Dental Artificial Intelligence Systems: A Review of Various Data Types

Area of Science:

• Dental informatics
• Artificial intelligence in healthcare
• Data science in dentistry

Background:

• The emergence of Data Dentistry bridges medicine and engineering through dental artificial intelligence systems (DAIS).
• Existing literature reviews explore advanced data collection and processing techniques for DAIS.
• The field necessitates a focus on expanding and improving datasets for future advancements.

Approach:

• Literature review of advanced data collection techniques for DAIS.
• Analysis of the current state of data processing within DAIS.
• Exploration of future trends and data requirements for DAIS.

Key Points:

• Imaging data (X-rays, CBCT, scans) are versatile inputs for AI.
• Standardized rules for electronic dental record (EDR) systems are essential for DAIS integration.
• Wearable device data offers significant potential for enhancing DAIS capabilities.
• Current DAIS excel in object location and diagnosis but need improvement in data integration and clinical decision-making.
• Enhancing both the quality and quantity of data is critical for DAIS development.

Conclusions:

• DAIS development requires diverse, high-quality datasets from various sources.
• Standardization of data formats and integration protocols is key for widespread DAIS adoption.
• Future research should focus on improving data quantity, quality, and AI algorithms for complex dental tasks.