Citations 1
Generative AI and Foundation Models in Radiology: Applications, Opportunities, and Potential Challenges
Tavakoli, Neda , Abad, Zahra Shakeri Hossein , Gowda, Vrushab, Samsel, Konrad , Bedayat, Arash , Ghasemiesfe, Ahmadreza , Bagci, Ulas, Hsiao, Albert , Leiner, Tim , Carr, James , Kim, Daniel , Shakeri, Zahra , Rahsepar, Amir Ali , Kim, Daniel , Leiner, Tim , Gowda, Vrushab , Bağcı, Ulaş
Radiology | November 18, 2025 | Volume: 317 | Issue: 2 | Pages: No page available
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.