG analysis for arrhythmia detection using AI. IEEE Transactions on Biomedical Engineering, 67(5), 1286-1295.
[29] Wang, Y., et al. (2019). AI-based mobile health applications for remote patient monitoring. Health Information Science and Systems, 7,10.
[30] Chen, L., et al. (2020). Challenges in deploying machine learning models in healthcare: Data quality and generalizability. Journal of Healthcare Engineering, 2020, 18-25.
[31] Pfeifer, J., et al. (2018). AI in healthcare: Regulatory, privacy, and data protection issues. Healthcare Management Review, 43(2), 111-118.
[32] Price, W. N., & Cohen, I. G. (2019). Ethical issues in AI in healthcare. The Journal of Law, Medicine & Ethics, 47(1), 51-60.
[33] Silverstein, M., et al. (2020). Data availability challenges for AI in healthcare. Journal of Medical Internet Research, 22(8), e18513.
[34] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
[35] Denecke, K., et al. (2020). Integrating AI into clinical practice: Challenges and opportunities. Journal of Medical Systems, 44(7), 131-139.
[36] Kashyap, R., & Kumar, R. (2021). Data privacy and security in AI-driven healthcare systems. International Journal of Computer Science in Sport, 13(1), 1-8.
[37] Goodfellow, I., et al. (2016). Deep learning in healthcare. Nature Reviews Disease Primers, 2, 1-14.