Artificial intelligence as a coming revolution in medicine
DOI:
https://doi.org/10.12775/JEHS.2023.16.01.013Keywords
Radiology, Informatics, Technology, MedicineAbstract
Introduction: The development of medicine and information technology in recent decades has undoubtedly contributed to improving public health. Artificial intelligence is a technology that has great potential to revolutionize the functioning of health care around the world. Appropriate use of the development of technology can revolutionize many areas of modern medicine, however, it should not be forgotten that this technology should be subjected to appropriate standardization and legal regulation.
Objective: The purpose of this study is to review the available scientific literature in order to systematize the current knowledge on the use of artificial intelligence in the process of diagnosis and treatment. Ethical aspects related to the implementation of AI for use in health care are also analyzed.
Results: Artificial intelligence uses deep machine learning algorithms. It is a technology that has been known for a long time, but recently the chances of its widespread use have increased significantly, although scientists still do not fully understand the operation of AI algorithms. Currently, there are attempts to use this technology in many medical fields such as cardiology, diagnostic imaging, gastroenterology, pathomorphology, ultrasound. Artificial intelligence can also be used to improve the functioning of patient service in health care.
Summary: The development of artificial intelligence algorithms creates a huge opportunity to improve the quality of diagnostic and treatment processes. The current rapid development of the technology is revolutionizing many branches of medicine, improving treatment outcomes. However, the development of this technology requires the creation of an appropriate law governing AI in medicine.
References
Kowski, Ryszard. "Kongres radiologiczny." Inżynier i Fizyk Medyczny 9.6 (2020).
Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004 Sep;86(5):334-8. doi: 10.1308/147870804290. PMID: 15333167; PMCID: PMC1964229.
Kundu, Shinjini. "AI in medicine must be explainable." Nature medicine 27.8 (2021): 1328-1328.
Trusz-Gluza M Zaburzenia rytmu serca i przewodzenia Interna Szczeklika 2019 Medycyna Praktyczna (227-295)
Sandhu RK, Healey JS. Screening for undiagnosed atrial fibrillation. Expert Rev Cardiovasc Ther. 2018 Aug;16(8):591-598. doi: 10.1080/14779072.2018.1496018. Epub 2018 Jul 16. PMID: 29963930.
Mintu P. Turakhia, Manisha Desai, Haley Hedlin, Amol Rajmane, Nisha Talati, Todd Ferris, Sumbul Desai, Divya Nag, Mithun Patel, Peter Kowey, John S. Rumsfeld, Andrea M. Russo, Mellanie True Hills, Christopher B. Granger, Kenneth W. Mahaffey, Marco V. Perez, Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study, American Heart Journal, Volume 207, 2019, Pages 66-75,
Tison, Geoffrey H., et al. "Passive detection of atrial fibrillation using a commercially available smartwatch." JAMA cardiology 3.5 (2018): 409-416.
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N et al. Electrocardiogram-based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation Circulation . 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov
Lim GB. AI used to detect cardiac murmurs. Nat Rev Cardiol. 2021 Jul;18(7):460. doi: 10.1038/s41569-021-00567-8. PMID: 33976396.
Thompson WR, Reinisch AJ, Unterberger MJ, Schriefl AJ. Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial. Pediatr Cardiol. 2019 Mar;40(3):623-629. doi: 10.1007/s00246-018-2036-z. Epub 2018 Dec 12. PMID: 30542919.
Suzuki, K. Overview of deep learning in medical imaging. Radiol Phys Technol 10, 257–273 (2017). https://doi.org/10.1007/s12194-017-0406-5
Rodríguez-Ruiz, Alejandro, et al. "Detection of breast cancer with mammography: effect of an artificial intelligence support system." Radiology 290.2 (2019): 305-314.
Yoon JH, Kim EK. Deep Learning-Based Artificial Intelligence for Mammography. Korean J Radiol. 2021 Aug;22(8):1225-1239. doi: 10.3348/kjr.2020.1210. Epub 2021 May 4. PMID: 33987993; PMCID: PMC8316774.
Denisiewicz, Natalia, et al. "NAJLEPSZE ZASTOSOWANIA SZTUCZNEJ INTELIGENCJI W RADIOLOGII." Innowacje w medycynie: 240.
Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol. 2019 Apr 14;25(14):1666-1683. doi: 10.3748/wjg.v25.i14.1666. PMID: 31011253; PMCID: PMC6465941.
Briganti, Giovanni, and Olivier Le Moine. "Artificial intelligence in medicine: today and tomorrow." Frontiers in medicine 7 (2020): 27.
Drukker, L et al. “Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.” Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology vol. 56,4 (2020): 498-505. doi:10.1002/uog.22122
Altamimi, Ibraheem et al. “Artificial Intelligence (AI) Chatbots in Medicine: A Supplement, Not a Substitute.” Cureus vol. 15,6 e40922. 25 Jun. 2023, doi:10.7759/cureus.40922
Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018 Mar 15;378(11):981-983. doi: 10.1056/NEJMp1714229. PMID: 29539284; PMCID: PMC5962261.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019; 366(6464):447-453.
Angwin, Julia, et al. "Machine bias. ProPublica, May 23, 2016." (2016).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Rami Ben Rhaiem, Grzegorz Tarsa, Katarzyna Sudelska, Zuzanna Sawińska, Przemysław Kępka, Aleksandra Łokczewska-Bojar, Daria Kuziemkowska, Jan Kuźma, Magdalena Skotalczyk, Anna Łącka-Majcher
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 293
Number of citations: 0