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. 2022 Jul 18;19(14):8735.
doi: 10.3390/ijerph19148735.

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

Ana Suárez  1 , Alberto Adanero  2 , Víctor Díaz-Flores García  1 , Yolanda Freire  1 , Juan Algar  2
Affiliations

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

Ana Suárez et al. Int J Environ Res Public Health. .

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.

Keywords: artificial intelligent; chatbot; dental students; diagnosis; virtual patient.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
N: population size; z: z-score; e: margin of error (percentage in decimal form).
Figure 2
Figure 2
Chatbot conceptualization diagram.
Figure 3
Figure 3
Data flow diagram.
Figure 4
Figure 4
Example of a conversation flow. (A) At the beginning of the interaction with Julia, she introduces herself and makes directions about what the student should do. (B) Julia is able to answer different questions about the current condition. (C) Colloquial responses to intimate questions that were unrelated to the case were established in order to arouse students’ curiosity and redirect them. (D) In case of reaching an incorrect diagnosis, Julia redirects the student.
Figure 5
Figure 5
If the user misspelled a word and the AI was able to identify that it was an error and associate it with the correct intent.
Figure 6
Figure 6
Distribution of responses per questionnaire item by fourth year dental students. 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree.
Figure 7
Figure 7
Distribution of responses per questionnaire item by fifth year dental students. 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree.
See this image and copyright information in PMC

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