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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Dental Diagnostics

Background:

  • Artificial intelligence (AI) systems are increasingly used for radiographic caries detection.
  • Current evaluations often rely on limited performance metrics lacking clinical context.
  • This leads to challenges in interpreting and comparing AI system performance.

Purpose of the Study:

  • To review and discuss essential performance metrics for AI in radiographic caries detection.
  • To highlight the importance of contextual information for meaningful interpretation of AI performance.
  • To provide guidance on transparent reporting for improved comparability and transportability of AI systems.

Main Methods:

  • Narrative review focusing on confusion-matrix and discrimination metrics.
  • Discussion of complementary assessments: calibration, decision-analytic metrics, and robustness.
  • Analysis of how interpretation depends on disease definition, unit of analysis, thresholds, and prevalence.

Main Results:

  • Performance metrics like sensitivity, specificity, accuracy, ROC-AUC, and PR-AUC require explicit reporting of clinical context.
  • Calibration, decision curves, and robustness assessments offer complementary insights.
  • Lack of standardized reporting hinders clinical utility and transportability of AI tools.

Conclusions:

  • Clinically meaningful evaluation of AI for radiographic caries detection demands comprehensive reporting beyond standard metrics.
  • Transparent reporting of disease definitions, thresholds, prevalence, and complementary assessments is crucial.
  • Standardized reporting frameworks will enhance the reliability and clinical adoption of AI in dentistry.