EDITORIAL

Artificial Intelligence in Dentistry: Hype or Hope?
Ulfat Bashir, Kanwal Zulfiqar

Artificial intelligence is one of the most significant 
contributions to the fourth industrial revolution, 
which ushers in a new digital era. It is defined as “the 
study of intelligent agents, which includes any 
machine that can comprehend its environment and 

1 
respond to increase its chances of success.”  The 
term "AI" is used informally when a machine imitates 
cognitive processes that people often connect with 
other human minds, such as "learning and problem-

1
solving."  Mathematician John McCarthy created the 
concept of "artificial intelligence" in 1955. McCarthy 
is widely considered the founder of the field. To 
explain how machines might be able to perform what 
can be referred to as "intelligent" activities, he 

2 
introduced this phrase. Numerous industrial sectors, 
including robotics, transportation, smart cities, 
financial analysis, etc., have incorporated AI. As an 
example, medical and dental imaging diagnostics, 
decision support, precision and digital medicine, 
drug discovery, wearable technologies, hospital 
monitoring, robotic and virtual assistants have all 
been employed in medicine and dentistry. In many 
instances, artificial intelligence (AI) can be seen as a 
helpful tool for physicians and dentists to lessen their 
labor. AI may learn from various information sources 
(multi-modal data) to diagnose diseases beyond the 
capability of humans, in addition to identifying 
diseases by means of a single information source that 

3
is focused on a particular illness.
The introduction of AI platforms such as CHAT GPT 
has completely revolutionized the dynamics of 
information being available within seconds. Similarly, 
health professionals are eagerly contemplating its 

3
effects in the Medical and Dental Health profession.  
The dentists all over the world are also rapidly 
embracing the advancements in AI and machine 

4
learning.  One of the biggest advantages of AI in 
dentistry is its ability to diagnose oral diseases with 
high accuracy and precision. AI algorithms can 
analyze dental images and detect early signs of oral 
cancer, periodontitis, and other oral diseases, 
allowing for early treatment and prevention of 

5
further complications.
AI has been heavily utilized in periodontology to 
investigate, comprehend, and build periodontal 
applications, such as detecting periodontal bone 
loss, identifying gingivitis inflammation, and 
evaluating connective tissues and other periodontal 

4 
cavities. Endodontic treatment planning has been 
greatly aided by AI in recent years. Different types of 
AI can aid dentists in the diagnosis and management 
of endodontic issues while fostering performance 
and assuring improved and precise patient care. The 
review's main objectives are to extract and evaluate 
AI-based methods for disease diagnosis and therapy 

4
planning.
When teeth exhibit periapical lesions and/or 
associated symptoms, it may be challenging for 
doctors to make a diagnosis and formulate a 

6
treatment plan.  The common disease known as 
apical periodontitis is responsible for about 75% of 

7
cases with radiolucent jaw lesions.  Early detection 
could improve the effectiveness of care, stop it from 
spreading to other tissues, and lessen potential 

8 
difficulties. Another benefit of AI in dentistry is its 
ability to improve the planning and execution of 

5 
dental procedures. AI algorithms can help dentists 
plan complex procedures such as implants, 
orthodontics, and restorations with high accuracy, 
reducing the risk of complications and ensuring the 

4
best possible outcome for the patient.
Due to its capacity to improve the efficiency and 
accuracy of the diagnostic process, artificial 
intelligence (AI) has become extremely popular in 
orthodontics in recent years. Since orthodontic 
treatments are frequently drawn-out processes, 
more effective planning calls for more effective and 
efficient solutions. Dentists can make judgments 
more precisely and quickly in a time-constrained 
context by using AI-based knowledge to automate 
disease diagnosis and treatment prognosis 
processes. Through their capacity to learn and make 

Correspondence:
Prof. Ulfat Bashir
Department of Orthodontics
Islamic International Dental College,
Riphah International University Islamabad
E-mail: ulfat.bashir@riphah.edu.pk

Received: February 27,2023; Accepted: March 01, 2023

1

Department of Orthodontics
Islamic International Dental College,
Riphah International University Islamabad

https://doi.org/10.57234/jiimc.march23.1666



automobile decisions, AI solutions can further aid in 
the prevention of human errors. Numerous studies 
have looked into using AI to diagnose and design 

9,10
treatments for orthodontic diseases.
In the area of dental education, AI is extensively used. 
The preclinical virtual patient input to the students 
has been much enhanced. The interactive interphase 
develops top-notch learning environments by letting 
pupils assess their own work and contrast it with the 
ideal. Numerous studies on the efficiency of these 
systems have revealed that, in comparison to 
conventional simulator units, these systems enable 
students to reach a competency-based skill level 

