arXiv:2605.24371v1 Announce Type: cross Abstract: CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices. Existing methods typically learn a direct image-to-text mapping, providing limited mechanisms for modeling how CT evidence evolves across slices or how reports respond to controlled changes in latent lesion-related factors. We propose SliceWorld, a CT-specific world-state framework that treats an axial CT scan as an ordered sequence along the z-axis. SliceWorld encodes prefix CT evidence into fac
This research is emerging as AI models advance in processing complex 3D medical imaging and as the need for more interpretable and controllable medical AI systems grows.
It represents a significant step towards more sophisticated and reliable AI-driven medical diagnostics, potentially improving the accuracy and efficiency of CT report generation and aiding clinical decision-making.
The ability to model the progression of CT evidence slice-by-slice and to control latent lesion factors introduces a new paradigm for medical AI, moving beyond simple image-to-text mapping.
- · Medical AI developers
- · Radiologists
- · Healthcare providers
- · Patients
- · Developers of less robust, direct image-to-text medical AI models
Improved diagnosis and treatment planning for conditions requiring CT scans.
Accelerated adoption of AI in clinical settings due to increased trustworthiness and explainability.
Potential for new AI-powered diagnostic tools that can predict disease progression or response to therapy based on subtle CT evidence.
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Read at arXiv cs.CL