Abstract

The advent of Artificial Intelligence (AI) and ChatGPT has marked its effect on the various aspects of society and the medical field is not exempt from its influence. The AI and ChatGPT can prove to be of great assistance to neuroanesthesiologists by helping in clinical decision-making during the perioperative period and in the neurocritical management and prognostication of the patients. Along with that, it can also help in medical recordkeeping, translations of medical education and research. However, the progress of technology isn’t exempted from its evil potential. The boon of increasing productivity and lightening the load off Neuroanesthesiologist’ shoulders comes with the bane of false information, misinterpretation, piracy and plagiarism. Hence, AI and ChatGPT should be allowed to analyze or develop sensitive information or decisions only under the scrutiny of a human assessment.

Keywords:

Artificial Intelligence, Chat GPT, Neuroanesthesia, Neurocritical Care

References

Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology.2020;132:379-394. doi:10.1097/ALN.0000000000002960.

Thrall JH, Li X, Li Q, et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol. 2018;15:504-508. doi:10.1016/j.jacr.2017.12.026.

Salto-Tellez M, Maxwell P, Hamilton P. Artificial intelligence-the third revolution in pathology. Histopathology. 2019;74:372-376. doi:10.1111/his.13760.

Deo RC. Machine Learning in Medicine. Circulation. 2015;132:1920-1930. doi:10.1161/CIRCULATIONAHA.115.001593.

Patel SB, Lam K. ChatGPT: the future of discharge summaries?. Lancet Digit Health. 2023;5:e107-e108. doi:10.1016/S2589-7500(23)00021-3.

Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. J Med Syst. 2023;47:33. doi:10.1007/s10916-023-01925-4.

Tewfik G, Naftalovich R, Kaila J, Adaralegbe A. ChatGPT and Its Potential Implications for Clinical Practice: An Anesthesiology Perspective. Biomed Instrum Technol. 2023;57(1):26-30. doi:10.2345/0899-8205-57.1.26.

Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. Plos Digit Health. 2023;2:e0000198. doi: 10.1371/journal.pdig.0000198.

Liévin V, Egeberg Hother C, Winther O. Can large lan¬guage models reason about medical questions? eprint arXiv. 2022;2207:08143. doi:10.48550/arXiv.2207.08143.

Shay D, Kumar B, Bellamy D, et al. Assessment of ChatGPT success with specialty medical knowledge using anaesthesiology board examination practice questions [published online ahead of print, 2023 May 18]. Br J Anaesth. 2023;S0007-0912(23)00192-7. doi:10.1016/j.bja.2023.04.017.

Bellini V, Rafano Carnà E, Russo M, Di Vincenzo F, Berghenti M, Baciarello M, Bignami E. Artificial intelligence and anesthesia: a narrative review. Ann Transl Med. 2022;10:528. doi: 10.21037/atm-21-7031.

Xue B, Li D, Lu C, et al. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021;4:e212-40. doi:10.1001/jamanetworkopen.2021.2240.

Kim JH, Kim H, Jang JS, et al. Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height. BMC Anesthesiol. 2021;21:125. doi:10.1186/s12871-021-01343-4.

Tavolara TE, Gurcan MN, Segal S, Niazi MKK. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Comput Biol Med. 2021;136:104737. doi:10.1016/j.compbiomed.2021.104737.

Zaouter C, Hemmerling TM, Lanchon R, et al. The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery. Anesth Analg. 2016;123:885-93. doi:10.1213/ANE.0000000000001152.

Shieh JS, Fan SZ, Chang LW, Liu CC. Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block. J Clin Monit Comput. 2000;16:583-92. doi:10.1023/a:1012212516100.

Martinoni EP, Pfister ChA, Stadler KS, et al. Model-based control of mechanical ventilation: design and clinical validation. Br J Anaesth. 2004;92:800-7. doi:10.1093/bja/aeh145.

Zhou CM, Wang Y, Xue Q, Yang JJ, Zhu Y. Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms. BMC Med Res Methodol. 2023;23:133. doi: 10.1186/s12874-023-01955-z.

Zhao K, Zhao Q, Zhou P, Liu B, Zhang Q, Yang M. Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis. Int J Clin Pract. 2022;2022:9430097. doi:10.1155/2022/9430097.

