Acil Tıp ve Yapay Zeka

Year 2021, Volume: 4 Issue: 3, 114 - 117, 30.09.2021

Mehmet Mahir Kunt , Mehmet Ali Karaca , Bülent Erbil Erhan Akpınar

Abstract

Yapay Zeka, bilgi ve teknoloji çağında günlük hayatımızda önemli ölçüde yer edinmiştir. Son yıllarda yapay zeka ve makine öğrenimi teknikleri kullanılması öğrenimi özellikle acil tıp başta olmak üzere tıbbın bir çok alanında hızlıca gelişmektedir. Yapay zeka, acil tıp içindeki tanısal görüntülemenin yorumlanması, hasta sonlanımının tahmin edilmesi ve hastanın yaşamsal bulgularının izlenmesi dahil sayısız uygulamada umut vaat etmektedir. Bu derlemede yapay zekanın acil tıpta kullanımına yönelik son yıllarda yapılan çalışmalar toplanmıştır.

Keywords

Yapay zeka, makina öğrenmesi, acil tıp

Emergency Medicine and Artificial Intelligence

Abstract

Artificial Intelligence has taken a significant place in our daily lives in the age of information and technology. In recent years, learning to use artificial intelligence and machine learning techniques has been developing rapidly in many fields of medicine, especially in emergency medicine. Artificial intelligence holds promise in numerous applications in emergency medicine, including interpreting diagnostic imaging, predicting patient outcome, and monitoring patient vital signs. In this review, recent studies on the use of artificial intelligence in emergency medicine were discussed.

Keywords

Artificial intelligence, machine learning, emergency medicine

References

• Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 2019;62(1):15-25. doi:10.1016/J.BUSHOR.2018.08.004
• Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manage Rev. 2019;61(4):5-14. doi:10.1177/0008125619864925
• Eliza (elizabot.js). Accessed September 16, 2021. https://www.masswerk.at/elizabot/
• Campbell M, Hoane AJ, Hsu F-H. Deep Blue. Artif Intell. 2002;134:57-83.
• Morris RGM. D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949. Brain Res Bull. 1999;50(5-6):437. doi:10.1016/S0361-9230(99)00182-3
• Silver D, Huang A, Maddison CJ, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484-489. doi:10.1038/nature16961
• Shafaf N, Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Arch Acad Emerg Med. 2019;7(1):1-9. Accessed September 19, 2021. /pmc/articles/PMC6732202/
• Stead WW. Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA. 2018;320(11):1107-1108. doi:10.1001/JAMA.2018.11029
• AI for Patient Consultations. Accessed September 19, 2021. https://www.corti.ai/
• Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. EMA - Emerg Med Australas. 2018;30(6):870-874. doi:10.1111/1742-6723.13145
• Berlyand Y, Raja AS, Dorner SC, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36(8):1515-1517. doi:10.1016/j.ajem.2018.01.017
• Center for Health Statistics N. National Hospital Ambulatory Medical Care Survey: 2014 Emergency Department Summary Tables. Accessed September 20, 2021. http://www.cdc.gov/nchs/ahcd/ahcd_survey_instruments.htm#nhamcs.
• Levin S, Toerper M, Hamrock E, et al. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med. 2018;71(5):565-574.e2. doi:10.1016/j.annemergmed.2017.08.005
• Sun Y, Heng BH, Seow YT, et al. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009;9. doi:10.1186/1471-227X-9-1
• Jones SS, Evans RS. An agent based simulation tool for scheduling emergency department physicians. AMIA Annu Symp Proc. Published online 2008:338-342.
• Li Y-H, Zhang L, Hu Q-M, et al. Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. Int J Comput Assist Radiol Surg. 2011;7(4):507-516. doi:10.1007/S11548-011-0664-3
• Yuh EL, Gean AD, Manley GT, et al. Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. In: Journal of Neurotrauma. Vol 25. ; 2008:1163-1172. doi:10.1089/neu.2008.0590
• Xiao F, Liao CC, Huang KC, et al. Automated assessment of midline shift in head injury patients. Clin Neurol Neurosurg. 2010;112(9):785-790. doi:10.1016/J.CLINEURO.2010.06.020
• Sjogren AR, Leo MM, Feldman J, et al. Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: A pilot study. J Ultrasound Med. 2016;35(11):2501-2509. doi:10.7863/ultra.15.11017
• Knackstedt C, Bekkers SCAM, Schummers G, et al. Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain the FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015;66(13):1456-1466. doi:10.1016/j.jacc.2015.07.052
• NH C. Ambient virtual scribes: Mutuo Health’s AutoScribe as a case study of artificial intelligence-based technology. Healthc Manag forum. 2020;33(1):34-38. doi:10.1177/0840470419872775
• Shashikumar SP, Stanley MD, Sadiq I, et al. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. J Electrocardiol. 2017;50(6):739-743. doi:10.1016/j.jelectrocard.2017.08.013
• Muniz GW, Wampler DA, Manifold CA, et al. Promoting early diagnosis of hemodynamic instability during simulated hemorrhage with the use of a real-time decision-assist algorithm. J Trauma Acute Care Surg. 2013;75(2 SUPPL. 2). doi:10.1097/TA.0B013E31829B01DB
• Zhang P-I, Hsu C-C, Kao Y, et al. Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain. Scand J Trauma, Resusc Emerg Med 2020 281. 2020;28(1):1-7. doi:10.1186/S13049-020-00786-X
• Naylor CD. On the Prospects for a (Deep) Learning Health Care System. JAMA. 2018;320(11):1099-1100. doi:10.1001/JAMA.2018.11103
• Grant K, McParland A, Mehta S, Ackery AD. Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential. Ann Emerg Med. 2020;75(6):721-726. doi:10.1016/j.annemergmed.2019.12.024
• Aslam AA, Tsou MH, Spitzberg BH, et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res. 2014;16(11). doi:10.2196/jmir.3532
• Burnap P, Colombo G, Amery R, et al. Multi-class machine classification of suicide-related communication on Twitter. Online Soc Networks Media. 2017;2:32-44. doi:10.1016/J.OSNEM.2017.08.001
• Grant K, McParland A. Applications of artificial intelligence in emergency medicine. Univ Toronto Med J. 2019;96(1):37-39