arXiv:2512.01085v3 Announce Type: replace-cross Abstract: Medical phrase grounding (MPG) maps textual descriptions of radiological findings to corresponding image regions. These grounded reports are easier to interpret, especially for non-experts. Existing MPG systems mostly follow the referring expression comprehension (REC) paradigm and return exactly one bounding box per phrase. Real reports often violate this assumption. They contain multi-region findings, non-diagnostic text, and non-groundable phrases, such as negations or descriptions of normal anatomy. Motivated by this, we reformulate
The continuous advancements in AI and natural language processing are enabling more sophisticated applications in specialized domains like medical imaging.
Improved medical phrase grounding enhances the interpretability of radiological reports, potentially leading to more accurate diagnoses and better patient outcomes.
This research moves beyond simplistic one-to-one mapping in medical imaging AI, embracing the complexity of real-world clinical documentation which includes multi-region findings and non-diagnostic text.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Medical imaging companies
- · Traditional manual medical image analysis processes
More accurate and efficient interpretation of medical images due to better AI-driven tools.
Reduced diagnostic errors and improved healthcare efficiency, benefiting both clinicians and patients.
Accelerated development of fully autonomous diagnostic AI systems that can handle the nuanced complexities of medical language and imagery, potentially reshaping radiology practices.
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Read at arXiv cs.CL