Is artificial intelligence necessary for healthcare system?

Close metadata

Recommended articles:

Cost-effectiveness analysis of low-dose computed tomography for lung cancer screening. Medical Technologies. Assessment and Choice. 2024;(1):60-73

Clinical and economic effectiveness of thrombopoietin receptor agonists in the treatment of primary immune thrombocytopenia in the Moscow region. Medical Technologies. Assessment and Choice. 2024;(1):74-82

Digital healthcare of the Russian Federation: basic concepts for practitioners. Russian Journal of Preventive Medicine. 2024;(1):97-105

Features of medical care provision in the profile «Maxillofacial surgery» in the Southern Federal District of the Russian Federation. Stomatology. 2024;(1):23-30

Comparison of the predictive values of traditional cardiovascular risk assessment methods using SCORE and FRAMINGHAM scales, machine learning technologies «INTEREPID». Russian Journal of Preventive Medicine. 2024;(2):96-102

First experience of using CAD EYE technology. Artificial intelligence-assisted colonoscopy in children. (A series of clinical cases). Russian Journal of Evidence-Based Gastroenterology. 2024;(1):94-100

Development and testing of new methodical approaches for predicting cardiovascular events in healthy people using machine learning technology based on the «INTEREPID» international research. Russian Journal of Preventive Medicine. 2024;(3):72-79

Artificial intelligent systems in the development of assisted reproductive technologies. Russian Bulletin of Obstetrician-Gynecologist. 2024;(2):19-29

Artificial intelligence in pathological anatomy. Russian Journal of Archive of Pathology. 2024;(2):65-71

The role of telemedicine in dermatology with examples on the organization of services for patients with psoriasis and acne. Russian Journal of Clinical Dermatology and Venereology. 2024;(2):111-117

Artificial intelligence (AI) is a relatively new technology in healthcare. Its development takes place due to combination of various factors. To date, these technologies are being actively introduced into healthcare system to optimize the work of medical personnel, ensure access and quality of medical care. At the same time, AI raises many questions like any innovation coming to a fairly conservative industry. The main ones are issues of efficiency and economic acceptability. Scientific data on AI effectiveness are not yet convincing enough to talk about its positive impact on healthcare system. Like any innovative medical technology, AI increases the cost of medical care and burden on healthcare system. Answers to this question comprise further research of AI in healthcare. It is necessary to provide correct assessment of such technologies and determine the methods for this assessment. As a starting point for discussion, it seems possible to draw a parallel between digital technologies (AI) and pharmaceutical technologies (medicines). Pharmacoeconomics has all necessary tools for such studies. The last ones have been successfully used for a long time. In this article, the authors discuss the issues related to definition of AI, its types and features as medical technology regarding possible impact on healthcare system. Introduction of AI into medicine considering specific features of application and challenges facing the entire health system is analyzed. A special attention is paid to AI assessment methods necessary for decision-makers on introduction of modern innovations into healthcare system.

Keywords:

artificial intelligence

healthcare

medical imaging

medical decision support systems

cost-effectiveness analysis

health technology assessment

Authors:

Morozov D.Yu.

Center for Expertise and Quality Control of Medical Care;
Russian Medical Academy of Continuous Professional Education

ORCID: 0000-0003-2063-6287

Omelyanovskiy V.V.

Center for Expertise and Quality Control of Medical Care;
Russian Medical Academy of Continuous Professional Education;
Research Financial Institute;
Semashko National Research Institute of Public Health

ORCID: 0000-0003-1581-0703

Received:

30.05.2024

Accepted:

29.07.2024

List of references:

