Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare
Abstract
The integration of artificial intelligence (AI) has brought about a paradigm shift in the landscape of Neurosurgery and Neurology, revolutionizing various facets of healthcare. This article meticulously explores seven pivotal dimensions where AI has made a substantial impact, reshaping the contours of patient care, diagnostics, and treatment modalities. AI’s exceptional precision in deciphering intricate medical imaging data expedites accurate diagnoses of neurological conditions. Harnessing patient-specific data and genetic information, AI facilitates the formulation of highly personalized treatment plans, promising more efficacious therapeutic interventions. The deployment of AI-powered robotic systems in neurosurgical procedures not only ensures surgical precision but also introduces remote capabilities, mitigating the potential for human error. Machine learning models, a core component of AI, play a crucial role in predicting disease progression, optimizing resource allocation, and elevating the overall quality of patient care. Wearable devices integrated with AI provide continuous monitoring of neurological parameters, empowering early intervention strategies for chronic conditions. AI’s prowess extends to drug discovery by scrutinizing extensive datasets, offering the prospect of groundbreaking therapies for neurological disorders. The realm of patient engagement witnesses a transformative impact through AI-driven chatbots and virtual assistants, fostering increased adherence to treatment plans. Looking ahead, the horizon of AI in Neurosurgery and Neurology holds promises of heightened personalization, augmented decision-making, early intervention, and the emergence of innovative treatment modalities. This narrative is one of optimism and collaboration, depicting a synergistic partnership between AI and healthcare professionals to propel the field forward and significantly enhance the lives of individuals grappling with neurological challenges. This article provides an encompassing view of AI’s transformative influence in Neurosurgery and Neurology, highlighting its potential to redefine the landscape of patient care and outcomes.
References
Kim HE, Kim HH, Han BK et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study. Lancet Digital Health. 2020; 2: e138-e148. doi: 10.1016/S2589-7500(20)30003-0
Liu K, LFaes L, Kale AU. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digital Health. 2019; 1: e271-e297. doi: 10.1016/S2589-7500(19)30123-2
Becker AS, Marcon M, Ghafoor S, et al. Deep Learning in Mammography. Investigative Radiology. 2017; 52(7): 434-440. doi: 10.1097/rli.0000000000000358
Price WN, Gerke S, Cohen IG. Potential Liability for Physicians Using Artificial Intelligence. JAMA. 2019; 322(18): 1765. doi: 10.1001/jama.2019.15064
Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision (ICCV). Published online October 2017. doi: 10.1109/iccv.2017.74
Chattopadhyay A, Sarkar A, Howlader P, Balasubramanian VN. Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks. IEEE WACV. 2018; 839–847. doi: 10.1109/WACV.2018.00097
Haq I, Mazhar T, Malik MA. Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach. Applied Sciences. 2022; 12. doi: 10.3390/app 122412614
Echtioui A, Zouch W, Ghorbel M, et al. Detection Methods of COVID-19. SLAS Technology. 2020; 25(6): 566-572. doi: 10.1177/2472630320962002
Hussain M, Islam A, Turi JA, et al. Machine Learning-Driven Approach for a COVID-19 Warning System. Electronics. 2022; 11(23): 3875. doi: 10.3390/electronics11233875
Khadhraoui M, Bellaaj H, Ammar MB, et al. Survey of BERT-Base Models for Scientific Text Classification: COVID-19 Case Study. Applied Sciences. 2022; 12(6): 2891. doi: 10.3390/app12062891
Alsaed Z, Khweiled R, Hamad M, et al. Role of Blockchain Technology in Combating COVID-19 Crisis. Applied Sciences. 2021; 11(24): 12063. doi: 10.3390/app112412063
Guefrechi S, Jabra MB, Ammar A, et al. Deep learning based detection of COVID-19 from chest X-ray images. Multimedia Tools and Applications. 2021; 80(21-23): 31803-31820. doi: 10.1007/s11042-021-11192-5
Ben Jabra M, Koubaa A, Benjdira B, et al. COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting. Applied Sciences. 2021; 11(6): 2884. doi: 10.3390/app11062884
Geirhos R, Jacobsen JH, Michaelis C, et al. Shortcut learning in deep neural networks. Nature Machine Intelligence. 2020; 2(11): 665-673. doi: 10.1038/s42256-020-00257-z
