Skip to main content

Advertisement

SpringerLink
Log in

Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence

  • Review
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of medicine. Although highly effective, the rapid expansion of this technology has created some anticipated and unanticipated bioethical considerations. With these powerful applications, there is a necessity for framework regulations to ensure equitable and safe deployment of technology. Generative Adversarial Networks (GANs) are emerging ML techniques that have immense applications in medical imaging due to their ability to produce synthetic medical images and aid in medical AI training. Producing accurate synthetic images with GANs can address current limitations in AI development for medical imaging and overcome current dataset type and size constraints. Offsetting these constraints can dramatically improve the development and implementation of AI medical imaging and restructure the practice of medicine. As observed with its other AI predecessors, considerations must be taken into place to help regulate its development for clinical use. In this paper, we discuss the legal, ethical, and technical challenges for future safe integration of this technology in the healthcare sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4.
Fig. 5.
Fig. 6
Fig. 7.
Fig. 8.

Similar content being viewed by others

References

  1. Hamet, P., and J. Tremblay. Artificial intelligence in medicine. Metabolism. 69S:S36–S40, 2017. https://doi.org/10.1016/j.metabol.2017.01.011.

    Article  CAS  PubMed  Google Scholar 

  2. Jiang, B., N. Guo, Y. Ge, et al. Development and application of artificial intelligence in cardiac imaging. Br. J. Radiol. 93:20190812, 2020. https://doi.org/10.1259/bjr.20190812.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Shimizu, H., and K. Nakayama. Artificial intelligence in oncology. Cancer Sci. 111:1452–1460, 2020. https://doi.org/10.1111/cas.14377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mekov, E., M. Miravitlles, and R. Petkov. Artificial intelligence and machine learning in respiratory medicine. Expert Rev. Respir. Med. 14:559–564, 2020. https://doi.org/10.1080/17476348.2020.1743181.

    Article  CAS  PubMed  Google Scholar 

  5. Thomasian, N. M., C. Eickhoff, and E. Y. Adashi. Advancing health equity with artificial intelligence. J. Public Health Policy. 42:602–611, 2021. https://doi.org/10.1057/s41271-021-00319-5.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kalantari, L. S., H. Zhang, M. B. A. McDermott, et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27:2176–2182, 2021. https://doi.org/10.1038/s41591-021-01595-0.

    Article  CAS  Google Scholar 

  7. Geis, J. R., A. P. Brady, C. C. Wu, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. J. Am. Coll. Radiol. 16:1516–1521, 2019. https://doi.org/10.1016/j.jacr.2019.07.028.

    Article  PubMed  Google Scholar 

  8. Buruk, B., P. E. Ekmekci, and B. Arda. A critical perspective on guidelines for responsible and trustworthy artificial intelligence. Med. Health Care Philos. 23:387–399, 2020. https://doi.org/10.1007/s11019-020-09948-1.

    Article  PubMed  Google Scholar 

  9. Kazzazi, F. The automation of doctors and machines: a classification for AI in medicine (ADAM framework). Future Healthc. J. 8:e257–e262, 2021. https://doi.org/10.7861/fhj.2020-0189.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dikici, E., M. Bigelow, R. D. White, et al. Constrained generative adversarial network ensembles for sharable synthetic medical images. J. Med. Imaging. 8:024004, 2021. https://doi.org/10.1117/1.JMI.8.2.024004.

    Article  Google Scholar 

  11. Kaul, V., S. Enslin, and S. A. Gross. History of artificial intelligence in medicine. Gastroint. Endosc. 92:807–812, 2020. https://doi.org/10.1016/j.gie.2020.06.040.

    Article  Google Scholar 

  12. Reddy, S., S. Allan, S. Coghlan, and P. Cooper. A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 27:491–497, 2020. https://doi.org/10.1093/jamia/ocz192.

    Article  PubMed  Google Scholar 

  13. Cichosz, S. L., and A. A. P. Xylander. A conditional generative adversarial network for synthesis of continuous glucose monitoring signals. J. Diabetes Sci. Technol. 16:1220–1223, 2022. https://doi.org/10.1177/19322968211014255.

