Scope and Implications of Artificial intelligence in dentistry. A Review. For (2022)

Authors

  • Rohma Yusuf Rama Dental College Hospital & Research Centre, Rama University, Mandhana, Kanpur, Uttar Pradesh- India 209217 Author
  • Nidhi Shukla Rama Dental College Hospital & Research Centre, Rama University, Mandhana, Kanpur, Uttar Pradesh- India 209217 Author
  • Charu Chitra Indra Gandhi Institute of Dental Sciences, Puducherry Author
  • Vaibhav Bansal Sri Aurobindo college of dentistry, Indore Author
  • Surbhit Singh Rama Dental College Hospital & Research Centre, Rama University, Mandhana, Kanpur, Uttar Pradesh- India 209217 Author

Keywords:

Dentistry, Artificial Intelligence, Neural Networks, Electronic Health Records, Clinical Decision Support, Hybrid Intelligence System, Healthcare, Robotics.

Abstract

Humans have recreated intelligence for effective human decision making and to unburden themselves of stupendous workload. The neural networks are a part of Artificial Intelligence and are similar to the human brain in their work. The field of Artificial Intelligence has shown a marked development and growth in the past few decades. Its application is expanding in the areas that were previously thought to be reserved for human experts. When applied to medicine and dentistry, Artificial Intelligence has shown tremendous potential to improve patient care and revolutionize the healthcare field. Artificial Intelligence has been investigated for variety of purposes, specifically identification of normal and abnormal structures, diagnosis of diseases and prediction of treatment outcomes. The advantages of this process is better efficiency, accuracy and time saving during diagnosis and treatment planning. Being an upcoming field, artificial intelligence has a long way in the field of medicine and dentistry. Hence, there is need for the dentists to be aware of its potential implications for a lucrative clinical practice in the future. Substantial data for this article was collected from different databases and original and systematic review articles previously published. This review will focus on application, advantages, disadvantages and limitations and future application of artificial intelligence in dentistry. 

Downloads

Download data is not yet available.

References

Khanna, S (2010). Artificial intelligence: contemporary applications and future compass.

Int Dent J., 60:269–72.

Kalappanavar, A., Sneha, S., Annigeri, R.G (2018). Artificial intelligence : A dentist’s

perspective . J Med Radiol Pathol Surg., 5:2–4.

Sherbet, G.V., Woo, W.L., Dlay, S (2018). Application of artificial intelligence-based

technology in cancer management: A commentary on the deployment of artificial neural

networks. Anticancer Res., 38:6607–13.

Nguyen, T.T., Larrivee, N., Lee, A., Bilaniuak, O., Durand, R (2021). Use of artifical

intelligence in dentistry. Current clinical trends and research advances. J can dent assoc.,

:17.

Ossowska, A., Kusiak, A., and Swietlik, D (2022). Artificial Intelligence in Dentistry—

Narrative Review. Int. J. Environ. Res. Public Health, 19, 3449.

Hamet, P., Tremblay, J (2017). Artificial intelligence in medicine. Metabolism, 69, S36–

S40.

Kakileti, S.T., Madhu, H.J., Krishnan, L., Manjunath, G., Sampangi, S., Ramprakash, H

(2020). Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting

Breast Cancer in Symptomatic and AsymptomaticWomen. JCO Glob. Oncol. 6, 1472–1480.

Sharma, S (2019). Artificial intelligence in dentistry: the current concepts and a peek into

the future. International Journal of Contemporary Medical Research, 6(12):L5-L9.

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., et al

(2017). Deep learning: A primer for radiologists. Radiographics, 37:2113–31.

Kareem, S.A., Pozos-Parra, P., Wilson, N (2017). An application of belief merging for

the diagnosis of oral cancer. Appl Soft Comput J., 61:1105–12.

Davenport, T., Kalakota, R (2019). digital technology. The potential for artificial

intelligence in healthcare. Future healthcare journal , 6(2):94-98.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., Arshad, H

(2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4:e00938.

Khalifa, M (2014). Clinical decision support: Strategies for success. Procedia Comput

Sci., 37:422–7.

Mendonça, E.A (2004). Clinical decision support systems: perspectives in dentistry. J

Dent Educ., 68:589–97.

Park, W.J., Park, J.B (2018). History and application of artificial neural networks in

dentistry. Eur J Dent.,12:594– 601.

