AI-generated radiology reports are usually evaluated by comparison with a radiologist-authored reference, leaving no way to score them when no reference exists. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. On ReXVal, RadRef achieves 73.1% agreement on non-tied same-radiograph report pairs with radiologist rankings, comparable to leading reference-based metrics overall. It is also competitive on false findings and has its clearest relative result on omissions (missed findings). We further evaluate 100 model-generated and 100 radiologist-authored reports from 100 MIMIC-CXR studies, all independently annotated by two board-certified radiologists. On omissions, RadRef attains Kendall's tau-b = 0.295, against 0.386 between the two readers themselves, and no compared metric differs significantly from RadRef on this target. The same image-grounded representation provides a false-finding readout, RadRef-FF, with tau-b = 0.32 on ReXVal and 0.29 in the reader study, without requiring a reference. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.

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