IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i14p8735-d865257.html
   My bibliography  Save this article

Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills

Author

Listed:
  • Ana Suárez

    (Department of Preclinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain)

  • Alberto Adanero

    (Department of Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain)

  • Víctor Díaz-Flores García

    (Department of Preclinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain)

  • Yolanda Freire

    (Department of Preclinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain)

  • Juan Algar

    (Department of Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain)

Abstract

Knowing how to diagnose effectively and efficiently is a fundamental skill that a good dental professional should acquire. If students perform a greater number of clinical cases, they will improve their performance with patients. In this sense, virtual patients with artificial intelligence offer a controlled, stimulating, and safe environment for students. To assess student satisfaction after interaction with an artificially intelligent chatbot that recreates a virtual patient, a descriptive cross-sectional study was carried out in which a virtual patient was created with artificial intelligence in the form of a chatbot and presented to fourth and fifth year dental students. After several weeks interacting with the AI, they were given a survey to find out their assessment. A total of 193 students participated. A large majority of the students were satisfied with the interaction (mean 4.36), the fifth year students rated the interaction better and showed higher satisfaction values. The students who reached a correct diagnosis rated this technology more positively. Our research suggests that the incorporation of this technology in dental curricula would be positively valued by students and would also ensure their training and adaptation to new technological developments.

Suggested Citation

  • Ana Suárez & Alberto Adanero & Víctor Díaz-Flores García & Yolanda Freire & Juan Algar, 2022. "Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8735-:d:865257
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/14/8735/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/14/8735/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bach Xuan Tran & Roger S. McIntyre & Carl A. Latkin & Hai Thanh Phan & Giang Thu Vu & Huong Lan Thi Nguyen & Kenneth K. Gwee & Cyrus S. H. Ho & Roger C. M. Ho, 2019. "The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis," IJERPH, MDPI, vol. 16(12), pages 1-16, June.
    2. Na Liu & Philip Shapira & Xiaoxu Yue, 2021. "Tracking developments in artificial intelligence research: constructing and applying a new search strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3153-3192, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hajkowicz, Stefan & Sanderson, Conrad & Karimi, Sarvnaz & Bratanova, Alexandra & Naughtin, Claire, 2023. "Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021," Technology in Society, Elsevier, vol. 74(C).
    2. Farhat Chowdhury & Albert N. Link & Martijn Hasselt, 2022. "Public support for research in artificial intelligence: a descriptive study of U.S. Department of Defense SBIR Projects," The Journal of Technology Transfer, Springer, vol. 47(3), pages 762-774, June.
    3. Ana Teresa Santos & Sandro Mendonça, 2022. "Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7449-7470, December.
    4. Abderahman Rejeb & John G. Keogh & Suhaiza Zailani & Horst Treiblmaier & Karim Rejeb, 2020. "Blockchain Technology in the Food Industry: A Review of Potentials, Challenges and Future Research Directions," Logistics, MDPI, vol. 4(4), pages 1-26, October.
    5. Yiming Xiao & Han Wu & Guohua Wang & Hong Mei, 2021. "Mapping the Worldwide Trends on Energy Poverty Research: A Bibliometric Analysis (1999–2019)," IJERPH, MDPI, vol. 18(4), pages 1-22, February.
    6. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    7. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
    8. Tamíris Pacheco da Costa & James Gillespie & Xavier Cama-Moncunill & Shane Ward & Joan Condell & Ramakrishnan Ramanathan & Fionnuala Murphy, 2022. "A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    9. Bordoloi, Tausif & Shapira, Philip & Mativenga, Paul, 2022. "Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
    11. Yitong Chen & Keye Wu & Yue Li & Jianjun Sun, 2023. "Impacts of inter-institutional mobility on scientific performance from research capital and social capital perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3473-3506, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8735-:d:865257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.