    Article  PubMed  Google Scholar 

  14. Jeong, J. J., A. Tariq, T. Adejumo, et al. Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. J. Digit. Imaging. 35:137–152, 2022. https://doi.org/10.1007/s10278-021-00556-w.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rezaei, M., J. J. Näppi, C. Lippert, et al. Generative multi-adversarial network for striking the right balance in abdominal image segmentation. Int. J. Comput. Assist. Radiol. Surg. 15:1847–1858, 2020. https://doi.org/10.1007/s11548-020-02254-4.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shi, X., T. Du, S. Chen, et al. UENet: a novel generative adversarial network for angiography image segmentation. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020. https://doi.org/10.1109/EMBC44109.2020.9175334.

    Article  PubMed  Google Scholar 

  17. Kamran, S. A., K. F. Hossain, A. Tavakkoli, S. L. Zuckerbrod. Attention2angiogan: synthesizing fluorescein angiography from retinal fundus images using generative adversarial networks. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9122–9129. IEEE (2021).

  18. Tavakkoli, A., S. A. Kamran, K. F. Hossain, and S. L. Zuckerbrod. A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs. Sci. Rep. 10:21580, 2020. https://doi.org/10.1038/s41598-020-78696-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kamran, S. A., K. F. Hossain, A. Tavakkoli, S. L. Zuckerbrod, K. M. Sanders, and S. A. Baker. RV-GAN: segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network. Springer. 2021. https://doi.org/10.1007/978-3-030-87237-3_4.

    Article  Google Scholar 

  20. Kamran, S. A., K. F. Hossain, H. Moghnieh, et al. New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning. Iscience. 25:104277, 2022. https://doi.org/10.1016/j.isci.2022.104277.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. You, A., J. K. Kim, I. H. Ryu, and T. K. Yoo. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis. 9:6, 2022. https://doi.org/10.1186/s40662-022-00277-3.

    Article  Google Scholar 

  22. Yang, Q., P. Yan, Y. Zhang, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging. 37:1348–1357, 2018. https://doi.org/10.1109/TMI.2018.2827462.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhu, J., G. Yang, and P. Lio. How can we make GAN perform better in single medical image super-resolution? A lesion focused multi-scale approach. IEEE. 2019. https://doi.org/10.1109/ISBI.2019.8759517.

    Article  PubMed Central  Google Scholar 

  24. de Farias, E. C., C. Di Noia, C. Han, E. Sala, M. Castelli, and L. Rundo. Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Sci. Rep. 11:1–12, 2021. https://doi.org/10.1038/s41598-021-00898-z.

    Article  CAS  Google Scholar 

  25. Bhattacharya, D., S. Banerjee, S. Bhattacharya, B. U. Shankar, S. Mitra. GAN-based novel approach for data augmentation with improved disease classification. Adv. Mach. Intell. Interact. Med. Image Anal. https://doi.org/10.1007/978-981-15-1100-4_11 (2020)

  26. Kamran, S. A., K. F. Hossain, A. Tavakkoli, S. L. Zuckerbrod, S. A. Baker. Vtgan: semi-supervised retinal image synthesis and disease prediction using vision transformers. Proc. IEEE/CVF Int. Conf. Comput. Vis. https://doi.org/10.48550/arXiv.2104.06757 (2021).

  27. Rashid, H., M. A. Tanveer, and H. A. Khan. Skin lesion classification using GAN based data augmentation. IEEE. 2019. https://doi.org/10.1109/EMBC.2019.8857905.

    Article  Google Scholar 

  28. Lei, Y., Y. Liu, X. Dong, et al. Automatic multi-organ segmentation in thorax CT images using U-Net-GAN. Med. Phys. 46:2157–2168, 2019. https://doi.org/10.1002/mp.13458.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Park, K.-B., S. H. Choi, and J. Y. Lee. M-gan: retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access. 8:146308–146322, 2020. https://doi.org/10.1109/ACCESS.2020.3015108.

    Article  Google Scholar 

  30. Yang, T., T. Wu, L. Li, and C. Zhu. SUD-GAN: deep convolution generative adversarial network combined with short connection and dense block for retinal vessel segmentation. J. Digit. Imaging. 33:946–957, 2020. https://doi.org/10.1007/s10278-020-00339-9.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Li, L., X. Zhao, W. Lu, and S. Tan. Deep learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing. 392:277–295, 2020. https://doi.org/10.1016/j.neucom.2018.10.099.

    Article  PubMed  Google Scholar 

  32. Nema, S., A. Dudhane, S. Murala, and S. Naidu. RescueNet: an unpaired GAN for brain tumor segmentation. Biomed. Signal Process. Control. 55:101641, 2020. https://doi.org/10.1016/j.bspc.2019.101641.

