  As massive medical data become available with an increasing number of scans,
expanding classes, and varying sources, prevalent training paradigms -- where
AI is trained with multiple passes over fixed, finite datasets -- face
significant challenges. First, training AI all at once on such massive data is
impractical as new scans/sources/classes continuously arrive. Second, training
AI continuously on new scans/sources/classes can lead to catastrophic
forgetting, where AI forgets old data as it learns new data, and vice versa. To
address these two challenges, we propose an online learning method that enables
training AI from massive medical data. Instead of repeatedly training AI on
randomly selected data samples, our method identifies the most significant
samples for the current AI model based on their data uniqueness and prediction
uncertainty, then trains the AI on these selective data samples. Compared with
prevalent training paradigms, our method not only improves data efficiency by
enabling training on continual data streams, but also mitigates catastrophic
forgetting by selectively training AI on significant data samples that might
otherwise be forgotten, outperforming by 15% in Dice score for multi-organ and
tumor segmentation.
  The code is available at https://github.com/MrGiovanni/OnlineLearning
