Generative AI in Medical Imaging: Applications, Challenges, and Ethics
- PMID: 37651022
- DOI: 10.1007/s10916-023-01987-4
Generative AI in Medical Imaging: Applications, Challenges, and Ethics
Abstract
Medical imaging is playing an important role in diagnosis and treatment of diseases. Generative artificial intelligence (AI) have shown great potential in enhancing medical imaging tasks such as data augmentation, image synthesis, image-to-image translation, and radiology report generation. This commentary aims to provide an overview of generative AI in medical imaging, discussing applications, challenges, and ethical considerations, while highlighting future research directions in this rapidly evolving field.
Keywords: Generative artificial intelligence; Large language models; Medical imaging; Radiology; Synthetic medical data.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
References
-
- Shad, R., Cunningham, J. P., Ashley, E. A., Langlotz, C. P. & Hiesinger, W. Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nature Machine Intelligence 3, 929–935 (2021). - DOI
-
- AlAmir, M. & AlGhamdi, M. The Role of generative adversarial network in medical image analysis: An in-depth survey. ACM Computing Surveys 55, 1–36 (2022). - DOI
-
- Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nature Reviews Physics, 1–4 (2023).
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