ROLE OF ARTIFICIAL INTELLIGENCE IN NEURO-ICU: FROM MONITORING TO PROGNOSIS

Izzat Ikromovich Toshev

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Izzat Ikromovich Toshev Assistant Lecturer, Department of Anesthesiology and Resuscitation, Pediatric Anesthesiology and Resuscitation, Bukhara State Medical Institute

Vol. 3 No. 6 (2025): International Journal of Cognitive Neuroscience and Psychology

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June 9, 2025

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Advances in artificial intelligence (AI) and machine learning are rapidly transforming neuro-intensive care units (Neuro-ICUs), enhancing patient monitoring, diagnostics and prognostication. This review explores the integration and application of AI-driven technologies within Neuro-ICUs, highlighting their role in real-time multimodal monitoring, early detection of neurological complications and accurate outcome prediction. AI-based algorithms utilizing continuous EEG, intracranial pressure, cerebral oxygenation and neuroimaging data offer significant improvements in detecting vasospasm, seizures and cerebral edema, facilitating timely interventions. Predictive modeling through deep learning and neural networks has shown promise in forecasting long-term outcomes, such as Glasgow Outcome Scale–Extended (GOS-E) and modified Rankin Scale (mRS) scores, thereby aiding clinical decision-making and family counseling. Despite these advances, significant challenges remain, including data privacy concerns, interpretability of algorithms, and clinical integration. This article synthesizes recent literature (2018–2025) to evaluate the potential, limitations, and ethical implications of AI applications in Neuro-ICUs and provides insights into future research directions aimed at optimizing patient care and outcomes.

Toshev, I. I. (2025). ROLE OF ARTIFICIAL INTELLIGENCE IN NEURO-ICU: FROM MONITORING TO PROGNOSIS. International Journal of Cognitive Neuroscience and Psychology, 3(6), 1–8. Retrieved from https://medicaljournals.eu/index.php/IJCNP/article/view/1876

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1. Adadi, A., & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

2. Belle, V., & Papantonis, I. (2021). Principles and practice of explainable machine learning. Frontiers in Big Data, 4, 688969. https://doi.org/10.3389/fdata.2021.688969

3. Claassen, J., Doyle, K., Matory, A., Couch, C., Burger, K. M., Velazquez, A., Roh, D. (2019). Detection of brain activation in unresponsive patients with acute brain injury. New England Journal of Medicine, 380(26), 2497–2505. https://doi.org/10.1056/NEJMoa1812757

4. Frontera, J. A., Claassen, J., Schmidt, J. M., Wartenberg, K. E., Temes, R., Connolly, E. S., Mayer, S. A. (2019). Prediction of symptomatic vasospasm after subarachnoid hemorrhage: The modified Fisher scale. Neurosurgery, 85(6), E1152–E1158. https://doi.org/10.1093/neuros/nyy408

5. Garcia, R., Patel, M., Watson, R., & Lim, J. (2024). Predictive algorithms for intracranial hypertension: A systematic review. Neurocritical Care, 41(1), 62–70.

https://doi.org/10.1007/s12028-024-01728-4

6. Goyal, N., Tsivgoulis, G., Male, S., Metter, E. J., Pandhi, A., & Chang, J. J. (2022). Artificial intelligence in neurovascular diagnostics: Current capabilities and future potential. Journal of NeuroInterventional Surgery, 14(10), 1041–1045. https://doi.org/10.1136/neurintsurg-2021-018071

7. Helbok, R., Meyfroidt, G., & Claassen, J. (2020). Advances in neurocritical care prognostication. Lancet Neurology, 19(7), 611–622. https://doi.org/10.1016/S1474-4422(20)30158-5

8. Hoelzle, J. B., Nelson, N. W., & Smith, C. A. (2020). Artificial intelligence applications in neuroimaging diagnostics: A clinical perspective. Journal of Neuropsychology, 14(2), 213–232. https://doi.org/10.1111/jnp.12193

9. Jha, A., Shrivastava, A., Raghavan, S., & Malik, N. (2024). Machine learning models for prognostication in neurocritical care: A systematic review. Critical Care, 28(1), 112. https://doi.org/10.1186/s13054-024-04622-9

10. Jing, J., Herlopian, A., Karakis, I., Ng, M., Halford, J. J., Lam, A., Struck, A. F. (2023). Deep learning models for seizure detection using EEG: Systematic review and meta-analysis. Neurology, 101(5), e457–e468. https://doi.org/10.1212/WNL.0000000000207344

11. Kim, Y. H., Kang, J., Lee, J., & Seo, J. (2023). Predicting delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using deep learning models. Journal of Neurosurgery, 139(2), 321–329. https://doi.org/10.3171/2022.7.JNS221125

12. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2023). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 29(4), 1–9. https://doi.org/10.1038/s41591-023-02274-6

