arXiv:2610.26871v1 Announce Type: new Abstract: Scientific knowledge in medical image analysis is shaped by venues such as the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference, which influence research agendas, professional credibility, and access to opportunity. As AI methods increasingly depend on large-scale data and compute, these dynamics risk further concentrating influence within already well-resourced regions and institutions. Prior meta-research on MICCAI has examined reproducibility, evaluation, and citation practices, but offers limited insight into who participates in and shapes the field. To better understand these dynamics, we conduct a longitudinal bibliometric analysis of the geographic and institutional structure of the MICCAI community, drawing on 9,748 papers published between 1998 and 2025. We find that the USA and China together account for nearly half of all publications, that high-income countries dominate collaboration networks as central and bridging nodes, while lower-income countries remain peripheral, and that participation inequality (Gini index 0.86-0.91 among contributing countries) has declined only marginally over 28 years. We further analyze the evolution of research topics between 2021 and 2025, distinguishing stable core pillars (e.g., segmentation, diagnosis, MRI/CT) from emerging paradigms (e.g., foundation models), and identify a persistent long tail of underexplored application-method and organ-modality intersections. To support transparency and reproducibility, we introduce the Atlas of MICCAI dataset and an interactive dashboard. We contextualize these findings within existing MICCAI equity initiatives, and propose actionable recommendations to reduce participation barriers, build research capacity, and recognize a wider range of scientific contributions.