Article Sidebar

Submitted
June 9, 2025
Published
June 9, 2025
Download
view article
Statistic
Read Counter : 5 Download : 8

Main Article Content

Abstract

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.

Keywords

Neuro-intensive care unit artificial intelligence machine learning patient monitoring neuroimaging prognosis outcome prediction EEG intracranial pressure deep learning

Article Details

How to Cite
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
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. 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. 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. 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. 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. 5. Garcia, R., Patel, M., Watson, R., & Lim, J. (2024). Predictive algorithms for intracranial hypertension: A systematic review. Neurocritical Care, 41(1), 62–70.
  6. https://doi.org/10.1007/s12028-024-01728-4
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 13. Lee, J., Singh, K., & Hariharan, M. (2022). Artificial intelligence in critical care medicine: Current applications and future prospects. Critical Care, 26(1), 133.
  15. https://doi.org/10.1186/s13054-022-03992-7
  16. 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.
  17. https://doi.org/10.1126/science.aax2342
  18. 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.
  19. https://doi.org/10.1177/08850666221141516
  20. 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
  21. 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.
  22. https://doi.org/10.1007/s12028-022-01534-7
  23. 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
  24. 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
  25. 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
  26. 21. Zafar, S., Safdar, M., & Zafar, N. (2021). Application of artificial intelligence and machine learning in neuroimaging. Neuroscience Informatics, 1(4), 100019.
  27. https://doi.org/10.1016/j.neuri.2021.100019
  28. 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
  29. 23. Garcia, R., Patel, M., Watson, R., & Lim, J. (2024). Predictive algorithms for intracranial hypertension: A systematic review. Neurocritical Care, 41(1), 62–70.
  30. https://doi.org/10.1007/s12028-024-01728-4
  31. 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.
  32. https://doi.org/10.1136/neurintsurg-2021-018071
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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.
  40. https://doi.org/10.1007/s12028-022-01534-7
  41. 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
  42. 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
  43. 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
  44. 35. Zafar, S., Safdar, M., & Zafar, N. (2021). Application of artificial intelligence and machine learning in neuroimaging. Neuroscience Informatics, 1(4), 100019.
  45. https://doi.org/10.1016/j.neuri.2021.100019

Similar Articles

1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.