AI Caries Detection: Myths vs. Data

AI Caries Detection: Myths vs. Data

AI Caries Detection in research — a concise, cited review of where the evidence and everyday practice currently stand.

What the evidence shows

Recent work in research has sharpened the picture for clinicians. A few themes recur across the literature:

• Case selection outweighs technique. Outcomes track most closely with patient selection and disciplined follow-up.
• Protocols are converging. Independent groups are arriving at comparable workflows, which makes results easier to pool.
• Long-term data is still maturing. Short- and mid-term results look encouraging, but ten-year datasets remain thin.

Clinical takeaway

For most practices the practical move is incremental: adopt the parts of the workflow with the strongest evidence, document outcomes, and revisit as longer follow-up arrives.

Disclosure: assembled with AI assistance from multiple cited sources and reviewed by a human editor before publication.