APA (7th ed.) Citation

G, B., C, J. P., K, D., R, H., T, P., M, F., . . . P, V. (2024). Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis. Radiology. Artificial intelligence, 6(6), e230514. https://doi.org/10.1148/ryai.230514

Chicago Style (17th ed.) Citation

G, Brugnara, et al. "Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis." Radiology. Artificial Intelligence 6, no. 6 (2024): e230514. https://doi.org/10.1148/ryai.230514.

MLA (9th ed.) Citation

G, Brugnara, et al. "Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis." Radiology. Artificial Intelligence, vol. 6, no. 6, 2024, p. e230514, https://doi.org/10.1148/ryai.230514.