Atlas of MICCAI: 28 Years of Representation, Collaboration, and the Road to Inclusion

arXiv:2610.26871v1 Announce Type: cross 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

Concentrated influence in well-resourced regions and institutions may exacerbate existing inequalities.

The analysis highlights persistent inequalities in participation and access in medical AI research, which could reinforce global disparities. However, it also offers transparency, advocacy for inclusion, and data-driven recommendations to reduce barriers, suggesting potential for positive change. Overall, the societal impact is mixed with a slight tilt towards concern.

This is a meta-research study documenting persistent global inequality in medical AI research participation, not a claim about AI capability or deployment harm per se. The implication is mildly concerning for equitable AI-driven healthcare benefit distribution, but it is descriptive and comes with constructive recommendations.

The study reveals a persistent and severe inequality in participation and influence within the medical AI research community, with high-income countries dominating. This concentration of power and resources risks excluding lower-income countries from contributing to and benefiting from advancements in medical AI, potentially exacerbating global health disparities.

The concentration of AI research in medical imaging within a few countries and institutions risks exacerbating global inequalities. The slow decline in participation inequality suggests persistent barriers to inclusion.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   1f9dcff2-2064-4daf-b77a-feec87ff816f
           https://arxiv.org/abs/2610.26871
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -48  (Adverse)
   ↓
CONFIDENCE 80%