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Open Access 01-12-2023 | Artificial Intelligence | Research

Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care

Authors: I.A. Orlova, Zh.A. Akopyan, A.G. Plisyuk, E.V. Tarasova, E.N. Borisov, G.O. Dolgushin, E.I. Khvatova, M.A. Grigoryan, L.A. Gabbasova, A.A. Kamalov

Published in: BMC Health Services Research | Issue 1/2023

Abstract

Background

To date, no opinion surveys has been conducted among Russian physicians to study their awareness about artificial intelligence. With a survey, we aimed to evaluate the attitudes of stakeholders to the usage of technologies employing AI in the field of medicine and healthcare and identify challenges and perspectives to introducing AI.

Methods

We conducted a 12-question online survey using Google Forms. The survey consisted of questions related to the recognition of AI and attitudes towards it, the direction of development of AI in medicine and the possible risks of using AI in medicine.

Results

301 doctors took part in the survey. 107 (35.6%) responded that they are familiar with AI. The vast majority of participants considered AI useful in the medical field (85%). The advantage of AI was associated with the ability to analyze huge volumes of clinically relevant data in real time (79%). Respondents highlighted areas where AI would be most useful—organizational optimization (74%), biopharmaceutical research (67%), and disease diagnosis (52%). Among the possible problems when using AI, they noted the lack of flexibility and limited application on controversial issues (64% and 60% of respondents). 56% believe that AI decision making will be difficult if inadequate information is presented for analysis. A third of doctors fear that specialists with little experience took part in the development of AI, and 89% of respondents believe that doctors should participate in the development of AI for medicine and healthcare. Only 20 participants (6.6%) responded that they agree that AI can replace them at work. At the same time, 76% of respondents believe that in the future, doctors using AI will replace those who do not.

Conclusions

Russian doctors are for AI in medicine. Most of the respondents believe that AI will not replace them in the future and will become a useful tool. First of all, for optimizing organizational processes, research and diagnostics of diseases.

Trial registration

This study was approved by the Local Ethics Committee of the Lomonosov Moscow State University Medical Research and Education Center (IRB00010587).

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Hastie TT, Friedman J, Tibshirani R. The Elements of Statistical Learning. New York: Springer Science & Business Media; 2009.CrossRef

Abu-Mostafa Y, Magdon-Ismail M, Lin HT. Learning from Data. Learning From Data: New York: AMLbook.com, 2012. р. 201. ISBN 978-1-600-49006-4.

Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89–109.CrossRefPubMed

Pyo S, Lee J, Cha M, Jang H. Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets. PLoS One. 2017;12(11):e0188107.CrossRefPubMedPubMedCentral

Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131(2):129–33.CrossRefPubMed

Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46(1):5–17.CrossRefPubMed

Ohno-Machado L. Research on machine learning issues in biomedical informatics modeling. J Biomed Inform. 2004;37(4):221–3.CrossRefPubMed

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547–53.CrossRefPubMedPubMedCentral

Dreyer KJ, Geis JR. When machines think: radiology’s next frontier. Radiology. 2017;285(3):713–8.

10.

Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ. Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging. 2017;30(5):622–8.CrossRefPubMedPubMedCentral

11.

Rios VE, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 2017;77(14):3922–30.CrossRef

12.

Mordang J, Gubern-Mérida A, Bria A, Tortorella F, den Heeten HG, Karssemeijer N. Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings. Med Phys. 2017;44(4):1390–401.CrossRefPubMed

13.

Liu J, Hoffman J, Zhao J, Yao J, Lu L, Kim L, et al. Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest. Med Phys. 2016;43(7):4362.CrossRefPubMedPubMedCentral

14.

Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging. 2017;30(4):427–41.CrossRefPubMedPubMedCentral

15.

Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016;129(1434):73–6.PubMed

16.

Oh S, Kim JH, Choi S-W, Lee HJ, Hong J, Kwon SH. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. Journal of Medical Internet Research. 2019;21:e12422. https://doi.org/10.2196/12422.CrossRefPubMedPubMedCentral

17.

Scheetz J, Rothschild P, McGuinness M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11:5193. https://doi.org/10.1038/s41598-021-84698-5.CrossRefPubMedPubMedCentral

18.