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more quickly. Artificial intelligence-powered virtual 
dental assistants can perform a variety of tasks in 
dental offices with greater accuracy and fewer errors. 
It is very helpful when discussing the patient's 
medical history and any habits they may have, such 
as smoking and drinking, with the dentist. The 
patient can choose to receive urgent teleassistance in 
dental crises, particularly if the practitioner is not 

12
readily available.
In recent years, there has been a noticeable increase 
in the number of research investigating the 

13,14 
application of AI in restorative dentistry. Various 
studies investigated the application of AI in helping 
caries detection, vertical tooth fracture prediction, 
and treatment planning. To accurately plan therapy 
utilizing clinical examples, Lee et al. suggested a 
machine learning method based on a decision tree to 
evaluate the tooth prognosis. The model's precision 

13
was 84.1%.
However, despite the rapid progress made in AI 
research in dentistry, there are still many challenges 
that need to be addressed. One of the biggest 
challenges is the lack of data standardization and 
interoperability between AI systems and existing 
dental systems. Also there is lack of understanding 
and adoption by dental professionals. While some 
dentists have embraced AI and its benefits, others 
are still skeptical about the technology and its ability 
to replace human expertise. Additionally, the high 
cost of AI technology can also limit its widespread 
adoption, particularly in resource-limited settings 
like Pakistan. This can lead to the inability to share 
data and collaborate on research thus limiting the 
advancement of the field. Additionally, there is a 
need for further research on the ethical and legal 
implications of AI in dentistry, such as data privacy 

and patient consent.
It is still necessary to use appropriate external data 
gathered from freshly enrolled patients or gathered 
from other dental facilities to confirm the 
generalizability and dependability of the offered AI 
models, even though their results have been 

15 
encouraging. Administration and exchange of 
clinical data are two major barriers to the use of AI 
systems in the healthcare sector. Patients' personal 
data is needed for both the initial training of AI 
algorithms as well as for ongoing training, validation, 
and improvement. The development of AI will also 
promote data sharing across multiple institutions 
and, in some circumstances, across international 
borders. AI must be integrated into healthcare 
operations while modifying systems that protect 

16
patient confidentiality and privacy.  Personal data 
must therefore be anonymized before considering a 

17 
wider distribution. Even if these protections are 
technically possible, the medical community has 
doubts about secure data sharing.
Despite these limitations, the future of AI in dentistry 
looks bright. As AI technology continues to advance 
and become more accessible, we can expect to see an 
increased adoption of the technology by dental 
professionals and patients alike. Furthermore, the 
development of AI-powered devices and tools will 
revolutionize the way dentists diagnose and treat oral 
diseases, leading to improved patient outcomes and 
a more efficient and effective delivery of dental care. 
computer learning Researchers will be better able to 
comprehend some multifactorial diseases with the 
aid of deep learning, and it will be feasible to increase 
our collective understanding of oral diseases and 
conditions that are not yet fully known. Artificial 
intelligence can undoubtedly be a tool for delivering 
improved healthcare to patients, but it cannot in any 
way take the place of human knowledge, skills, and 

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capacity of judgment.
Despite the difficulties, there is a good probability 
that AI will be used in dentistry in the future, and as 
we adopt these exciting innovations, patient care will 
only improve. However, for this to happen in the 
dental sector, new finance resources are required, 
along with debt and an understanding that open 
systems lead to innovations that are good for the 
sector. The possibilities are endless if these problems 
can be fixed. In conclusion, AI is the next paradigm 
shift in the healthcare. We as healthcare 

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professionals need to carefully evaluate the 
challenges we face when moving towards this new 
age of transformation.AI is not absolute and cannot 
replace human judgement. It is imperative that we 
adapt to AI to improve patient care but also be 
watchful of its limitations.

REFERENCES
1. Russel S, Norvig P. Artificial Intelligence: A Modern 

Approach. 3rd ed. New Jersey: Pearson Education; 2010.  
Back to cited text no. 2.

2. Rajaraman V. John McCarthy—Father of artificial 
intelligence. Resonance. 2014 Mar;19:198-207.

3. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, 
Corrado GS, et al. Prediction of cardiovascular risk factors 
from retinal fundus photographs via deep learning. Nat 
Biomed Eng. (2018) 2(3):158–64. Doi: 10.1038/s41551-
018-0195-0 PubMed Abstract | CrossRef Full Text | Google 
Scholar.

4. Fatima A, Shafi I, Afzal H, Díez ID, Lourdes DR, Breñosa J, 
Espinosa JC, Ashraf I. Advancements in Dentistry with 
Artificial Intelligence: Current Clinical Applications and 
Future Perspectives. InHealthcare 2022 Oct 31 (Vol. 10, No. 
11, p. 2188). MDPI.