Ghosh S, Feng M, Nguyen H, Li J. Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure. IEEE J Biomed Health Inform. 2016;20:1416-26. doi: 10.1109/JBHI.2015.2453478.

Oh J, Makar M, Fusco C, et al. A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers. Infect Control Hosp Epidemiol. 2018;39:425-33. doi:10.1017/ice.2018.16.

Park E, Chang HJ, Nam HS. A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors. Front Neurol. 2018 Sep 7;9:699. doi: 10.3389/fneur.2018.00699.

Gupta S, Sharma DK, Gupta MK. Artificial intelligence in diagnosis and management of ischemic stroke. Biomed J Sci & Tech Res 2019;13:9964–7. DOI: 10.26717/BJSTR.2019.13.002398

Shin H-C, RobertsK, LuL, et al. Learning to read chest x-rays: recurrent neural cascade model for automated image annotation. Paper presented at: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV; 2016;2497–506. doi.org/10.48550/arXiv.1603.08486

Khalili H, Rismani M, Nematollahi MA, et al. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep. 2023;13:960. doi:10.1038/s41598-023-28188-w.

Pérez Del Barrio A, Esteve Domínguez AS, Menéndez Fernández-Miranda P, et al. A deep learning model for prognosis prediction after intracranial hemorrhage. J Neuroimaging. 2023;33:218-26. doi:10.1111/jon.13078.

Sengupta J, Alzbutas R. Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies. Biomed Res Int. 2022;2022:5416726. doi: 10.1155/2022/5416726.

Miao K, Miao J. Diagnosis and Prognosis of Stroke Using Artificial Intelligence and Imaging (P11-5.018). Neurology. 2023;100(17_supplement_2):4732. doi: 10.1212/WNL.000000000020419

Gilson A, Safranek C, Huang T, et al. How Well Does ChatGPT Do When Taking the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment. medRxiv. 2022. doi.org/10.1101/2022.12.23.22283901.

Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health 2023;2:e0000198. doi.org/10.1371/journal.pdig.0000198.

Anders BA. Why ChatGPT is such a big deal for education. C2C Digital Magazine. 2023;1:4.

Jackson JL, Kuriyama A, Anton A, et al. The Accuracy of Google Translate for Abstracting Data from Non-English-Language Trials for Systematic Reviews. Ann Intern Med. 2019;171:677-9. doi:10.7326/M19-0891.

Earnshaw CH, Pedersen A, Evans J, Cross T, Gaillemin O, Vilches-Moraga A. Improving the quality of discharge summaries through a direct feedback system. Future Healthc J. 2020;7:149-54. doi:10.7861/fhj.2019-0046.

Chow JCL, Sanders L, Li K. Impact of ChatGPT on medical chatbots as a disruptive technology. Front Artif Intell. 2023 Apr 5;6:1166014. doi: 10.3389/frai.2023.1166014.

Hirschberg J, Manning CD. Advances in natural language processing. Science. 2015;349:261-6. doi: 10.1126/science.aaa8685.

Kovacek, D., and Chow, J. C. An AI-assisted chatbot for radiation safety education in radiotherapy. IOP SciNotes. 2021; 2:034002. doi:10.1088/2633-1357/ac1f88

Grigio TR, Timmerman H, Wolff AP. ChatGPT in anaesthesia research: risk of fabrication in literature searches. Br J Anaesth. 2023;131:e29-e30. doi:10.1016/j.bja.2023.04.009.

van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. 2023;614:224-226. doi:10.1038/d41586-023-00288-7.

O'Connor S. Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? [published correction appears in Nurse Educ Pract. 2023 Feb;67:103572]. Nurse Educ Pract. 2023;66:103537. doi:10.1016/j.nepr.2022.103537.

How to Cite

Thappa, P., Barik, A. K., Mohanty, C., Jangra, K., Reddy, A., Chouhan, R., … Luthra, A. (2024). Artificial intelligence and ChatGPT in neuroanesthesia and neurocritical practice: a revolution or a discombobulation. The Evidence, 2(3). https://doi.org/10.61505/evidence.2024.2.3.74
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