International Journal of

INTELLIGENT SYSTEMS AND APPLICATIONS

IN ENGINEERING
ISSN:2147-6799 www.ijisae.org Original Research Paper

Knowledge Attitude and Practices of Dental Students and

Dental Practitioners Towards Artificial Intelligence

M. Shiva Thulasi 1, Bathula Sowjanya 2, Dr. K. Sreenivasulu3, M. Rudra Kumar *4

Submitted: 06/06/2022 Accepted: 10/09/2022

Abstract: Background: Artificial intelligence (A.I.) and its subsets, machine learning (ML) and deep learning (DL), have been
developed to analyze complex data obtained from various sources using algorithms integrated into decision support systems (D.S.S.s).DL
algorithms in dentistry are useful in various diagnostic and treatment modalities. However, very few literature follow-up surveys and
multi-regional studies were conducted to explore the practice of A.I. by dental professionals Aim: The present study aimed to evaluate
the knowledge, attitude, and practices of dental students as well as dental practitioners toward artificial intelligence Methodology: A
15-question survey was prepared and distributed through Google Forms among dental students and professionals across Tamil Nadu,
India. It comprised various sections aiming to evaluate the knowledge, attitude, and practice toward A.I. and its potential applications in
dentistry. Results: 200 dental students and professionals (101 female, 99 male) responded to the questionnaire. Of these, about 70%
(interns), 78.97% (Post graduates), and 77.95% (Dentists with less than five years of experience) had basic knowledge about A.I.
technologies. Only 39.5% (p<.05)agreed A.I. has potential application both in the field of medicine and dentistry, but 53.5%
(p<.05)thinks A.I. cannot replace the role of the dentist either in patient management or diagnosis shortly. In addition, 53.5% are aware
of the potential applications; 44% recommended A.I. to be included in the undergraduate and postgraduate dental curriculum.
Conclusion: The present study results indicate that most dental students and practitioners with less than 5year of experience are aware of
A.I. but lack basic knowledge about incorporation and working models. Most participants emphasized that the basic working principles
of A.I., such as data science and logical statistics, should be taught in dentistry as a part of the curriculum or as value-added courses
during their clinical training. Thus demanding the need for better evidence-based teaching with the expanded application of A.I. tools in
dental practice.
Keywords: Attitude, Clinical Decision Support System, Deep Learning, Dental Education, Surveys, and questionnaires.

1. Introduction
Artificial Intelligence (A.I.) combines the advances of build models like Genetic algorithms (G.A.), and Artificial
computers or machines and informatics technologies to Neural networks (ANN) can read and inspect the data to
acquire intelligence to perform tasks that normally require implement various functions
human intellect [1]. In 1956, John McCarthy, popularly Several DL models such as deep neural networks
known as the "Father of Artificial Intelligence," coined (D.N.N.), recurrent neural networks (R.N.N.), and
A.I., constructed and developed computers or machines convolutional neural networks (CNN) were widely used to
capable of carrying out tasks by analyzing the data based perform various clinical tasks like image recognition,
on individual preferences and achieving specific goals [2]. image quality enhancement in the field of image-based
Over the years, A.I. and its subsets, machine learning (ML) automated diagnosis [4-6]. In the field of dentistry, DL
and deep learning algorithms are a useful tool in tracing Cephalometric
(DL), have been developed to analyze complex data landmarks, tooth color selection, prosthetic defects and
obtained from various sources using algorithms integrated removable partial denture designs, diagnosis of
into decision support systems (D.S.S.s). Machine learning, temporomandibular disorders, pulpal and periapical
a subset of A.I., can be used to learn the inherent patterns disease, periodontal lesions, identification of tooth-root
and structures in data for in-depth analysis and perform morphology, localization of tooth, and detection of
data functions using computer algorithms [1, 3]. ML radiolucent or cystic lesions [1, 6-8]
algorithms Several Studies have shown that students of health care
delivery systems are not anxious or concerned about being
substituted by A.I. and believe A.I. is a supportive tool to
_______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

