Table 1.

Key Considerations for Data Used to Train an AI Model

Category	Key Questions
Dataset size	•How large is the training dataset? Consider both the number of images and the number of unique patients or eyes. •Was augmentation used to artificially increase dataset size? If so, what type?
Dataset scope	•Does the dataset include a range of disease severities (e.g., mild, moderate, severe)? •Was demographic information (age, gender, and race) reported? •Does the dataset reflect diverse and representative demographics? •Could potential biases in the dataset affect model performance?
Labeling methods	•Were labels assigned by a single expert or through consensus labeling? •For unsupervised learning, how was the model evaluated for clinical relevance?
Pretraining	•Was the model pretrained on a general dataset (e.g., ImageNet)?

AI = artificial intelligence.