Sharma, S (2019). Artificial intelligence in dentistry: the current concepts and a peek into

the future. International Journal of Contemporary Medical Research, 6(12):L5-L9.

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., et al

(2017). Deep learning: A primer for radiologists. Radiographics, 37:2113–31.

Kareem, S.A., Pozos-Parra, P., Wilson, N (2017). An application of belief merging for

the diagnosis of oral cancer. Appl Soft Comput J., 61:1105–12.

Davenport, T., Kalakota, R (2019). digital technology. The potential for artificial

intelligence in healthcare. Future healthcare journal , 6(2):94-98.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., Arshad, H

(2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4:e00938.

Khalifa, M (2014). Clinical decision support: Strategies for success. Procedia Comput

Sci., 37:422–7.

Mendonça, E.A (2004). Clinical decision support systems: perspectives in dentistry. J

Dent Educ., 68:589–97.

Park, W.J., Park, J.B (2018). History and application of artificial neural networks in

dentistry. Eur J Dent.,12:594– 601.

Sharma, S (2019). Artificial intelligence in dentistry: the current concepts and a peek into

the future. International Journal of Contemporary Medical Research, 6(12):L5-L9.

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., et al

(2017). Deep learning: A primer for radiologists. Radiographics, 37:2113–31.

Kareem, S.A., Pozos-Parra, P., Wilson, N (2017). An application of belief merging for

the diagnosis of oral cancer. Appl Soft Comput J., 61:1105–12.

Davenport, T., Kalakota, R (2019). digital technology. The potential for artificial

intelligence in healthcare. Future healthcare journal , 6(2):94-98.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., Arshad, H

(2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4:e00938.

Khalifa, M (2014). Clinical decision support: Strategies for success. Procedia Comput

Sci., 37:422–7.

Mendonça, E.A (2004). Clinical decision support systems: perspectives in dentistry. J

Dent Educ., 68:589–97.

Park, W.J., Park, J.B (2018). History and application of artificial neural networks in

dentistry. Eur J Dent.,12:594– 601.

Sharma, S (2019). Artificial intelligence in dentistry: the current concepts and a peek into

the future. International Journal of Contemporary Medical Research, 6(12):L5-L9.

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., et al

(2017). Deep learning: A primer for radiologists. Radiographics, 37:2113–31.

Kareem, S.A., Pozos-Parra, P., Wilson, N (2017). An application of belief merging for

the diagnosis of oral cancer. Appl Soft Comput J., 61:1105–12.

Davenport, T., Kalakota, R (2019). digital technology. The potential for artificial

intelligence in healthcare. Future healthcare journal , 6(2):94-98.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., Arshad, H

(2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4:e00938.

Khalifa, M (2014). Clinical decision support: Strategies for success. Procedia Comput

Sci., 37:422–7.

Mendonça, E.A (2004). Clinical decision support systems: perspectives in dentistry. J

Dent Educ., 68:589–97.

Park, W.J., Park, J.B (2018). History and application of artificial neural networks in

dentistry. Eur J Dent.,12:594– 601.8. Sharma, S (2019). Artificial intelligence in dentistry: the current concepts and a peek into

the future. International Journal of Contemporary Medical Research, 6(12):L5-L9.

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., et al

(2017). Deep learning: A primer for radiologists. Radiographics, 37:2113–31.

Kareem, S.A., Pozos-Parra, P., Wilson, N (2017). An application of belief merging for

the diagnosis of oral cancer. Appl Soft Comput J., 61:1105–12.

Davenport, T., Kalakota, R (2019). digital technology. The potential for artificial

intelligence in healthcare. Future healthcare journal , 6(2):94-98.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., Arshad, H

(2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4:e00938.

Khalifa, M (2014). Clinical decision support: Strategies for success. Procedia Comput

Sci., 37:422–7.

Mendonça, E.A (2004). Clinical decision support systems: perspectives in dentistry. J

Dent Educ., 68:589–97.

Park, W.J., Park, J.B (2018). History and application of artificial neural networks in

dentistry. Eur J Dent.,12:594– 601.

Ramesh, A.N., Kambhampati, C., Monson, J.R.T., Drew, P.J (2004). Artificial

intelligence in medicine. Ann R Coll Surg Engl., 86:334–8.

Beltrán-Aguilar, E.D., Barker, L.K., Canto, M.T., Dye, B.A., Gooch, B.F., Griffin, S.O.,

Hyman, J., Jaramillo, F., Kingman, A., Nowjack-Raymer, R., et al (2005). Surveillance for

dental caries, dental sealants, tooth retention, edentulism, and enamel fluorosis— United

States, 1988–1994 and 1999–2002. MMWR Surveill Summ. , 54, 1–43.

