Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

Autor:	Tejani AS; From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.)., Cook TS; From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.)., Hussain M; From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.)., Sippel Schmidt T; From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.)., O'Donnell KP; From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.).
Jazyk:	angličtina
Zdroj:	Radiology [Radiology] 2024 Jun; Vol. 311 (3), pp. e232653.
DOI:	10.1148/radiol.232653
Abstrakt:	The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives. (© RSNA, 2024.)
Databáze:	MEDLINE
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