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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

A six-level clinical autonomy framework for artificial intelligence in dentistry.

Sohaib Shujaat¹

¹King Abdullah International Medical Research Center, Department of Maxillofacial Surgery & Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.

Frontiers in Oral Health

|July 15, 2026

Summary

A new six-level framework classifies artificial intelligence (AI) clinical autonomy in dentistry, from human-controlled (L0) to fully autonomous (L5). This taxonomy guides the safe integration of AI systems in oral healthcare.

Keywords:

artificial intelligence autonomous systems clinical autonomy decision support systems dental AI dentistry

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Last Updated: Jul 16, 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

Area of Science:

Dental Informatics
Artificial Intelligence in Healthcare
Clinical Autonomy Frameworks

Background:

AI in dentistry is advancing from decision support to autonomous task execution.
A structured framework is needed to classify AI clinical autonomy in dental practice.
Existing AI systems lack defined levels of autonomy across diagnostic, procedural, and workflow domains.

Purpose of the Study:

To introduce a dentistry-specific conceptual framework for classifying AI clinical autonomy.
To define six levels of autonomy (L0-L5) based on agentic capability, decision authority, human oversight, and risk.
To provide a taxonomy supporting the safe and responsible integration of AI in oral healthcare.

Main Methods:

Development of a six-level (L0-L5) conceptual framework for AI clinical autonomy in dentistry.
Characterization of AI systems based on agentic capability, delegated decision authority, human oversight, clinical operating domain, and risk.
Identification of a key inflection point at Level 3, marking a transition to delegated execution.

Main Results:

A six-level taxonomy (L0-L5) categorizes AI systems from human-controlled to fully autonomous within defined contexts.
Level 3 signifies a critical transition point where AI systems move from advisory to delegated execution.
The framework emphasizes functional-level classification, human-centered considerations, and context-aware deployment.

Conclusions:

The proposed framework offers a structured approach to defining and governing AI clinical autonomy in dentistry.
It facilitates discussion on responsibility, regulation, and safety requirements for AI in oral healthcare.
This conceptual taxonomy aims to support future research, regulatory dialogue, and refinement for responsible AI integration.