Can Artificial Intelligence Match Human Expertise in Long‐Term Periodontal Prognosis? A Comparative Accuracy Study

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Abstract
Aim: To compare the prognostic performance of an artificial intelligence (AI) model with that of experienced clinicians in predicting tooth loss over a 10‐year period. Materials and Methods: An AI model trained on structured clinical and radiographic data was compared with 12 periodontists and 11 general dentists (GDs), who independently assigned prognostic scores (0–10 scale) to 300 teeth with known 10‐year outcomes. AI and clinician performance were evaluated under a fixed threshold and group‐specific optimal thresholds. Accuracy, sensitivity, specificity, predictive values, area under the receiver operating characteristic curve and calibration were used for comparison. Results: Using the fixed threshold (score > 5 = survival), clinicians achieved higher overall accuracy than AI (75.6% periodontists, 74.9% GDs, 69.2% AI; p < 0.05), with sensitivity low and comparable across groups (14.7%–22.7%). Using group‐specific optimal thresholds, AI achieved the highest accuracy for all‐cause tooth loss (67.5%) and performed comparably to GDs for periodontitis‐related tooth loss (76.2% vs. 76.3%). Calibration was substantially worse for AI (Brier score 0.247) than for periodontists and GDs (0.171, 0.170). Individually, AI performance overlapped with that of several clinicians. Conclusion: Clinicians outperformed AI under a fixed threshold, but AI performance was comparable to clinicians using optimal thresholds, situating AI within the range of experienced clinicians despite modest overall predictive ability.

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