Current AI Applications and Challenges in Oral Pathology

Area of Science:

• Oral pathology
• Medical artificial intelligence
• Digital health

Background:

• Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers significant potential in oral pathology.
• Techniques like convolutional neural networks (CNNs) and natural language processing (NLP) are advancing image analysis and clinical documentation.

Purpose of the Study:

• To review the transformative potential of AI in oral pathology.
• To highlight studies demonstrating AI's current improvements in disease detection and diagnostic streamlining.
• To critically examine limitations hindering AI adoption in the field.

Main Methods:

• Literature review of key studies on AI applications in oral pathology.
• Analysis of AI's role in image analysis and clinical documentation.
• Examination of challenges including data quality, generalizability, legal/ethical issues, and financial constraints.

Main Results:

• AI demonstrates improved accuracy in detecting oral diseases.
• AI techniques streamline diagnostic processes in oral pathology.
• Significant limitations exist, including data quality, generalizability, ethical considerations, and financial barriers.

Conclusions:

• Addressing challenges through collaboration, validation, and strategic integration is crucial.
• AI has the potential to revolutionize oral pathology practice.
• Successful AI implementation can lead to improved patient outcomes and field advancement.