[Artificial intelligence in ophthalmology : Guidelines for physicians for the critical evaluation of studies]


wide range of ophthalmological questions with an unprecedented precision. Which criteria must be considered for the evaluation of AI-related studies in ophthalmology? subjects). The data were analyzed with nested cross-validation (for learning algorithm selection and hyperparameter optimization). mean absolute error (MAE, 95% confidence interval, CI of 0.142 LogMAR [0.077; 0.207]). Healthy versus diseased eyes could be classified in the test data-set with an agreement of 0.92 (Cohen’s kappa). The exemplary incorrect learning algorithm and variable selection resulted in an MAE for visual acuity prediction of 0.229 LogMAR [0.150; 0.309] for the test data-set. The drastic overfitting became obvious on comparison of the MAE with the null model MAE (0.235 LogMAR [0.148; 0.322]). null or reference model can obscure the actual goodness-of-fit of AI models. The illustrated pitfalls can help clinicians to identify such shortcomings. zision. cksichtigt werden? Probanden). Die Daten wurden mit verschachtelter Kreuzvalidierung (zur Lernalgorithmusauswahl und Hyperparameteroptimierung) analysiert. beranpassung offensichtlich. nglichkeiten zu erkennen.