Generative AI in Medical Imaging: Applications, Challenges, and Ethics

1. 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).

   Article  Google Scholar 

2. 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).

   Article  Google Scholar 

3. Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nature Reviews Physics, 1–4 (2023).

4. Kottlors, J. et al Feasibility of Differential Diagnosis Based on Imaging Patterns Using a Large Language Model. Radiology 308, e231167 (2023).

   Article  PubMed  Google Scholar 

5. Yu, B. et al Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE transactions on medical imaging 38, 1750–1762 (2019).

   Article  PubMed  Google Scholar 

6. Wang, J., Chen, Y., Wu, Y., Shi, J. & Gee, J. in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 3627–3636.

7. Gao, C. et al Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis. Nature Machine Intelligence 5, 294–308 (2023).

   Article  Google Scholar 

8. Jung, E., Luna, M. & Park, S. H. Conditional GAN with 3D discriminator for MRI generation of Alzheimer’s disease progression. Pattern Recognition 133, 109061 (2023).

   Article  Google Scholar 

9. Ghorbani, A., Natarajan, V., Coz, D. & Liu, Y. in Machine learning for health workshop. 155–170 (PMLR).

10. Yang, Z. et al A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nature communications 12, 7065 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

11. Zhou, Y., Wang, B., He, X., Cui, S. & Shao, L. DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images. IEEE Journal of Biomedical and Health Informatics 26, 56–66 (2020).

    Article  CAS  Google Scholar 

12. Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nature Medicine 28, 1773–1784 (2022).

    Article  CAS  PubMed  Google Scholar 

13. Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I. & Lungren, M. P. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital medicine 3, 136 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

14. Adams, L. C. et al Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 307, e230725 (2023).

    Article  PubMed  Google Scholar 

15. Li, H. et al Ethics of large language models in medicine and medical research. The Lancet Digital Health 5, e333-e335 (2023).

    Article  PubMed  Google Scholar 

16. Wang, J. et al FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis. Neurocomputing 546, 126282 (2023).

    Article  Google Scholar 

17. Schwarz, K., Liao, Y. & Geiger, A. On the frequency bias of generative models. Advances in Neural Information Processing Systems 34, 18126–18136 (2021).

    Google Scholar 

18. Shabani, S., Homayounfar, M., Vardhanabhuti, V., Mahani, M.-A. N. & Koohi-Moghadam, M. Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning. Computers in Biology and Medicine 149, 106033 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

19. Gurney-Champion, O. J., Landry, G., Redalen, K. R. & Thorwarth, D. in Seminars in Radiation Oncology. 377–388 (Elsevier).

20. Shneiderman, B. Responsible AI: Bridging from ethics to practice. Communications of the ACM 64, 32–35 (2021).

    Article  Google Scholar 

Download references