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- DOI 10.18231/j.ijashnb.v.11.i.2.3
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CrossMark
A review on advancements in neurosurgery through the integration of artificial intelligence: Current impact and future prospects
- Author Details:
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Shivam Dubey *
Shivam Dubey *
The use of artificial intelligence (AI) in neurosurgery has greatly improved clinical precision, safety, and efficiency. AI-driven innovations, such as image-guided navigation, robotic surgery, predictive analytics, and brain-computer interfaces, improve outcomes and allow for real-time, data-driven decisions. This study examines existing AI uses in neurosurgical operations, diagnostics, and postoperative care, as well as prospects such as real-time intraoperative decision-making, individualized neurosurgery planning, and autonomous surgical systems. Despite the potential, ethical, legal, and infrastructure difficulties persist. Continued multidisciplinary collaboration is required to realize AI's promise in neurosurgical developments fully.
References
- Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, et al. Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery. 2018;83(2):181–92.
- Panesar SS, Kliot M, Parrish R, Fernandez-Miranda J, Cagle Y, Britz GW. Promises and perils of artificial intelligence in neurosurgery. Neurosurgery. 2020;87(1):33–44.
- Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, et al. Artificial intelligence in brain tumour surgery—an emerging paradigm. Cancers (Basel). 2021;13(19):5010.
- Mofatteh M. Neurosurgery and artificial intelligence. AIMS Neurosci. 2021;8(4):477–95.
- Danilov GV, Shifrin MA, Kotik KV, Ishankulov TA, Orlov YuN, Kulikov AS, et al. Artificial intelligence in neurosurgery: a systematic review using topic modeling. Part I: major research areas. Sovrem Tekhnologii Med. 2021;12(5):106–12.
- Schilling AT, Shah PP, Feghali J, Jimenez AE, Azad TD. A brief history of machine learning in neurosurgery. Acta Neurochir Suppl. 2022;134:245–50.
- Dasgupta A, Gupta T, Pungavkar S, Shirsat N, Epari S, Chinnaswamy G, et al. Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients. Neuro Oncol. 2018;21(1):115–24.
- Krivoshapkin AL, Sergeev GS, Kalneus LE, Gaytan AS, Murtazin VI, Kurbatov VP, et al. New software for preoperative diagnostics of meningeal tumor histologic types. World Neurosurg. 2016;90:123–32.
- Yamazawa E, Takahashi S, Shin M, Tanaka S, Takahashi W, Nakamoto T, et al. MRI-based radiomics differentiates skull base chordoma and chondrosarcoma: a preliminary study. Cancers (Basel). 2022;14(13):3264.
- Yan J, Liu L, Wang W, Zhao Y, Li KK, Li K, et al. Radiomic features from multi-parameter MRI combined with clinical parameters predict molecular subgroups in patients with medulloblastoma. Front Oncol. 2020;10:558162.
- Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, et al. Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One. 2015;10:e0141506.
- Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, et al. Artificial intelligence in neurosurgery: a state- of-the-art review from past to future. Diagnostics (Basel). 2023;13(14):2429.
- Cannizzaro D, Zaed I, Safa A, Jelmoni AJM, Composto A, Bisoglio A, et al. Augmented reality in neurosurgery: state of art and future projections. a systematic review. Front Surg. 2022;9:864792.
- Scharp D, Hobensack M, Davoudi A, Topaz M. Natural language processing applied to clinical documentation in post- acute care settings: a scoping review. J Am Med Dir Assoc. 2024;25(1):69–83. 44 Dubey / IP Indian Journal of Anatomy and Surgery of Head, Neck and Brain 2025;11(2):37–44
- Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203–
- Singh R, Wang K, Qureshi MB, Rangel IC, Brown NJ, Shahrestani S, et al. Robotics in neurosurgery: current prevalence and future directions. Surg Neurol Int. 2022;13:373.
- Staartjes VE, de Wispelaere MP, Vandertop WP, Schröder ML. Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling. Spine J. 2019;19(5):853–61.
- Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ. Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging. 2017;30(5):622–8.
- Choi YS, Bae S, Chang JH, Kang SG, Kim SH, Kim J, et al. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol. 2021;23(2):304–13.
- Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PC. Supervised autonomous robotic soft tissue surgery. Sci Transl Med. 2016;8(337):337ra64.
- Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. High-performance brain-to-text communication via handwriting. Nature. 2021;593(7858):249–54.