Abstract
Chest radiography is the most common diagnostic X-ray examination, and paired images and reports are abundant in routine care, yet lesion-level spatial annotation remains scarce. This structured narrative review examines how radiology text has been used to supervise localization in chest radiographs: phrase grounding, weakly supervised abnormality localization, language-guided segmentation, grounded report generation, and multimodal large language models with spatial reasoning. Literature was drawn from a Web of Science seed search, citation chaining, and targeted searches through September 2026, and is organized along one axis, the granularity at which text binds to pixels, in five levels from study-level alignment to promptable or generative grounding. We show the axis is better read as two: train-time binding and inference-time spatial output come apart, and the systems that decouple them are the informative ones. Benchmarks such as MS-CXR and PadChest-GR made local evaluation realistic, and anatomy-aware resources such as PAXRay indicate that structural supervision improves grounding. Lesion masks now exist for the largest report corpus but are derived from those same reports, so model and reference share failure modes. We argue that anatomy-referenced evaluation, computable today, is the most tractable near-term correction, and that cross-study score comparison remains unsafe.