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2D foundation models can dominate 3D medical imaging tasks without the massive inference overhead of per-slice encoding simply by compressing volume depth via gradient-free multiscale PCA before feature extraction.
The shift from static software components to adaptive, goal-directed agents demands a new engineering framework to ensure reliability and trust in AI systems.
Despite the promise of transcriptomic models for predicting immunotherapy response, existing models fail to generalize across independent patient cohorts, raising serious questions about their clinical utility.