Chatbots is the modern reality of consulting in medicine

Cover Page

Cite item

Full Text

Abstract

Introduction. Modern digital innovations and artificial intelligence technologies are being actively introduced in Medicine. Now chatbots are able to provide consulting services and make appointments for patients, make a diagnosis. Chatbots can significantly improve the efficiency and accuracy of symptom detection, assist in remote biomonitoring.

Goal. To study the possibilities of development and directions of implementation of chatbots based on artificial intelligence technologies in medicine and to assess the potential of their application.

Material and methods. The study is prospective, includes analysis of secondary information and conducting an expert interview on issues related to the development, application practice, and distribution of chatbots.

Results. The survey showed most experts already to see the need to introduce chatbots in Medicine. The main advantages are: getting an “instant” response and saving patients’ time. The disadvantages of using chatbots may be: “incorrect interpretation” of both user requests and interpretation by patients. Experts see risks in the “erroneous” diagnosis and in the “measure of responsibility”.

Limitations of research. The research materials are limited by the results of an expert survey conducted in 2023 and the quantitative and qualitative characteristics of the respondents who met the requirements for experts.

Conclusions. Chatbots in the field of healthcare have already become a reality in consulting and providing the necessary medical information. Thanks to the development of information technologies, chatbots are able to process significant amounts of data received from patients, quickly and accurately find answers, provide information support, and establish a preliminary diagnosis. Such solutions can reduce the burden on medical professionals and increase patient satisfaction.

Compliance with ethical standards. The conclusion of the biomedical ethics committee was not required to conduct this study (the study was carried out on publicly available information and data obtained as a result of expert interviews).

Contribution of the authors:
Aksenova E.I. — concept and design of the study, writing an article;
Medvedeva E.I. — concept and design of the study, writing the article, editing;
Kroshilin S.V. — collection and processing of material, statistical processing, writing an article, editing.
All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.

Acknowledgment. The study had no sponsorship.

Conflict of interest. The authors declare no conflict of interest.

Received: June 22, 2023
Accepted: August 23, 2023
Published: November 3, 2023

About the authors

Elena I. Aksenova

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department; Peoples’ Friendship University of Russia named after Patrice Lumumba

Author for correspondence.
Email: noemail@neicon.ru
ORCID iD: 0000-0003-1600-1641
Russian Federation

Elena I. Medvedeva

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department; Institute of Socio-Economic Studies of Population named after N.M. Rimashevskaya — Branch of the Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences

Email: noemail@neicon.ru
ORCID iD: 0000-0003-4200-1047
Russian Federation

Sergey V. Kroshilin

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department; Institute of Socio-Economic Studies of Population named after N.M. Rimashevskaya — Branch of the Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences; I.P. Pavlov Ryazan State Medical University

Email: krosh_sergey@mail.ru
ORCID iD: 0000-0002-6070-1234

MD, PhD, Researcher at the Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department, Moscow, 115088, Russian Federation.

