这篇论文用大量数据和对照实验拆解了医学基础模型表征收敛的真正原因，发现关键不是临床标签而是自监督目标，对训练和评估模型很有启发。
该论文研究了医学影像基础模型表征收敛的原因。通过对比18个图像编码器和7个文本编码器（参数从7M到27B，涵盖五种成像模态，使用650,982张胸片），发现自监督目标的收敛度（40.4%）远高于标签监督（21.1%）和图像-文本（3.3%）。收敛度随模型规模增长不显著（Spearman 0.302，p=0.223）。线性分类器跨编码器转移后仍保留约85%的原始性能。结论是自监督目标而非临床监督驱动了表征收敛。
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similarity measures behind such claims are fragile. We present a controlled dissection across 18 image and 7 text encoders, all open-weight and run locally, spanning 7M to 27B parameters and five imaging modalities, including 650,982 chest radiographs from six datasets. To isolate cause, we train encoders that vary only the objective under fixed data, architecture, and scale, and reproduce the effect in a synthetic model. Convergence is modest but above a random floor, driven by the self-supervised objective, not clinical supervision: matched self-supervised encoders aligned most (40.4% on chest radiography), with label-supervised (21.1%) and image-text (3.3%) far lower, and did not grow with size (Spearman 0.302, p=0.223) or capability. It is within-modality, does not reach clinical language, and does not reproduce how radiologists judge case similarity. Yet a linear classifier transfers across encoders and to five held-out hospitals, retaining about 85% of within-encoder performance. Convergence in medical imaging is therefore set by the pretraining objective, not inherited from scale or clinical supervision. Interoperability is accordingly something to design for through that objective, and to validate where the shared geometry is weakest, across patient subgroups and against clinical judgment.