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Updated: Jun 5, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Artificial intelligence-driven workflow synchronizing interdisciplinary dentistry: Narrative review.

Arvin Kadempour¹, Ploy Wathanapong², Andrew Cantatore²

¹Ashman Department of Periodontology and Implant Dentistry, New York University College of Dentistry, New York, USA.

Journal of Prosthodontic Research

|June 3, 2026

Summary

Artificial intelligence (AI) in digital dentistry enhances diagnosis and treatment planning. This AI-integrated workflow improves data acquisition accuracy and clinician acceptance, streamlining interdisciplinary communication for better patient care.

Keywords:

Artificial intelligence Digital workflow Interdisciplinary dentistry Periodontics Prosthodontics

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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Area of Science:

Digital dentistry
Artificial intelligence in healthcare
Multidisciplinary treatment planning

Background:

Current dental workflows often face challenges in diagnostic coordination and interdisciplinary communication.
The integration of artificial intelligence (AI) offers a potential solution to enhance efficiency and accuracy in dental practice.

Purpose of the Study:

To review an AI-integrated digital workflow for seamless, multidisciplinary dental diagnosis and treatment planning.
To assess the impact of AI on diagnostic coordination, efficiency, and interdisciplinary communication in dentistry.

Main Methods:

A narrative review of studies from the PubMed database.
Selection of 40 studies, with 24 for general information and 16 for statistical analysis of AI software accuracy.
Analysis of AI performance in data acquisition, treatment planning, and clinical implementation.

Main Results:

AI-integrated data acquisition tools (IOS, CBCT, FS, JMT) achieved 88-97% accuracy.
AI models demonstrated 87-98% accuracy in treatment planning.
Clinician acceptance rate for AI-generated treatment plans ranged from 75-95%.

Conclusions:

An AI-integrated digital workflow using IOS, CBCT, FS, and JMT creates a virtual patient for improved communication and planning.
This workflow can enhance interdisciplinary coordination and treatment planning efficiency.
Further prospective clinical studies are needed to validate patient outcomes and satisfaction.