Huang G, Liu Z, Van Der Maaten L, et al. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published online July 2017. doi: 10.1109/cvpr.2017.243
Yang J, Shi R, Ni B. MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). Published online April 13, 2021. doi: 10.1109/isbi48211.2021.9434062
Al-Khawari H, Athyal RP, Al-Saeed O, et al. Inter- and intraobserver variation between radiologists in the detection of abnormal parenchymal lung changes on high-resolution computed tomography. Annals of Saudi Medicine. 2010; 30(2): 129-133. doi: 10.4103/0256-4947.60518
Xie Y, Chen M, Kao D, et al.” CheXplain. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Published online April 21, 2020. doi: 10.1145/3313831.3376807
Venugopal VK, Takhar R, Gupta S, et al. Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) – Changing the Way We Validate Classification Algorithms. Journal of Medical Systems. 2022; 46(4). doi: 10.1007/s10916-022-01806-2
Jacovi A, Goldberg Y. Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Published online 2020. doi: 10.18653/v1/2020.acl-main.386
Tufail AB, Anwar N, Othman MTB, et al. Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. Sensors. 2022; 22(12): 4609. doi: 10.3390/s22124609
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577(7788): 89-94. doi: 10.1038/s41586-019-1799-6
Lapuschkin S, Wäldchen S, Binder A, et al. Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications. 2019; 10(1). doi: 10.1038/s41467-019-08987-4
Raza A, Ayub H, Khan JA, et al. A Hybrid Deep Learning-Based Approach for Brain Tumor Classification. Electronics. 2022; 11(7): 1146. doi: 10.3390/electronics11071146
Kriegsmann M, Kriegsmann K, Steinbuss G, et al. Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma. International Journal of Molecular Sciences. 2021; 22(10): 5385. doi: 10.3390/ijms22105385
Sun X, Yin Y, Yang Q, et al. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. European Journal of Medical Research. 2023; 28(1). doi: 10.1186/s40001-023-01065-y
Chlif M, Ammar MM, Said NB, et al. Mechanism of Dyspnea during Exercise in Children with Corrected Congenital Heart Disease. International Journal of Environmental Research and Public Health. 2021; 19(1): 99. doi: 10.3390/ijerph19010099
Chirica C, Haba D, Cojocaru E, et al. One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging. Life. 2023; 13(7): 1561. doi: 10.3390/life13071561
Moffat JG, Vincent F, Lee JA, et al. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nature Reviews Drug Discovery. 2017; 16(8): 531-543. doi: 10.1038/nrd.2017.111
Van den Broeck WMM. Drug Targets, Target Identification, Validation, and Screening. The Practice of Medicinal Chemistry. Published online 2015: 45-70. doi: 10.1016/b978-0-12-417205-0.00003-1
Gashaw I, Ellinghaus P, Sommer A, et al. What makes a good drug target? Drug Discovery Today. 2011; 16(23-24): 1037-1043. doi: 10.1016/j.drudis.2011.09.007
Gashaw I, Ellinghaus P, Sommer A, et al. What makes a good drug target? Drug Discovery Today. 2012; 17: S24-S30. doi: 10.1016/j.drudis.2011.12.008
Lindsay MA. Target discovery. Nature Reviews Drug Discovery. 2003; 2(10): 831-838. doi: 10.1038/nrd1202
Farhud DD, Zokaei S. Ethical Issues of Artificial Intelligence in Medicine and Healthcare. Iranian Journal of Public Health. Published online October 27, 2021. doi: 10.18502/ijph.v50i11.7600
EUR-Lex. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Available online: https://eur-lex.europa.eu/eli/reg/2016/679/oj (accessed on 12 March 2024).
American Society of Human Genetics (ASHG). The genetic information nondiscrimination act (GINA). Available online: https://www.ashg.org/advocacy/gina/ (accessed on 12 March 2024).
Nordling L. A fairer way forward for AI in health care. Nature. 2019; 573(7775): S103-S105. doi: 10.1038/d41586-019-02872-2
The Guardian. RoboDoc: How India’s robots are taking on Covid patient care. Available online: https://www.theguardian.com/global-development/2020/dec/02/robodoc-how-india-robots-are-taking-on-covid-patient-care-mitra (accessed on 12 March 2024).
Markose A, Krishnan R, Ramesh M. Medical ethics. Journal of Pharmacy And Bioallied Sciences. 2016; 8(5): 1. doi: 10.4103/0975-7406.191934
Varkey B. Principles of Clinical Ethics and Their Application to Practice. Medical Principles and Practice. 2020; 30(1): 17-28. doi: 10.1159/000509119
Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019; 8(7): 2328-2331. doi: 10.4103/jfmpc.jfmpc_440_19
Farhud DD. Epigenetic and Ethics: How are Ethical Traits Inherited? International Journal of Ethics & Society (IJES). 2019; 1: 1-4.
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