Title
Role of artificial intelligence in teledentistry diagnosis and treatment planning
Authors
M. Aarti Rajambigai1,*, Faraz Ahmed2, S Mohammed Harris3, Jyotirmayee Rath4, Megha Varghese5 & Poorani A Elango6
Affiliation
1Department of Prosthodontics, Rajas Dental College and Hospital, Kavalkinaru, Tamil Nadu, India; 2Department of Conservative Dentistry & Endodontics, Oral and Maxillofacial Surgery, House surgeon, Burdwan Dental College and Hospital, Khosbagan, Bardhaman, West Bengal, India; 3Department of Periodontics, SRM Dental College, Bharathi Salai, Ramapuram, Chennai-98, Tamil Nadu, India; 4Department of Endodontics and Conservative Dentistry, Hitech Dental College, Pandara, Bhubaneswar-751025, Odisha, India; 5Department of Periodontology, FAME (Farookh Academy of Medical Education), Near Ilavala, Hunsur Taluk, Mysore, Karnataka, India; 6Department of Periodontology, SRM Dental College, Bharathi Salai, Ramapuram, Chennai, Tamil Nadu, India; *Corresponding author
M. Aarti Rajambigai - E-mail: aartimds@rediffmail.com; Phone: +91 99655 41511
Faraz Ahmed - E-mail: ahmedfaraz109@gmail.com; Phone: +91 9874178786
S Mohammed Harris - E-mail: harris.demmahom@gmail.com; Phone: +91 7358180547
Jyotirmayee Rath - E-mail: jyoti.mamun@gmail.com; Phone: +91 6371315943
Megha Varghese - E-mail: docjk007@yahoo.com; Phone: +91 8105259057
Poorani A Elango - E-mail: pooranielango1999@gmail.com; Phone: +91 6381400454
Article Type
Research Article
Date
Received April 1, 2026; Revised April 30, 2026; Accepted April 30, 2026, Published April 30, 2026
Abstract
Limited access to dental specialists delays accurate diagnosis and treatment planning in remote and underserved populations. Therefore, it is of interest to evaluate the reliability of an artificial intelligence system for remote dental diagnosis and treatment planning using intraoral images, panoramic radiographs and clinical histories from 320 patients. AI-generated outputs were compared with independent assessments by three board-certified dentists using Cohen’s kappa, sensitivity and specificity metrics. The system achieved 93.2% agreement for caries detection, 89.1% for periodontal assessment and 90.5% for malocclusion classification, with caries detection sensitivity of 94.8% and specificity of 92.1%. Thus, use of artificial intelligence showed substantial diagnostic concordance and may improve triage efficiency and access to care in teledentistry settings.
Keywords
Teledentistry, remote diagnosis, intelligent diagnostic tools, dental AI, digital health
Citation
Rajambigai et al. Bioinformation 22(4): 1974-1977 (2026)
Edited by
A Prashanth
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