Ketan Paranjape, MS, MBA, Michiel Schinkel, MD, Richard D Hammer, MD, Bo Schouten, MSc, R S Nannan Panday, MD, PhD, Paul W G Elbers, MD, PhD, Mark H H Kramer, MD, Prabath Nanayakkara, MD, PhD, The Value of Artificial Intelligence in Laboratory Medicine: Current Opinions and Barriers to Implementation, American Journal of Clinical Pathology, Volume 155, Issue 6, June 2021, Pages 823–831, https://doi.org/10.1093/ajcp/aqaa170.

19.

Blease C, Locher C, Leon-Carlyle M, Doraiswamy M. Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey. DIGITAL HEALTH. 2020. https://doi.org/10.1177/2055207620968355.CrossRefPubMedPubMedCentral

20.

Orly Ardon, PhD, MBA, Robert L Schmidt, MD, PhD, MBA, Clinical Laboratory Employees’ Attitudes Toward Artificial Intelligence, Laboratory Medicine, Volume 51, Issue 6, November 2020, Pages 649–654, https://doi.org/10.1093/labmed/lmaa023.

21.

Hugo Layard Horsfall, Paolo Palmisciano, Danyal Z. Khan, William Muirhead, Chan Hee Koh, Danail Stoyanov, Hani J. Marcus, Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey, World Neurosurgery, Volume 146, 2021, Pages e724-e730, https://doi.org/10.1016/j.wneu.2020.10.171

22.

Blease C, Bernstein MH, Gaab J, Kaptchuk TJ, Kossowsky J, Mandl KD, et al. (2018) Computerization and the future of primary care: A survey of general practitioners in the UK. PLoS ONE 13(12): e0207418. https://doi.org/10.1371/journal.pone.0207418.

23.

Abdullah R, Fakieh B Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study J Med Internet Res 2020;22(5):e17620: https://doi.org/10.2196/17620.

24.

Reffien MAM. Ellyana Mohamad Selamat, Hanani Nabilah Mohd Sobri, Mohd Fathulzhafran Mohamed Hanan, Mohd Ihsanuddin Abas, Muhammad Faiz Mohd Ishak, Noor Atika Azit, Noor Dalila Inche Zainal Abidin, Nor Hazmi Noor Hassim, Norain Ahmad, Sharifah Ain Shameera Syed Rusli, Sharifah Fazlinda Syed Nor and Aniza Ismail PHYSICIANS’ ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN MEDICINE, THEIR EXPECTATIONS AND CONCERNS: AN ONLINE MOBILE SURVEY Malaysian. J Public Health Med. 2021;21(1):181–9.

25.

Huisman M, Ranschaert E, Parker W, et al. An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education. Eur Radiol. 2021;31(11):8797–806. https://doi.org/10.1007/s00330-021-07782-426. (PintodosSantosD,GieseD,BrodehlS,etal.Medicalstudents'attitudetowardsartificialintelligence:amulticentresurvey.EuropeanRadiology,(2019),1640-1646,29(4)).

26.

Choudhury A, Asan O. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Human Factors in Healthcare. 2022;2:100021. https://doi.org/10.1016/j.hfh.2022.100021.

27.

Choudhury A, Asan O, Medow J. Effect of risk, expectancy, and trust on clinicians’ intent to use an artificial intelligence system -- Blood Utilization Calculator. Applied Ergonomics, (2022), 103708, 101: https://doi.org/10.1016/j.apergo.2022.103708.

Title: Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care
Authors: I.A. Orlova
Zh.A. Akopyan
A.G. Plisyuk
E.V. Tarasova
E.N. Borisov
G.O. Dolgushin
E.I. Khvatova
M.A. Grigoryan
L.A. Gabbasova
A.A. Kamalov
Publication date: 01-12-2023
Publisher: BioMed Central
Keyword: Artificial Intelligence
Published in: BMC Health Services Research / Issue 1/2023
Electronic ISSN: 1472-6963
DOI: https://doi.org/10.1186/s12913-023-09493-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care

Abstract

Background

Methods

Results

Conclusions

Trial registration

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Trial registration

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