Burrell A, Zsombor Z, Champion A, Carl A. How Useful Are Digital Health Terms for Outcomes Research? An ISPOR Special Interest Group Report. Value in Health. 2022;25(9):1469-1479. https://doi.org/10.1016/j.jval.2022.04.1730
Global’naya strategiya v oblasti cifrovogo zdravoohraneniya na 2020—2025 gg. Zheneva: Vsemirnaya organizaciya zdravoohraneniya; 2021. Accessed Marth 06, 2024. (In Russ.). https://iris.who.int/bitstream/handle/10665/344249/9789240027596-rus.pdf
Trinadcatuya obshchuya programma raboty na 2019—2023 gg. Accessed Marth 06, 2024. (In Russ.) https://apps.who.int/gb/ebwha/pdf_files/EB152/B152_28-ru.pdf
Pugachev PS, Gusev AV, Kobyakova OS, Kadyrov FN, Gavrilov DV, Novitskii RE, Vladzimirskii AV. Global trends in the digital transformation of the healthcare industry. National Health Care (Russia).2021;2(2):5-12. (In Russ.). https://doi.org/10.47093/2713-069X.2021.2.2.5-12
Gospic KAM, Passmore G. Importance of AI in Medicine. In: Lidströmer N, Ashrafian H, eds. Artificial Intelligence in Medicine. Cham: Springer; 2021: 1-16. https://doi.org/10.1007/978-3-030-58080-3_277-1
Gusev AV, Dobridnyuk SL. Artificial intelligence in medicine and healthcare. Informacionnoe obshchestvo. 2017;(4-5):78-93. (In Russ.).
Artificial Intelligence (Al) in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Application (Medical Imaging & Diagnostics, Patient Data & Risk Analysis), End User & Region — Global Forecast to 2029. Accessed October 02, 2024. https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcaremarket-54679303.html
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015—20): a comparative analysis. The Lancet Digital Health. 2021;3(3):e195-e203. https://doi.org/10.1016/S2589-7500(20)30292-2
Kiseleva AYu. Application of artificial intelligence in healthcare: aspects of medical law. Medicinskoe Pravo: Teoriya i Praktika. 2020;6(2):29-38. (In Russ.).
Lim C-P,·Chen Y-W,·Vaidya A, Mahorkar C,· Jain LC. Handbook of Artificial Intelligence in Healthcare. Vol 2: Practicalities and Prospects Cham: Springer International Publishing; 2022.
Artificial intelligence in health care: within touching distance. Lancet. 2017;390(10114):2739. https://doi.org/10.1016/S0140-6736(17)31540-4
McKinney SM, Sieniek M, Godbole V, Godwinet J, Antropova N, Ashrafian H, et. al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. https://doi.org/10.1038/s41586-019-1799-6
Morozov SP, Gavrilov AV, Arkhipov IV, Dolotova DD, Lysenko MA, Tsarenko SV, et al. Effect of artificial intelligence technologies on the CT scan interpreting time in COVID-19 patients in inpatient setting. Russian Journal of Preventive Medicine.2022; 25(1):14-20. (In Russ.). https://doi.org/10.17116/profmed20222501114
Gusev AV, Tokarev SA, Gavrilov DV, Kuznecova TYu. Primenenie sistemy podderzhki prinyatiya vrachebnyh reshenij v dispanserizacii vzroslogo naseleniya dlya kontrolya ocenki urovnya serdechno-sosudistogo riska. Menedzhment kachestva v medicine. 2022;(1):72-79. (In Russ.).
Michie S, Yardley L, West R, Patrick K, and Greaves F. Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting from an International Workshop. Journal of Medical Internet Research. 2017;19(6):e232. https://doi.org/10.2196/jmir.7126
Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The Ethical, Legal and Social Implications of Using Artificial Intelligence Systems in Breast Cancer Care. The Breast. 2019;49:25-32. https://doi.org/10.1016/j.breast.2019.10.001
Morozov SP, Vladzimirskiy AV, Ledikhova NV, Sokolina IA, Kulberg NS, Gombolevskiy VA. Evaluation of diagnostic accuracy of the system for pulmonary tuberculosis screening based on artificial neural networks. Tuberkulez i bolezni legkix. 2018;96(8):42-49. (In Russ.). https://doi.org/10.21292/2075-1230-2018-96-8-42-49
Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ. 2021;374:n1872. https://doi.org/10.1136/bmj.n1872
Bright TJ, Wong A, Dhurjati R, Erin B, Lori B, Remy RC, et al. Effect of clinical decision support systems. A systematic review. Annals of Internal Medicine. 2012;157(1):29-43. https://doi.org/10.7326/0003-4819-157-1-201207030-00450
Data science and AI in the age of COVID-19. Reflections on the response of the UK’s data science and AI community to the COVID-19 pandemic. Accessed October 02, 2024. https://www.turing.ac.uk/sites/default/files/2021-06/data-science-and-ai-in-the-age-of-covid_full-report_2.pdf?uploadToLiner=true
Mårtensson G, Ferreira D, Granberg T, Cavallin L, Oppedal K, Padovani A, et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study. Medical Image Analysis. 2020;66:101714. https://doi.org/10.1016/j.media.2020.101714
Bhati D, Deogade MS, Kanyal D. Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review. Cureus. 2023;15(10):e47731. https://doi.org/10.7759/cureus.47731
Global spending on health: a world in transition. Geneva: World Health Organization; 2019. Accessed October 02, 2024. https://www.who.int/publications/i/item/WHO-HIS-HGF-HFWorkingPaper-19.4
Global Burden of Disease Health Financing Collaborator Network. Past, present, and future of global health financing: a review of development assistance, government, out-of-pocket, and other private spending on health for 195 countries, 1995—2050. Lancet. 2019;393(10187):2233-2260. https://doi.org/10.1016/s0140-6736(19)30841-4
Hill NR, Sandler B, Mokgokong R, Steven L, Thomas W, Rebecca B, et al. Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm. Journal of Medical Economics.2020;23(4):386-393. https://doi.org/10.1080/13696998.2019.1706543
Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. Oxford University Press; 2015.
Chernina VY, Belyaev MG, Silin AY, Avetisov IO, Pyatnitskiy IA, Petrash EA, et.al. Analysis of the diagnostic and economic impact of the combined artificial intelligence algorithm for analysis of 10 pathological findings on chest computed tomography. Digital Diagnostics. 2023;4(2):105-132. (In Russ.). https://doi.org/10.17816/DD321963
2021 AI Index Report. Accessed October 02, 2024. https://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-Index-Report_Master.pdf
Voets MM, Veltman J, Slump CH, Siesling S, Koffijberg H. Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah. Value in Health.2022; 25(3):344-348. https://doi.org/10.1016/j.jval.2021.11.1362

Close metadata