Saghiri, M.A., Asgar, K., Boukani, K.K., Lotfi, M., Aghili, H., Delvarani, A., Karamifar,

K., Saghiri, A.M., Mehrvarzfar, P., Garcia-Godoy, F (2012). A new approach for locating the

minor apical foramen using an artificial neural network. Int. Endod. J., 45, 257–265.

Setzer, F.C., Shi, K.J., Zhang, Z., Yan, H., Yoon, H., Mupparapu, M., Li, J (2020).

Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam

Computed Tomographic Images. J. Endod., 46, 987–993.

Poswar, F.D.O., Farias, L.C., de Carvalho Fraga, C.A., Bambirra, W., Brito-Júnior, M.,

Sousa-Neto, M.D., Santos, S.H.S., De Paula, A.M.B., D’Angelo, M.F.S.V., Guimarães,

A.L.S (2015). Bioinformatics, Interaction Network Analysis, and Neural Networks to

Characterize Gene Expression of Radicular Cyst and Periapical Granuloma. J. Endod., 41,

–883.

Li, P., Kong, D., Tang, T., Su, D., Yang, P., Wang, H., Zhao, Z., Liu, Y (2019).

Orthodontic Treatment Planning based on Artificial Neural Networks. Sci. Rep., 9, 2037.

Kök, H., Izgi, M.S., Acilar, A.M (2020). Determination of growth and development

periods in orthodontics with artificial neural network. Orthod. Craniofacial Res., 24, 76–83.

Auconi, P., Scazzocchio, M., Cozza, P., McNamara, J.J.A., Franchi L (2014). Prediction

of Class III treatment outcomes through orthodontic data mining. Eur. J. Orthod., 37, 257–267

.

Patcas, R., Timofte, R., Volokitin , A., Agustsson, E., Eliades, T., Eichenberger, M.,

Bornstein, M.M (2019). Facial attractiveness of cleft patients: A direct comparison between

artificial-intelligence-based scoring and conventional rater groups. Eur. J. Orthod., 41, 428–

Kim, B.S., Yeom, H.G., Lee, J.H., Shin, W.S., Yun, J.P., Jeong, S.H., Kang, J.H., Kim,

S.W., Kim, B.C (2021). Deep Learning-Based Prediction of Paresthesia after Third Molar

Extraction: A Preliminary Study Diagnostics , 11, 1572.

Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C.,

Schwendicke, F (2019). Deep Learning for the Radiographic Detection of Periodontal Bone

Loss. Sci. Rep., 9, 8495.

Cha, J.Y., Yoon, H.I., Yeo, I.S., Huh, K.H., Han, J.S (2021). Peri-Implant Bone Loss

Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical

Radiographs. J. Clin. Med., 10, 1009.

Vadzyuk, S., Boliuk, Y., Luchynskyi, M., Papinko, I., Vadzyuk, N (2021). Prediction

of the development of periodontal disease. Proc. Shevchenko Sci. Soc. Med. Sci., 65.

Khanna, S.S, Dhaimade, A.P (2017). Artificial Intelligence: Transforming Dentistry

Today. Indian J Basic Appl Med Res., 6:161–7.

Wang, C.W., Huang, C.T., Lee, J.H., Li, C.H., Chang, S.W., Siao, M.J., et al (2016).

A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal.,

:63–76.

Scrobota, I., Baciu, G., Filip, A.G., Todor, B., Blaga, F., Baciu, M.F (2017). Application

of fuzzy logic in oral cancer risk assessment. Iran J Public Health, 46:612–9.

Davenport, T.H. , Dreyer, K (2018). AI will change radiology, but it won't replace

radiologists. Harvard Business: Review.

Amisha, Malik, P., Pathania, M., Rathaur, V.K (2019). Overview of artificial intelligence

in medicine. J. Fam. Med. Prim. Care, 8, 2328–2331.

Downloads

Published

2022-02-28

Issue

Section

Articles

How to Cite

Yusuf, R., Shukla, N., Chitra, C., Bansal, V., & Singh, S. (2022). Scope and Implications of Artificial intelligence in dentistry. A Review. For (2022). History of Medicine, 8(1). https://historymedjournal.com/HOM/index.php/medicine/article/view/330