Back to Authors

Ghasemiesfe, Ahmadreza

https://orcid.org/0009-0006-6635-8971

A5021135183 | I4210160527

Mayo Clinic

University of California Davis Medical Center

Citations 1

Generative AI and Foundation Models in Radiology: Applications, Opportunities, and Potential Challenges

Radiology | November 18, 2025 | Volume: 317 | Issue: 2 | Pages: No page available

10.1148/radiol.242961 | PMID 41251552

Foundation models can streamline imaging workflows, improve diagnostic accuracy, enable personalized treatment, and, via integrated generative artificial intelligence, create synthetic images to augment scarce real-world data; however, successful adoption requires explainability, trust, and workflow integration.