Developing a taxonomy of decisions based on artificial intelligence technologies in health care practices
https://doi.org/10.35854/1998-1627-2024-7-819-831
Abstract
Aim. To conduct an analysis of research on the application of artificial intelligence (AI) technologies in medicine, norms and practices governing this field, and on its basis to build a taxonomy of AI-based decisions in the practice of medical services.
Objectives. To structure existing AI-based solutions in medicine; to identify, based on research and state registration data, the most mature areas of AI application and potential areas of development; to study the specific features of the applied technologies.
Methods. The authors using general methods of scientific cognition in various aspects considered the sphere of application of AI technologies in medicine, identified and systematized the characteristic features of the current state of this field and trends of further development.
Results. According to the results of the analysis of existing solutions in the field of AI application in medicine all solutions are divided by the degree of elaboration, main processes and type of used data. The constructed taxonomy is the first step in comprehending and structuring the existing AI solutions, possibilities of their use in the process of rendering various medical services.
Conclusions. Today, the most developed area of AI use in medicine is the analysis of medical images in the process of diagnosis, treatment and rehabilitation. Further development and introduction of these technologies into medical practice requires a more structured approach to assessing their effectiveness and efficiency, as well as solving a number of ethical and regulatory issues.
About the Authors
L. V. Lapidus
Lomonosov Moscow State University
Russian Federation
Competing Interests:
Russian Federation
Larisa V. Lapidus - D.Sc. in Economics, Professor, Head of the Laboratory of Applied Industrial Analysis of the Faculty of Economics
IstinaResearcherID (IRID): 7747618
Scopus Author ID: 56346948300
ResearcherID: AAZ-8362-2020
1-46 Leninskie Gory, Moscow 119991
Competing Interests:
The authors declare no conflict of interest related to the publication of this article.
O. M. Tokareva
Lomonosov Moscow State University
Russian Federation
Competing Interests:
Russian Federation
Oksana M. Tokareva - postgraduate student
IstinaResearcherID (IRID): 658167460
1-46 Leninskie Gory, Moscow 119991
Competing Interests:
The authors declare no conflict of interest related to the publication of this article.
References
1. Topol E.J. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. 2019;25(1):44-56. DOI: 10.1038/s41591-018-0300-7
2. Topol E. Deep medicine: How artificial intelligence can make healthcare human again. New York, NY: Basic Books; 2019. 400 p.
3. Lapidus L. Improving the quality of social services in modern conditions. Sovremennye problemy servisa i turizma = Service and Tourism: Current Challenges. 2014;8(2):34-41. (In Russ.). DOI: 10.12737/4308
4. Simchenko N.A., Safonov V.V. Possibilities of using artificial intelligence in the provision of medical services. Bol’shaya Evraziya: razvitie, bezopasnost’, sotrudnichestvo = Greater Eurasia: Development, Security, Cooperation. 2021;(4-1):668-669. (In Russ.).
5. Pérez-García F., Sparks R., Ourselin S. Torch IO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine. 2021;208:106236. DOI: 10.1016/j.cmpb.2021.106236
6. The passport of the national project “Healthcare” has been published. Government of Russia. Feb. 11, 2019. URL: http://government.ru/info/35561/ (accessed on 19.05.2024). (In Russ.).
7. Medtech market grew by 27% in 2023. Smart Ranking. Mar. 14, 2024. URL: https://smart-ranking.ru/ru/analytics/medicinskie-tehnologii/medtech-rynok-vyros-za-2023-god-na-27/ (accessed on 14.05.2024). (In Russ.).
8. On the fundamentals of health protection of citizens in the Russian Federation. Federal Law of November 21, 2011 No. 323-FZ. Official website of the Ministry of Health of the Russian Federation. URL: https://minzdrav.gov.ru/documents/7025-federalnyy-zakon-323-fz-ot-21-noyabrya-2011-g (accessed on 15.05.2024). (In Russ.).
9. Choudhury A., Asan O. Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Medical Informatics. 2020;8(7):e18599. DOI: 10.2196/18599
10. On the development of artificial intelligence in the Russian Federation. Decree of the President of the Russian Federation of September 10, 2019 No. 490. Official website of the President of Russia. URL: http://kremlin.ru/acts/bank/44731 (accessed on 15.05.2024). (In Russ.).