    Article  Google Scholar 

  33. Yousefirizi, F., and A. Rahmim. GAN-based bi-modal segmentation using mumford-shah loss: application to head and neck tumors in PET-CT images. Springer. 12603:99–108, 2020. https://doi.org/10.1007/978-3-030-67194-5_11.

    Article  Google Scholar 

  34. Han, C., Y. Kitamura, A. Kudo, et al. Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection. IEEE. 2019. https://doi.org/10.1109/3DV.2019.00085.

    Article  Google Scholar 

  35. Hammami, M., D. Friboulet, and R. Kéchichian. Cycle GAN-based data augmentation for multi-organ detection in CT images via Yolo. IEEE. 2020. https://doi.org/10.1109/ICIP40778.2020.9191127.

    Article  Google Scholar 

  36. Kanayama, T., Y. Kurose, K. Tanaka, et al. Gastric cancer detection from endoscopic images using synthesis by GAN. Springer. 11768:530–538, 2019. https://doi.org/10.1007/978-3-030-32254-0_59.

    Article  Google Scholar 

  37. Romo-Bucheli, D., P. Seeböck, J. I. Orlando, et al. Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomed. Opt. Express. 11:346–363, 2019. https://doi.org/10.1364/BOE.379978.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen, H.S.-L., G.-A. Chen, J.-Y. Syu, et al. Early glaucoma detection by using style transfer to predict retinal nerve fiber layer thickness distribution on the fundus photograph. Ophthalmol. Sci. 2:100180, 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Sikka, A., J. S. Virk, D. R. Bathula. MRI to PET cross-modality translation using globally and locally aware GAN (GLA-GAN) for multi-modal diagnosis of Alzheimer's Disease (2021). arXiv:2108.02160v1.

  40. Bazangani, F., F. J. Richard, B. Ghattas, and E. Guedj. FDG-PET to T1 weighted MRI translation with 3D elicit generative adversarial network (E-GAN). Sensors. 22:4640, 2022. https://doi.org/10.3390/s22124640.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yang, Q., N. Li, Z. Zhao, X. Fan, E. I. Chang, and Y. Xu. MRI cross-modality image-to-image translation. Sci. Rep. 10:3753, 2020. https://doi.org/10.1038/s41598-020-60520-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Liu, X., F. Xing, G. El Fakhri, and J. Woo. A unified conditional disentanglement framework for multimodal brain MR image translation. IEEE. 10–14:2021, 2021. https://doi.org/10.1109/isbi48211.2021.9433897.

    Article  Google Scholar 

  43. Goodfellow, I., J. Pouget-Abadie, M. Mirza, et al. Generative adversarial networks. Commun. ACM. 63:139–144, 2020. https://doi.org/10.1145/3422622.

    Article  Google Scholar 

  44. Isensee, F., P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge. Springer. 10670:287–297, 2017. https://doi.org/10.1007/978-3-319-75238-9_25.

    Article  Google Scholar 

  45. Ding, J., A. Li, Z. Hu, and L. Wang. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. Springer. 10435:559–567, 2017. https://doi.org/10.1007/978-3-319-66179-7_64.

    Article  Google Scholar 

  46. Ficarra, V., G. Novara, S. Secco, et al. Preoperative aspects and dimensions used for an anatomical (PADUA) classification of renal tumours in patients who are candidates for nephron-sparing surgery. Eur. Urol. 56:786–793, 2009. https://doi.org/10.1016/j.eururo.2009.07.040.

    Article  PubMed  Google Scholar 

  47. Pachade, S., P. Porwal, D. Thulkar, et al. Retinal fundus multi-disease image dataset (RFMiD): a dataset for multi-disease detection research. Data. 6:14, 2021. https://doi.org/10.3390/data6020014.

    Article  Google Scholar 

  48. Zhou, G., Y. Fan, J. Shi, Y. Lu, and J. Shen. Conditional generative adversarial networks for domain transfer: a survey. Appl. Sci. 12:8350, 2022. https://doi.org/10.3390/app12168350.

    Article  CAS  Google Scholar 

  49. Reed, S. et al. Generative adversarial text to image synthesis. In Proceedings of the International Conference on Machine Learning, New York City, NY, USA, pp. 1060–1069 (2016). https://doi.org/10.48550/arXiv.1605.05396.