1
M.Tech Student, Department of Computer Science and Engineering, G.
Pullaiah College of Engineering and Technology, (Autonomous), Kurnool execute patient trials and for screening purposes [8, 9].To
Email: mtulasi191297@gmail.com. the best of our knowledge, very few studies [10- 12] were
2
Assistant professor, Department of Computer Science and Engineering,
G. Pullaiah College of Engineering and Technology, Kurnool. Email:
conducted to establish dental students' views and attitudes
sowjanyareddy1230@gmail.com regarding the application of A.I. in India's dentistry field.
3
Professor, Department of CSE, GPCET, Kurnool. Thus, the present study aimed to evaluate the knowledge,
Enail: ksrinivasulucse@gpcet.ac.in attitude, and practices of dental students as well as dental
4
Professor, Department of CSE, GPCET, Kurnool. practitioners towards artificial intelligence.
Email: mrudrakumar@gmail.com
* Corresponding Author Email: mrudrakumar@gmail.com
International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2022, 10(1s), 248–253 | 1
 2. Methodology On the evaluation of knowledge- based questions about
A cross-sectional questionnaire survey was conducted artificial intelligence, it was observed t h a t 34.5%
amongst dental students and practitioners across Chennai (69)recognized John McCarthy established the concept of
city to assess their knowledge, attitude, and practice toward Artificial intelligence, among which only 41.5% (83) were
artificial intelligence (A.I.)After obtaining the Ethical familiar with both Artificial Neural networks (ANN) and
clearance, the required information was collected through convolutional neural networks (CNN) models. 53.5% (107)
published scientific articles about the study, and self- were also aware of the clinical decision support system
administered structured questionnaires comprising 15 (CDSS), commonly used as an adjuvant diagnostic tool in
questions in the English language were prepared and dentistry. The majority of the participants knew several
evaluated. The questionnaire combined selected responses A.I. applications, such as the Manufacturing sector (29%),
to certain questions and a few close-ended questions (Yes / Stock Market (34%), and Metrology (18.5). 50.5% (101)
No/don't know). agree that insufficient knowledge and lack of awareness of
200 randomly selected dental students and practitioners the incorporation of A.I. in dental practice as the major
across Chennai participated in this survey. Since this study drawbacks
was conducted during the COVID-19 Pandemic lockdown
period, online Google forms were generated and Attitude Based Responses
distributed through social media platforms. The internal On t h e assessment of the participant's attitude, 29.5%
consistency of the questionnaire was adequate (59) agree with t h e application of A.I. in t h e
(Cronbach's alpha = 0.791). The tool's reliability was diagnosis of oral cavity lesions, 39.5% feel (79) A.I. has
evaluated by test-re-test reliability analysis and Kappa potential application both in the field of medicine and
statistic (0.83). All the participants were briefed about dentistry, and 50% (100)found a beneficial role in
the purpose of the study, informed consent was obtained classifying suspicious altered mucosal lesions (Malignant
before the survey through Google forms, and they assured changes). 40.5%(91) and 44.5%(89) does not have a
that their participation was purely voluntary. positive attitude towards using software program for
planning surgeries and predicting genetic predisposition;
Statistical Evaluation nonetheless, 53.5% (107)thinks A.I. can replace the role
Non-probability, random sampling technique was of dentist neither in patient management nor in diagnosis
employed that yielded information from 200 individuals
taken into this observational study having a cross-sectional Practice-Based Responses
design. Responses recorded among the selected population On the estimation of practice-based questions, about 37.5%
group were evaluated using SPSS software Version 22.0. (75)suggest the use of A.I. integrated programs in
In the final analysis, "yes" or correct responses were given radiological diagnosis as an excellent tool, among which
a score of 1, and "no" or incorrect responses were given 44% (88) recommend A.I. Program training sessions on data
a score of 0; the scores were summed to obtain the overall science (24.5%), Logic statistics (47%) for the radiologist
scores in Internship, Postgraduates, and Dental to facilitate image-based automated diagnosis. 47%(94)
practitioners under five year of experience group practice A.I. to remove the necessity of several laboratory
Results steps and ease the treatment procedures.
On analysis of the given data, the mean age of t h e It was observed interns presented with 70% knowledge,
study population was observed as 26.08 ± 4.1387 years 77.07% attitude, and 70.48% practice scores. In contrast,
(mean ± S.D) with 0.577 at a 95% confidence level postgraduates responded with 78.97% knowledge, 77.75%
comprising 99 (49.5%) male and 101 (50.5%) female attitude, and 75.71% practice scores, and Dental
participants. It was observed that 25%(50) of study practitioners with less than five years of experience replied
participants are undergraduate students, 24.5%(49)are with 77.95% knowledge, 88.86% attitude, and 70.68%
postgraduate students, followed by 22% (44) dental practice scores, respectively. The overall K.A.P. score was
practitioners under ten years of experience, 20.5%(41) observed to be 72.51% for interns, 77.47% for
students pursuing a n internship, 5%(10) are dental postgraduates, and 79.16% for Dental practitioners with
practitioners with 5-10 years of experience and the least less than five years of experience, respectively, with a
being 3%(6) dental practitioners with more than 5years of significant score of p<0.0001, suggesting that scores
experience respectively. Chi-square test analysis was done improved with experience. [ Table 1 and Figure 1].
to correlate the interrelationship between the year-wise
distribution of the study participant. The result is
significant at p<.05

