Diagnostic imaging in veterinary medicine is commonly judged by its capacity to reveal abnormalities, yet abnormality detection is not equivalent to diagnostic utility. A conspicuous lesion may be incidental, artifactual, biologically nonspecific, or unrelated to the decision that prompted imaging, whereas clinically important disease may remain occult or only partially characterized. This article develops an original non-empirical framework in which imaging is evaluated by the extent to which it reduces decision-relevant uncertainty. The framework separates detection from diagnosis and actionability; requires an explicit clinical question and pre-imaging probability state; treats modality selection as a question of expected information gain rather than technological hierarchy; and distinguishes incidental abnormalities, modality discordance, and imaging–pathology discordance from definitive diagnostic conclusions. It further proposes that positive and negative findings can generate asymmetric errors of false escalation and false reassurance, and that sequential imaging should be triggered by a plausible opportunity to change interpretation or management rather than by elapsed time or persistent abnormality alone. Reporting and multidisciplinary interpretation are positioned as part of the diagnostic process because clinical context, reader agreement, operative findings, pathology, and temporal change may contribute nonredundant information. The framework is intended as a decision architecture rather than a validated score or universal rule. Its principal limitations are the heterogeneity of available veterinary evidence, dependence on disease- and modality-specific studies, and the need for prospective validation of reliability, decision impact, external transferability, and clinically meaningful reassessment strategies.