  50. Sorin, V., Y. Barash, E. Konen, and E. Klang. Creating artificial images for radiology applications using generative adversarial networks (GANs)—a systematic review. Acad Radiol. 27:1175–1185, 2020. https://doi.org/10.1016/j.acra.2019.12.024.

    Article  PubMed  Google Scholar 

  51. You, A., J. K. Kim, I. H. Ryu, and T. K. Yoo. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis (Lond). 2022. https://doi.org/10.1186/s40662-022-00277-3.

    Article  PubMed  Google Scholar 

  52. Tavakkoli, A., S. A. Kamran, K. F. Hossain, and S. L. Zuckerbrod. A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs. Sci Rep. 10:21580, 2020. https://doi.org/10.1038/s41598-020-78696-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Siarohin, A., S. Lathuiliere, E. Sangineto, and N. Sebe. Appearance and pose-conditioned human image generation using deformable GANs. IEEE Trans. Pattern Anal. Mach. Intell. 43:1156–1171, 2021. https://doi.org/10.1109/TPAMI.2019.2947427.

    Article  PubMed  Google Scholar 

  54. Wang, Y., et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 174:550–562, 2018. https://doi.org/10.1016/j.neuroimage.2018.03.045.

    Article  PubMed  Google Scholar 

  55. Johnson, P. M., and M. Drangova. Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn. Reson. Med. 82:901–910, 2019. https://doi.org/10.1002/mrm.27772.

    Article  PubMed  Google Scholar 

  56. Liu, M., et al. Multi-conditional constraint generative adversarial network-based MR imaging from CT scan data. Sensors (Basel). 2022. https://doi.org/10.3390/s22114043.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Tan, J., et al. LGAN: lung segmentation in CT scans using generative adversarial network. Comput. Med. Imaging Graph. 87:101817, 2021. https://doi.org/10.1016/j.compmedimag.2020.101817.

    Article  PubMed  Google Scholar 

  58. Han, L., et al. Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network. Comput. Methods Programs Biomed. 189:105275, 2020. https://doi.org/10.1016/j.cmpb.2019.105275.

    Article  PubMed  Google Scholar 

  59. Ruan, Y., et al. MB-FSGAN: joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network. Med. Image Anal. 64:101721, 2020. https://doi.org/10.1016/j.media.2020.101721.

    Article  PubMed  Google Scholar 

  60. Ahmad, W., H. Ali, Z. Shah, and S. Azmat. A new generative adversarial network for medical images super resolution. Sci. Rep. 12:9533, 2022. https://doi.org/10.1038/s41598-022-13658-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Cao, F., A. Budhota, H. Chen, and K. S. Rajput. Feature matching based ECG generative network for arrhythmia event augmentation. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 296–299:2020, 2020. https://doi.org/10.1109/EMBC44109.2020.9175668.

    Article  Google Scholar 

  62. Singh, P., and G. Pradhan. A new ECG denoising framework using generative adversarial network. IEEE/ACM Trans. Comput. Biol. Bioinform. 18:759–764, 2021. https://doi.org/10.1109/TCBB.2020.2976981.

    Article  PubMed  Google Scholar 

  63. Chandaliya, P. K., and N. Nain. PlasticGAN: holistic generative adversarial network on face plastic and aesthetic surgery. Multimed. Tools Appl. 81:32139–32160, 2022. https://doi.org/10.1007/s11042-022-12865-5.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zhou, Y., B. Wang, X. He, S. Cui, and L. Shao. DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images. IEEE J. Biomed. Health Inform. 26:56–66, 2022. https://doi.org/10.1109/JBHI.2020.3045475.

    Article  PubMed  Google Scholar 

  65. Liu, Y., et al. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br. J. Ophthalmol. 104:1735–1740, 2020. https://doi.org/10.1136/bjophthalmol-2019-315338.

    Article  PubMed  Google Scholar 

  66. Yoo, T. K., J. Y. Choi, and H. K. Kim. CycleGAN-based deep learning technique for artifact reduction in fundus photography. Graefes Arch. Clin. Exp. Ophthalmol. 258:1631–1637, 2020. https://doi.org/10.1007/s00417-020-04709-5.