Knowledge-Based Responses

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2022, 10(1s), 248–253 | 2
 Table 1: Responses To Questions
Questions Option Total (n%) P value
S.no
1 The concept of artificial intelligence was given by John mc Carthy in 1956 69(34.5%) 0.00001
Do you agree that artificial intelligence has useful Yes 79(39.5%)
2 0.00001
applications i n t h e m e d i c a l a n d d e n t a l f i e l d s No 96(48%)
? Don't know 25(12.5%)
3 Do you think artificial intelligence can replace the role of Yes 59(29.5%) 0.00001
the dentist? No 107(53.5%)
Don't know 34(17%)
4 What are the studies of artificial intelligence? Convolutional neural 32(16%) 0.00001
network
Artificial neural 80(40%)
network
Both (a) and (b) 83(41.5%)
None of the above 5(2.5%)
5 Do you suggest using this program in radiological Yes 75(37.5%) 0.00001
diagnosis No 101(50.5%)
Don't know 24(12%)
6 Does radiologist requires training to access artificial Yes 88(44%) 0.00001
intelligence in dentistry No 97(48.5%)
Don't know 15(7.5%)

7 What kind of training must be given? Data Science 49(24.5%) 0.00001

Logic statistics 94(47%)
No training requires 48(24%)
Don't know 9(4.5%)
8 What are the conditions in which Artificial intelligence Lesion of the oral cavity 59(29.5%) 0.00001
can be used as a modality in diagnosis and treatment? Classifying suspicious 100(50%)
altered mucosa
Both(a)and (b) 34(17%)
None of the above 7(3.5%)
9 Do you think that artificial intelligence removes the Yes (47%) 0.00001
necessity for making a patient impression as well as
several laboratory steps No (43%)
Don't know (10%)
10 Does artificial intelligence has an advantage in future Yes 80(40%) 0.00001
No 94(47%)
Don't know 26(13%)
11 Do you have any idea of how artificial intelligence Yes 69(34.5%) 0.00001
can be incorporated into dental practice No 101(50.5%)
Don't know 30(15%)

12 Do you think this software program assists the oral Yes 78(39%) 0.00001
surgeon in planning surgeries No 91(40.5%)
Don't know 31(15.5%)
13 Do you think artificial intelligence predicts the genetic Yes 80(40%) 0.00001
predisposition of oral cancer No 89(44.5%)
Don't know 31(15.5%)