e-mail: krosh_sergey@mail.ru

Russian Federation

References

  1. Elizarova M.I., Urazova K.M., Ermashov S.N. Artificial intelligence in medicine. International Journal of Professional Science. 2021; (5): 81–5. https://elibrary.ru/owaclc
  2. Weiss S., Kulikowski C.A., Safir A. Glaucoma consultation by computer. Comput. Biol. Med. 1978; 8(1): 25–40. https://doi.org/10.1016/0010-4825(78)90011-2
  3. Cheremiskin Yu.V. Pharmacotherapy order entry by means of clinical information system DOCA+: reaction of Krasnozyorsk central regional hospital physicians on messages of proactive functions. Vrach i informatsionnye tekhnologii. 2011; (1): 43–9. https://elibrary.ru/nmzgpn (in Russian)
  4. Li X. Artificial intelligence neural network based on intelligent diagnosis. J. Ambient Intell. Human Comput. 2020; 12(1): 923–31. https://doi.org/10.1007/s12652-020-02108-6
  5. Arul K., Jayanthy T. Application of back propagation artificial neural network in detection and analysis of diabetes mellitus. J. Ambient Intell. Human Comput. 2020; 12(7): 7063–70. https://doi.org/10.1007/s12652-020-02371-7
  6. Zharkova O.S., Sharopin K.A., Seidova A.S., Berestneva E.V., Osadchaya I.A. Building decision support systems in medicine based on decision trees. Sovremennye naukoemkie tekhnologii. 2016; (6-1): 33–7. https://elibrary.ru/wcduod (in Russian)
  7. Astakhova I.F., Kiseleva E.I. Intelligent support for decision-making. Sovremennye informatsionnye tekhnologii i IT-obrazovanie. 2020; 16(3): 664–72. https://doi.org/10.25559/SITITO.16.202003.664-672 https://elibrary.ru/zzloeo (in Russian)
  8. Pombo N., Arabjo P., Viana J. Knowledge discovery in clinical decision support systems for pain management. Artif. Intell. Med. 2014; 60(1): 1–11. https://doi.org/10.1016/j.artmed.2013.11.005
  9. Liu N., Liu Y., Logan B., Xu Z., Tang J., Wang Y. Learning the dynamic treatment regimes from medical registry data through deep Q-network. Sci. Rep. 2019; 9(1): 1495. https://doi.org/10.1038/s41598-018-37142-0
  10. Harutyunyan H., Khachatrian H., Kale D.C., Ver Steeg G., Galstyan A. Multitask learning and benchmarking with clinical time series data. Sci. Data. 2019; 6(1): 96. https://doi.org/10.1038/s41597-019-0103-9
  11. Istepanian R.S.H., Al-Anzi T. m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics. Methods. 2018; 151: 34–40. https://doi.org/10.1016/j.ymeth.2018.05.015
  12. Reddy A.V.N., Satapathy S.K., Krishna C.P., Mallick P.K., Tiwari P., Zymbler M., et al. Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks. J. Big Data. 2020; 7(1): 1–17. https://doi.org/10.1186/s40537-020-00311-y https://elibrary.ru/jxnxmu
  13. Alikperova N.V. Artificial intelligence in medicine: the search for new opportunities. In: Proceedings of the Research Institute of Healthcare Organization and Medical Management [Trudy nauchno-issledovatel’skogo instituta organizatsii zdravookhraneniya i meditsinskogo menedzhmenta]. Moscow; 2022: 94–7. https://elibrary.ru/xtqjck (in Russian)
  14. Yarasheva A.V., Aleksandrova O.A., Medvedeva E.I., Alikperova N.V., Kroshilin S.V. Problems and prospects of personnel support of the Moscow healthcare system. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz. 2020; 13(1): 174–90. https://doi.org/10.15838/esc.2020.1.67.10 https://elibrary.ru/oxryhb (in Russian)
  15. Medvedeva E.I., Aleksandrova O.A., Kroshilin S.V. Telemedicine in modern conditions: the attitude of society and the vector of development. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz. 2022; 15(3): 200–22. https://doi.org/10.15838/esc.2022.3.81.11 (in Russian)
  16. Reshetnikova Yu.S., Sharapova O.V., Katkova A.L., Nesterova O.A., Brynza N.S., Petrov I.M. The profile of the patient to be ready to use digital technologies and artificial intelligence methods when receiving medical care. Zdravookhranenie Rossiyskoy Federatsii. 2022; 66(1): 20–6. https://doi.org/10.47470/0044-197X-2022-66-1-20-26 https://elibrary.ru/vghdcc (in Russian)
  17. Dillon S. The Eliza effect and its dangers: from demystification to gender critique. J. Cult. Res. 2020; 24(1): 1–15. https://doi.org/10.1080/14797585.2020.1754642
  18. Ayers J.W., Poliak A., Dredze M., Leas E.C., Zhu Z., Kelley J.B., et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. 2023; 183(6): 589–96. https://doi.org/10.1001/jamainternmed.2023.1838

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2023 Aksenova E.I., Medvedeva E.I., Kroshilin S.V.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ:  ПИ № ФС77-50668 от 13.07.2012 г.