5. Agrawal P, Nikhade P. Artificial Intelligence in Dentistry: 
P a s t ,  P r e s e n t ,  a n d  F u t u r e .  C u r e u s .  2 0 2 2  J u l  
28;14(7):e27405. doi: 10.7759/cureus.27405. PMID: 
36046326; PMCID: PMC9418762.

6. Artificial intelligence in endodontics: Current applications 
and future directions. Aminoshariae A, Kulild J, 
Nagendrababu V. J Endod. 2021;47:1352–1357. [PubMed] 
[Google Scholar].

7. Radiolucent inflammatory jaw lesions: a twenty-year 
analysis. Becconsall-Ryan K, Tong D, Love RM. Int Endod J. 
2010;43:859–865. [PubMed] [Google Scholar].

8. Periapical lucency around the tooth: radiologic evaluation 
and differential diagnosis. Chapman MN, Nadgir RN, 
Akman AS, Saito N, Sekiya K, Kaneda T, Sakai O. 
Radiographics. 2013;33:0–32. [PubMed] [Google Scholar].

9. Khalid MA, Zulfiqar K, Bashir U, Shaheen A, Iqbal R, Rizwan 
Z, Rizwan G, Fraz MM. 'Aariz: A Benchmark Dataset for 
Automatic Cephalometric Landmark Detection and CVM 
Stage Classification. arXiv preprint arXiv:2302.07797. 2023 

Feb 15.
10. Chen S., Wang L., Li G., Wu T.H., Diachina S., Tejera B., Kwon 

J.J., Lin F.C., Lee Y.T., Xu T., et al. Machine learning in 
orthodontics: Introducing a 3D auto-segmentation and 
auto-landmark finder of CBCT images to assess maxillary 
constriction in unilateral impacted canine patients. Angle 
Orthod. 2020;90:77–84. doi: 10.2319/012919-59.1. [PMC 
free article] [PubMed] [CrossRef] [Google Scholar].

11. Artificial intelligence: transforming dentistry today. Khanna 
S S ,  D h a i m a d e  PA .  h t t p s : / / w w w. i j b a m r. c o m /  
assets/images/issues/pdf/June%202017%20161167.pdf.p
df Indian J Basic Appl Med Res. 2017;6:161–167. [Google 
Scholar].

12. Artificial Intelligence in Dentistry: Current Concepts and a 
Peep into the Future. Alexander B, John S. Int J Adv Res. 
2018;30:1105–1108. [Google Scholar].

13. Lee S.J., Chung D., Asano A., Sasaki D., Maeno M., Ishida Y., 
Kobayashi T., Kuwajima Y., Da Silva J.D., Nagai S. Diagnosis of 
Tooth Prognosis Using Artificial Intelligence. Diagnostics. 
2022;12:1422. doi: 10.3390/diagnostics12061422. [PMC 
free article] [PubMed] [CrossRef] [Google Scholar].

14. Abdalla-Aslan R., Yeshua T., Kabla D., Leichter I., Nadler C. An 
artificial intelligence system using machine-learning for 
automatic detection and classification of dental 
restorations in panoramic radiography. Oral Surg. Oral Med. 
Oral Pathol. Oral Radiol. 2020;130:593–602. doi: 
10.1016/j.oooo.2020.05.012. [PubMed] [CrossRef] [Google 
Scholar].

15. The use and performance of artificial intelligence 
applications in dental and maxillofacial radiology: A 
systematic review. Hung K, Montalvao C, Tanaka R, Kawai T, 
B o r n s t e i n  M M .  D e n t o m a x i l l o f a c  R a d i o l .  
2020;49:20190107. [PMC free article] [PubMed] [Google 
Scholar].

16. Char DS, Shah NH, Magnus D. Implementing machine 
learning in healthcare — addressing ethical challenges. N 
Eng J Med. 2018;378(11):981-3.

17. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical 
implementation of artificial intelligence technologies in 
medicine. Nat Med. 2019 Jan;25(1):30-36. doi: 
10.1038/s41591-018-0307-0. Epub 2019 Jan 7. PMID: 
30617336; PMCID: PMC6995276.

18. Israni ST, Verghese A. 2019. Humanizing artificial 
intelligence. JAMA. 321(1):29–30.

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CONFLICT OF INTEREST
Authors declared no conflicts of Interest.

GRANT SUPPORT AND FINANCIAL DISCLOSURE
Authors have declared no specific grant for this 
research from any funding agency in public, 
commercial or nonprofit sector.

DATA SHARING STATMENT
The data that support the findings of this study 
are available from the corresponding author 
upon request.

This is an Open Access article distributed under the terms of the Creative Commons Attribution- Non-
Commercial 2.0 Generic License.

https://doi.org/10.57234/jiimc.march23.1666