14 Which of the following network act as an adjuvant in Artificial neural 66(33%) 0.00001
diagnostic network (ANN)

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2022, 10(1s), 248–253 | 3
 suspicious altered mucosal lesions. Similar to studies by
Yüzbasıoğlu E [1], Yu and Kohane [3], Dos Santos DP
[6] et al., Singh J et al. [11], and Sur J et al. [12] who
observed significant (p<.05) a positive attitude towards
potential application of A.I. both in the field of medicine
and dentistry. Literature studies have also shown many
A.I. systems h a v e been developed with deep learning
algorithms to identify altered mucosal lesions, perform
automated diagnosis of oral lesions [19], bone age
assessment [20], detection and radiographic diagnosis of
tooth caries [5, 21] and periodontal diseases [22].
Half of the respondents practice A.I. to remove the
Figure 1- Group Distribution (Interns, Post necessity of several laboratory steps and ease the treatment
Graduates, Dental Practitioners) procedures, similar to observation by Singh J et al. [11]
and Sur J et al. [12]. Hwang et al. [23], in their
3. Discussion observation, also reported that the diagnostic precision of
deep learning algorithms at a quicker rate has transformed
Artificial intelligence is believed to have a greater impact
aided diagnostics into a more interactive practice. These
on the future generation of diagnostic and treatment
observations indicate that A.I. was preferred for its ability
modalities in health care sectors. Several studies were
to obtain quick, high-quality real- time data and ease
caried o ut to evaluate the knowledge and attitude towards
practices in health care services with minimal errors. Park
the development and future of A.I. among medical
et al. [24] and Mupparapu et al. [25] also illustrated the
professionals [6,13-15]. Applications of A.I. programs in
expanding application of A.I. quickly beyond text-based
dentistry are unique and remarkable, particularly in the
and image-based dental investigation and diagnosis
field of diagnostic medicine and radiology, which serves as
influence the treatment outcome and eventually help better
an advantage for budding young dental practitioners.
patient care.
However, very few scientific resources or survey has been
Most dental students agree that the major drawbacks were
conducted among dental students and dental professionals
insufficient knowledge and lack of awareness towards
on their attitudes towards A.I. in dental practice [10-12,
incorporating A.I. into dental practice. This was in contrast
16]
to Turkish study by Yüzbasıoğlu E [1],Korean study byOh
Most dental students and practitioners with less than 5years
S et al [16].Thes e o b s e r v a t i o n s could be due to
of experience were aware that AIand CDSS were
different educational curricula and teaching strategies
commonly used as adjuvant diagnostic tools in dentistry
across diverse countries. Additionally, the selected
but lacked basic knowledge about incorporation and
participants included dental students and professionals with
working models. Dental students believe that A.I. will
clinical experience who might have had different A.I.
modernize the future of dentistry, although the majority of
conceptualizations influencing the overall study outcome
respondents did not agree that A.I. could replace the role of
[26],[27],[28],[29].
t h e dentist neither in patient management nor in diagnosis
shortly, in contrast to previous studies by Yüzbasıoğlu [1],
4. Conclusion
Ranjana, et al. [10], Sur J et al. [12],
Oh, S et al. [16] conducted among dental students and The present study results indicate that most dental
professionals. This could be attributed to the fact that many students and practitioners with less than 5year of
study participants feel that physical examination, patient experience are aware of A.I. but lack basic knowledge
trust, empathy, and comfort play an important role apart about incorporation and working models. Most
from artificial sensors that gather accurate, relevant participants emphasized that the basic working principles
information to aid in diagnosis and treatment planning. of A.I., such as data science, Logic statistics, should be
About half of the respondents were familiar with both taught in dentistry as a part of the curriculum or as value-
Artificial Neural Network (ANN) and convolutional neural added courses during their clinical training. Thus
networks (CNN) models, and the majority of the demanding the need for better evidence- based teaching
participants had knowledge of several A.I. applications and with the expanded application of A.I. tools in dental
recommended A.I. Program training sessions which practice
suggests the active interest of respondents in new
technologies such as A.I. and their willingness to learn. References
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