Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
arXiv:2605.30984v1 Announce Type: cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output diversity, collapsing to generic templates that under-report rare yet critical findings. We identify this failure mode as Template Collapse. This failure stems from the unique constraints of 3D medical imaging, e.g., limited data, severe label imbalance, and weak signals from volumetric encoders. Under these constraints, text-generation objectives encourage shortcut learning and fluent but weakly g
This paper highlights a critical limitation in current medical AI models, specifically 'Template Collapse' in 3D CT report generation, as the field pushes for greater automation in healthcare diagnostics.
Sophisticated readers should care as this reveals a significant hurdle in deploying AI for complex medical tasks, pointing to a need for more robust model design and data strategies to ensure reliability and avoid critical diagnostic errors.
The understanding of AI's limitations in highly specialized domains is refined, emphasizing that fluent text generation does not equate to accurate, diverse, or safe pathological detection, especially with imbalanced medical data.
- · AI Safety Researchers
- · Medical Data Annotation Services
- · Specialized VLM Developers
- · Undifferentiated Medical AI Solutions
- · Early Adopters of Untrustworthy AI
- · Generative AI in Healthcare (short term)
Increased focus on robust data collection and bias mitigation techniques for medical AI.
Slower adoption of generative AI in critical healthcare diagnostics until reliability issues are demonstrably resolved.
Development of regulatory frameworks specifically targeting 'template collapse' and similar failure modes in medical AI.
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Read at arXiv cs.AI