Neutral-to-slightly favorable toward ML-driven clinical decision support, emphasizing high performance metrics and potential utility in incomplete-data settings while not detailing limitations or external validation.
Study introduces a gating-weight-aware Mixture-of-Experts framework for unpaired MG/US/MRI breast cancer diagnosis, with DenseNet-121 experts, a gating mechanism, Lite-MoE pruning, and Grad-CAM interpretability, reporting high AUC/accuracy.
Bias may reflect training data emphasis on ML metrics; limited clinical nuance.
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