    Article  PubMed  Google Scholar 

  67. Cheong, H., et al. DeshadowGAN: a deep learning approach to remove shadows from optical coherence tomography images. Transl. Vis. Sci. Technol. 9:23, 2020. https://doi.org/10.1167/tvst.9.2.23.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ha, A., et al. Deep-learning-based enhanced optic-disc photography. PLoS ONE. 15:e0239913, 2020. https://doi.org/10.1371/journal.pone.0239913.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Yoo, T. K., et al. Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Comput. Methods Programs Biomed. 197:105761, 2020. https://doi.org/10.1016/j.cmpb.2020.105761.

    Article  PubMed  Google Scholar 

  70. Yoo, T. K., J. Y. Choi, H. K. Kim, I. H. Ryu, and J. K. Kim. Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images. Comput. Methods Programs Biomed. 205:106086, 2021. https://doi.org/10.1016/j.cmpb.2021.106086.

    Article  PubMed  Google Scholar 

  71. Zheng, C., et al. Assessment of generative adversarial networks for synthetic anterior segment optical coherence tomography images in closed-angle detection. Transl. Vis. Sci. Technol. 10:34, 2021. https://doi.org/10.1167/tvst.10.4.34.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Yildiz, E., et al. Generative adversarial network based automatic segmentation of corneal subbasal nerves on in vivo confocal microscopy images. Transl. Vis. Sci. Technol. 10:33, 2021. https://doi.org/10.1167/tvst.10.6.33.

    Article  PubMed  PubMed Central  Google Scholar 

  73. S. 2904—116th Congress (2019-2020); IOGAN Act. (2020, December 23). https://www.congress.gov/bill/116th-congress/senate-bill/2904

  74. H.R.3103—104th Congress (1995-1996): Health Insurance Portability and Accountability Act of 1996. (1996, August 21). https://www.congress.gov/bill/104th-congress/house-bill/3103

  75. Shachar, C., and S. Gerke. Prevention of bias and discrimination in clinical practice algorithms. JAMA. 2023. https://doi.org/10.1001/jama.2022.23867.

    Article  PubMed  Google Scholar 

  76. Goodman, K. E., D. J. Morgan, and D. E. Hoffmann. Clinical algorithms, antidiscrimination laws, and medical device regulation. JAMA. 2023. https://doi.org/10.1001/jama.2022.23870.

    Article  PubMed  Google Scholar 

  77. Pashkov, V. M., A. O. Harkusha, and Y. O. Harkusha. Artificial intelligence in medical practice: regulative issues and perspectives. Wiad Lek. 73(12 cz 2):2722–2727, 2020.

    Article  PubMed  Google Scholar 

  78. Arora, A., and A. Arora. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc. J. 9(2):190–193, 2022. https://doi.org/10.7861/fhj.2022-0013.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

NASA Grant [80NSSC20K183]: A Non-intrusive Ocular Monitoring Framework to Model Ocular Structure and Functional Changes due to Long-term Spaceflight.

Author information

Authors and Affiliations

  1. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Phani Srivatsav Paladugu

  2. Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA

    Phani Srivatsav Paladugu & Nicolas Nelson

  3. Michigan Medicine, University of Michigan, Ann Arbor, MI, USA

    Joshua Ong

  4. Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA

    Sharif Amit Kamran, Nasif Zaman & Alireza Tavakkoli

  5. University College Dublin School of Medicine, Belfield, Dublin, Ireland

    Ethan Waisberg

  6. University of Miami, Coral Gables, FL, USA

    Rahul Kumar

  7. Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA

    Roger Daglius Dias

  8. STRATUS Center for Medical Simulation, Brigham and Women’s Hospital, Boston, MA, USA

    Roger Daglius Dias

  9. Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA

    Andrew Go Lee

  10. Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew Go Lee

  11. The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew Go Lee

  12. Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA

    Andrew Go Lee

  13. Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA

    Andrew Go Lee

  14. University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Andrew Go Lee

  15. Texas A&M College of Medicine, Bryan, TX, USA

    Andrew Go Lee

  16. Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA

    Andrew Go Lee

Authors
  1. Phani Srivatsav Paladugu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicolas Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sharif Amit Kamran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ethan Waisberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nasif Zaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rahul Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Roger Daglius Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrew Go Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alireza Tavakkoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Tavakkoli.

Ethics declarations

Conflict of interest

The authors declare that they have no financial or non-financial conflicts of interests nor competing interests.

Additional information

Associate Editor Stefan M. Duma oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paladugu, P.S., Ong, J., Nelson, N. et al. Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence. Ann Biomed Eng 51, 2130–2142 (2023). https://doi.org/10.1007/s10439-023-03304-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-023-03304-z

Keywords

Search

Navigation