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It is shown that predictions on a patient's unseen future data can change significantly if a model observed that same patient's anonymised historical data during training, a phenomenon known as memorisation bias, which exists across diverse data modalities and model architectures, and over prolonged time spans.
Identity preservation in counterfactual retinal images varies significantly across editing methods, with paired-training emerging as the most reliable approach.
