Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation
arXiv:2411.15490v2 Announce Type: replace-cross Abstract: Acute ischemic stroke (AIS) requires time-critical decision-making, where inaccurate interpretation of neuroimaging findings can lead to irreversible disability. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps from magnetic resonance imaging (MRI) are central to detecting acute infarction, yet generating factually reliable radiology reports directly from 3D MRI remains challenging due to the difficulty of learning robust cross-modal alignments between volumetric images and clinical text. We propose paired
The rapid advancements in AI, particularly in natural language processing and computer vision, are increasingly being applied to complex medical imaging tasks, pushing the boundaries of automated diagnostics.
Improving the factuality and reliability of AI-generated medical reports directly impacts patient care, reduces diagnostic errors, and enhances the efficiency of healthcare systems.
The proposed method moves toward more accurate and trustworthy AI interpretation of complex 3D medical images, reducing the burden on human radiologists and potentially improving diagnostic consistency.
- · Healthcare sector
- · Patients requiring medical imaging
- · Medical AI developers
- · Radiologists (as辅助)
- · AI models with poor factuality
- · Manual report generation
More accurate and faster diagnosis of conditions like acute ischemic stroke.
Increased trust in AI-powered diagnostic tools, accelerating their integration into clinical workflows.
Further development of multimodal AI systems that combine image and text analysis for comprehensive medical insights across various specialties.
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Read at arXiv cs.LG