Automated diagnosis and treatment planning in dentistry

https://doi.org/10.53730/ijhs.v7nS1.14330

Authors

  • Salman Aziz Associate Professor Dental Materials, Institute of Dentistry, CMH Lahore Medical College, National University of Medical Sciences
  • Kashif Adnan BDS, MFDS RCPS (Glasgow), FICD, Demonstrator/ Registrar de'Montmorency College of Dentistry, Lahore
  • Hijab Fatemah BDS, MSc (UK) Assistant Professor in Department of Oral Biology at Sir Syed College of Medical Sciences
  • Umair Farrukh Vice Principal, Associate Professor & Head of Department of Community Dentistry, Watim Dental College, Rawalpindi
  • Ehsan Rathore Associate professor Oral Medicine Faryal Dental College Lahore
  • Muhammad Umer General Dentist Ahmed Dental Clinic Rawalpindi

Keywords:

Automated Diagnosis, Treatment, Planning, Dentistry

Abstract

This study aims to investigate the perceptions and experiences regarding automated diagnosis and treatment planning in dentistry. The field of automated diagnosis and treatment planning is rapidly evolving, leveraging advanced technologies such as artificial intelligence (AI) and machine learning to enhance patient care and outcomes. However, there is a need to understand the perspectives of dental professionals regarding the adoption and implementation of these automated systems. A questionnaire survey was conducted among 100 dentists from various dental practices to gather data on their familiarity, usage, and perceptions of automated diagnosis and treatment planning. The survey also explored the perceived benefits, challenges, and future implications of automated systems in dental care. Preliminary findings indicate that the majority of dentists in the sample (80%) have some level of familiarity with automated diagnosis and treatment planning. However, only 45% reported actively using such systems in their practice. Among the dentists using automated systems, the most commonly cited benefits include time-saving (60%), enhanced accuracy (55%), and improved treatment planning (50%). Challenges associated with the adoption of automated systems were also identified. 

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References

Alhazmi AM, et al. (2019). Artificial intelligence applications in dentistry: A review. Journal of Dentistry. 86: 1-9.

Estai M, et al. (2018). Applications of machine learning in caries detection: A systematic review. Journal of Dental Education. 82(5): 539-549.

Gani NH, et al. (2020). The current applications of artificial intelligence in dentistry: A scoping review. Artificial Intelligence in Medicine. 109: 101937.

Patel N, et al. (2021). Artificial intelligence in dentistry: Current applications and future perspectives.

Willems H, et al. (2018). Artificial intelligence in dentistry: Chances and challenges. Journal of Orofac

Published

07-06-2023

How to Cite

Aziz, S., Adnan, K., Fatemah, H., Farrukh, U., Rathore, E., & Umer, M. (2023). Automated diagnosis and treatment planning in dentistry. International Journal of Health Sciences, 7(S1), 1166–1179. https://doi.org/10.53730/ijhs.v7nS1.14330

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Section

Peer Review Articles

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