13. Lee, J., Singh, K., & Hariharan, M. (2022). Artificial intelligence in critical care medicine: Current applications and future prospects. Critical Care, 26(1), 133.

https://doi.org/10.1186/s13054-022-03992-7

14. 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.

https://doi.org/10.1126/science.aax2342

15. Raj, R., Luostarinen, T., & Skrifvars, M. B. (2023). Artificial intelligence and machine learning in neurocritical care. Journal of Intensive Care Medicine, 38(5), 500–512.

https://doi.org/10.1177/08850666221141516

16. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0

17. Sharma, N., Jain, S., Gupta, A., & Rana, P. (2022). Predictive analytics for intracranial hypertension in traumatic brain injury patients. Neurocritical Care, 37(3), 495–503.

https://doi.org/10.1007/s12028-022-01534-7

18. Shortliffe, E. H., & Sepúlveda, M. J. (2021). Clinical decision support in the era of artificial intelligence. JAMA, 326(21), 2109–2110. https://doi.org/10.1001/jama.2021.20919

19. Struck, A. F., Tabaeizadeh, M., Schmitt, S. E., Ruiz, A. R., Swisher, C. B., & Subramaniam, T. (2021). Assessment of the validity of automated seizure detection algorithms. JAMA Neurology, 78(9), 1117–1124. https://doi.org/10.1001/jamaneurol.2021.1907

20. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

21. Zafar, S., Safdar, M., & Zafar, N. (2021). Application of artificial intelligence and machine learning in neuroimaging. Neuroscience Informatics, 1(4), 100019.

https://doi.org/10.1016/j.neuri.2021.100019

22. Belle, V., & Papantonis, I. (2021). Principles and practice of explainable machine learning. Frontiers in Big Data, 4, 688969. https://doi.org/10.3389/fdata.2021.688969

23. Garcia, R., Patel, M., Watson, R., & Lim, J. (2024). Predictive algorithms for intracranial hypertension: A systematic review. Neurocritical Care, 41(1), 62–70.

https://doi.org/10.1007/s12028-024-01728-4

24. Goyal, N., Tsivgoulis, G., Male, S., Metter, E. J., Pandhi, A., & Chang, J. J. (2022). Artificial intelligence in neurovascular diagnostics: Current capabilities and future potential. Journal of NeuroInterventional Surgery, 14(10), 1041–1045.

https://doi.org/10.1136/neurintsurg-2021-018071

25. Helbok, R., Meyfroidt, G., & Claassen, J. (2020). Advances in neurocritical care prognostication. Lancet Neurology, 19(7), 611–622. https://doi.org/10.1016/S1474-4422(20)30158-5

26. Jha, A., Shrivastava, A., Raghavan, S., & Malik, N. (2024). Machine learning models for prognostication in neurocritical care: A systematic review. Critical Care, 28(1), 112. https://doi.org/10.1186/s13054-024-04622-9

27. Jing, J., Herlopian, A., Karakis, I., Ng, M., Halford, J. J., Lam, A., Struck, A. F. (2023). Deep learning models for seizure detection using EEG: Systematic review and meta-analysis. Neurology, 101(5), e457–e468. https://doi.org/10.1212/WNL.0000000000207344

28. Kim, Y. H., Kang, J., Lee, J., & Seo, J. (2023). Predicting delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using deep learning models. Journal of Neurosurgery, 139(2), 321–329. https://doi.org/10.3171/2022.7.JNS221125

29. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2023). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 29(4), 1–9. https://doi.org/10.1038/s41591-023-02274-6

30. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0

31. Sharma, N., Jain, S., Gupta, A., & Rana, P. (2022). Predictive analytics for intracranial hypertension in traumatic brain injury patients. Neurocritical Care, 37(3), 495–503.

https://doi.org/10.1007/s12028-022-01534-7

32. Shortliffe, E. H., & Sepúlveda, M. J. (2021). Clinical decision support in the era of artificial intelligence. JAMA, 326(21), 2109–2110. https://doi.org/10.1001/jama.2021.20919

33. Struck, A. F., Tabaeizadeh, M., Schmitt, S. E., Ruiz, A. R., Swisher, C. B., & Subramaniam, T. (2021). Assessment of the validity of automated seizure detection algorithms. JAMA Neurology, 78(9), 1117–1124. https://doi.org/10.1001/jamaneurol.2021.1907

34. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

35. Zafar, S., Safdar, M., & Zafar, N. (2021). Application of artificial intelligence and machine learning in neuroimaging. Neuroscience Informatics, 1(4), 100019.

https://doi.org/10.1016/j.neuri.2021.100019

International Journal of Cognitive Neuroscience and Psychology

ROLE OF ARTIFICIAL INTELLIGENCE IN NEURO-ICU: FROM MONITORING TO PROGNOSIS

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