arXiv:2606.20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a large-scale bilingual (German/English) dataset of 1.2M CT and MR image-text pairs derived from clinical practice, with task-specific VQA and spatial grounding subsets generated automatically via LLM-based curation and automated segmentation. Trained on this data, our model RadGrounder jointly performs report generation, visual question answering, and spatial grounding via bounding-box detection or segm
The proliferation of large language models and advancements in automated data curation techniques are enabling the creation of specialized, high-quality datasets for complex domains like medical imaging.
This development allows for the training of advanced vision-language models capable of interpreting medical images with spatial grounding, which is crucial for diagnostic accuracy and efficiency in healthcare.
The ability to train robust radiology VLMs without extensive manual spatial annotations significantly lowers the barrier to entry for developing AI solutions in medical diagnostics, potentially accelerating their adoption.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Specialized VLM companies
- · Traditional radiology software vendors
- · Manual annotation services
More accurate and efficient AI-assisted radiologic diagnostics become commercially viable.
Reduced diagnostic errors and faster turnaround times lead to improved patient outcomes and lower healthcare costs.
The development of highly specialized, multimodal medical AI agents that can participate in clinical decision-making workflows gains momentum.
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Read at arXiv cs.CL