11. Busnatu Ș., Niculescu A.-G., Bolocan A., et al. Clinical applications of artificial intelligence — an updated overview. Journal of Clinical Medicine. 2022;11(8):2265. DOI: 10.3390/jcm11082265
12. Bi W.L., Hosny A., Schabath M.B., et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians. 2019;69(2):127-157. DOI: 10.3322/caac.21552
13. Liu X., Faes L., Kale A.U., et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health. 2019;1(6):e271-e297. DOI: 10.1016/S2589-7500(19)30123-2
14. Panayides A.S., Amini A., Filipovic N.D., et al. AI in medical imaging informatics: Current challenges and future directions. IEEE Journal of Biomedical and Health Informatics. 2020;24(7):1837-1857. DOI: 10.1109/JBHI.2020.2991043
15. Chen J., Remulla D., Nguyen J.H., Dua A., Liu Y., Dasgupta P., Hung A.J. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU International. 2019;124(4):567-577. DOI: 10.1111/bju.14852
16. Rajpurkar P., Chen E., Banerjee O., Topol E.J. AI in health and medicine. Nature Medicine. 2022;28(1):31-38. DOI: 10.1038/s41591-021-01614-0
17. Lu Z.-X., Qian P., Bi D., et al. Application of AI and IoT in clinical medicine: Summary and challenges. Current Medical Science. 2021;41(6):1134-1150. DOI: 10.1007/s11596-021-2486-z
18. Results of computer vision development in one year. Habr. Jan. 06, 2018. URL: https://habr.com/ru/articles/346140/ (accessed on 15.04.2024). (In Russ.).
19. Artificial intelligence and machine learning (AI/ML) – enabled medical devices. FDA. Aug. 07, 2024. URL: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (accessed on 15.05.2024).
20. List of domestic medical products based on artificial intelligence technologies approved by Roszdravnadzor as of April 4, 2023. Portal for Operational Interaction of Participants of the Unified State Information System in the Field of Healthcare. URL: https://portal.egisz.rosminzdrav.ru/news/855 (accessed on 15.05.2024). (In Russ.).
21. Van Leeuwen K.G., Schalekamp S., Rutten M.J.C.M., van Ginneken B., de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. European Radiology. 2021;31(6):3797-3804. DOI: 10.1007/s00330-021-07892-z
22. Williams S., Horsfall H.L., Funnell J.P., et al. Artificial intelligence in brain tumour surgery — an emerging paradigm. Cancers. 2021;13(19):5010. DOI: 10.3390/cancers13195010
23. Kelly C.J., Karthikesalingam A., Suleyman M., Corrado G., King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 2019;17(1):195. DOI: 10.1186/s12916-019-1426-2
24. Sutton R.T., Pincock D., Baumgart D.C., Sadowski D.C., Fedorak R.N., Kroeker K.I. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine. 2020;3(1):17. DOI: 10.1038/s41746-020-0221-y
25. Berner E.S., La Lande T.J. Overview of clinical decision support systems. In: Berner E.S., ed. Clinical decision support systems. Health Informatics. Cham: Springer-Verlag; 2016:1-17. DOI: 10.1007/978-3-319-31913-1_1
26. Alshehri F., Muhammad G. A comprehensive survey of the Internet of things (IoT) and AI-based smart healthcare. IEEE Access. 2021;9:3660-3678. DOI: 10.1109/ACCESS.2020.3047960
27. Manickam P., Mariappan S.A., Murugesan S.M., et al. Artificial intelligence (AI) and Internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors. 2022;12(8):562. DOI: 10.3390/bios12080562
28. Kavidha V., Gayathri N., Kumar S.R. AI, IoT and robotics in the medical and healthcare field. In: Dubey A.K., Kumar A., Kumar S.R., et al., eds. AI and IoT-based intelligent automation in robotics. Beverly, MA: Scrivener Publishing LLC; 2021:165-187. DOI: 10.1002/9781119711230.ch10
Review
For citations:
Lapidus L.V., Tokareva O.M. Developing a taxonomy of decisions based on artificial intelligence technologies in health care practices. Economics and Management. 2024;30(7):819-831. (In Russ.) https://doi.org/10.35854/1998-1627-2024-7-819-831
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