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The Gamuts website provides online reference information to link radiology to other Semantic Web resources, including an online quiz-question generator, and a visualization tool.

See also:

Joseph J. Budovec, MD, Cesar A. Lam, MD, Charles E. Kahn, "Informatics in Radiology: Radiology Gamuts Ontology: Differential Diagnosis for the Semantic Web", 'RadioGraphics', January 2014, doi: 10.1148/rg.341135036

Abstract: The Semantic Web is an effort to add semantics, or “meaning,” to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web.

Charles E. Kahn Jr., Joseph J. Budovec, Cesar A. Lam and Stephen Goth, "An Ontology of Differential Diagnosis in Diagnostic Radiology", presented at 2014 AMIA Translational Summit

Abstract: We created an ontology of 282 differential-diagnosis lists, or “gamuts,” in the domain of gastrointestinal radiology. The model describes 7,042 relationships for causality, subsumption, and synonymy among 3,363 disorders and imaging observations; the ontology’s concepts are annotated with and indexed by RadLex® concepts and SNOMED Clinical Terms®. The ontology is published as a Web Ontology Language (OWL) document. The knowledge representation allows automated reasoning over the ontology and integration with heterogeneous biomedical knowledge resources such as decision support systems, clinical image repositories, and the biomedical literature. This ontology has been applied to create several applications, including a RESTful web service, a web-based, illustrated gamuts reference, and a differential-diagnosis quiz generator. The present work serves as a model for a comprehensive ontology of differential diagnosis in diagnostic radiology.

Charles E. KAHN, Jr., "Ontology-Based Diagnostic Decision Support in Radiology", Medical Informatics Europe 2014

Abstract: The Radiology Gamuts Ontology (RGO) is a knowledge model of diseases, interventions, and imaging manifestations. RGO incorporates 16,822 terms with their synonyms and abbreviations and 55,393 relationships between

terms. Subsumption defines the relationship between more general and more specific terms; causality relates disorders and their imaging manifestations. We explored the application of the RGO to build an interactive decision support system for radiological diagnosis. The Gamuts DDx system was created to apply the RGO's knowledge: it identifies a list of potential diagnoses in response to one or more user-specified imaging observations. The system also identifies a set of observations that allow one to narrow the diagnosis, and dynamically narrows or expands the list of diagnoses as imaging findings are selected or deselected. The functionality has been implemented as a web-based user interface and as a web service. The current work demonstrates the feasibility of exploiting the RGO's causal knowledge to provide interactive decision support for diagnosis of imaging findings. Ongoing efforts include the further development of the system's knowledge base and evaluation of the system in clinical use.

Copies of the above can be obtained from Charles Kahn