Akbar, M. U., Wang, W., & Eklund, A. (2023). Beware of diffusion models for synthesizing medical [preprint]mages—A comparison with Gans [preprint]n [preprint]erms of memorizing brain MRI and chest X-Ray [preprint]mages [Preprint]. SSRN. https://doi.org/10.2139/ssrn.4611613

Aydin, O. U., Hilbert, A., Koch, A., Lohrke, F., Rieger, J., Tanioka, S., & Frey, D. (2024). Generative modeling of the circle of Willis using 3D-StyleGAN. Neuroimage, 120936. https://doi.org/10.1016/j.neuroimage.2024.120936

Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., & Wallace, E. (2023). Extracting training data from diffusion models (arXiv:2301.13188). arXiv. http://arxiv.org/abs/2301.13188

Chen, S., Ma, K., & Zheng, Y. (2019). Med3D: Transfer learning for 3D medical image analysis. ArXiv. ArXiv:1904.00625. http://arxiv.org/abs/1904.00625

Chen, J., Zhu, L., Mou, W., Lin, A., Zeng, D., Qi, C., Liu, Z., Jiang, A., Tang, B., Shi, W., Kahlert, U. D., Zhou, J., Guo, S., Lu, X., Sun, X., Ngo, T., Pu, Z., Jia, B., Jeon, C. O., & Luo, P. (2024). STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability. iMetaOmics, 1(1), e7. https://doi.org/10.1002/imo2.7

Article  Google Scholar 

Dar, S. U. H., Seyfarth, M., Ayx, I., Papavassiliu, T., Schoenberg, S. O., Siepmann, R. M., Laqua, F. C., Kahmann, J., Frey, N., Baeßler, B., Foersch, S., Truhn, D., Kather, J. N., & Engelhardt, S. (2025). Unconditional latent diffusion models memorize patient imaging data: Implications for openly sharing synthetic data. ArXiv. https://doi.org/10.48550/ArXiv.2402.01054. ArXiv:2402.01054.

Dice, L. R. (1945). Measures of the amount of Ecologic association between species. Ecology, 26(3), 297–302. https://doi.org/10.2307/1932409

Article  Google Scholar 

Dippel, J., Prenißl, N., Hense, J., Liznerski, P., Winterhoff, T., Schallenberg, S., Kloft, M., Buchstab, O., Horst, D., Alber, M., Ruff, L., Müller, K. R., & Klauschen, F. (2024). AI-Based anomaly detection for Clinical-Grade histopathological diagnostics. NEJM AI, 1(11), AIoa2400468. https://doi.org/10.1056/AIoa2400468

Article  Google Scholar 

Dockhorn, T., Cao, T., Vahdat, A., & Kreis, K. (2023). Differentially private diffusion models (arXiv:2210.09929). arXiv.. https://doi.org/10.48550/ArXiv.2210.09929

Dombrowski, M., Zhang, W., Cechnicka, S., Reynaud, H., & Kainz, B. (2024). Image generation diversity issues and how to tame them (arXiv:2411.16171). arXiv. https://doi.org/10.48550/arXiv.2411.16171

Dutt, R., Sanchez, P., Bohdal, O., Tsaftaris, S. A., & Hospedales, T. (2024). MemControl: Mitigating memorization in medical diffusion models via automated parameter selection (arXiv:2405.19458; Version 1). arXiv. https://doi.org/10.48550/arXiv.2405.19458

Fernandez, V., Sanchez, P., Pinaya, W. H. L., Jacenków, G., Tsaftaris, S. A., & Cardoso, J. (2023). Privacy distillation: Reducing re-identification risk of multimodal diffusion models. https://doi.org/10.48550/arXiv.2306.01322

Fernandez, V., Pinaya, W. H. L., Borges, P., Graham, M. S., Tudosiu, P. D., Vercauteren, T., & Cardoso, M. J. (2024). Generating multi-pathological and multi-modal images and labels for brain MRI. Medical Image Analysis, 97, 103278. https://doi.org/10.1016/j.media.2024.103278

Article  PubMed  PubMed Central  Google Scholar 

Ferreira, A., Li, J., Pomykala, K. L., Kleesiek, J., Alves, V., & Egger, J. (2024). GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy. Medical Image Analysis, 93, 103100. https://doi.org/10.1016/j.media.2024.103100

Article  PubMed  Google Scholar 

Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018). GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 321, 321–331. https://doi.org/10.1016/j.neucom.2018.09.013

Article  Google Scholar 

GitHub—Google-deepmind/surface-distance: Library to compute surface distance based performance metrics for segmentation tasks. (n.d.). Retrieved January 15 (2025). from https://github.com/google-deepmind/surface-distance/tree/master

Giuffrè, M., & Shung, D. L. (2023). Harnessing the power of synthetic data in healthcare: Innovation, application, and privacy. NPJ Digital Medicine, 6, 186. https://doi.org/10.1038/s41746-023-00927-3

Article  PubMed  PubMed Central  Google Scholar 

Gupta, D., Loane, R., Gayen, S., & Demner-Fushman, D. (2023). Medical image retrieval via nearest neighbor search on pre-trained image features. Knowledge-Based Systems, 278, 110907. https://doi.org/10.1016/j.knosys.2023.110907

Article  PubMed  PubMed Central  Google Scholar 

Hofmanninger, J., Prayer, F., Pan, J., Röhrich, S., Prosch, H., & Langs, G. (2020). Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental, 4(1), 50. https://doi.org/10.1186/s41747-020-00173-2

Article  PubMed  PubMed Central  Google Scholar 

Hoopes, A., Mora, J. S., Dalca, A. V., Fischl, B., & Hoffmann, M. (2022). SynthStrip: Skull-stripping for any brain image. Neuroimage, 260, 119474. https://doi.org/10.1016/j.neuroimage.2022.119474

Article  PubMed  Google Scholar 

Ibrahim, M., Khalil, Y. A., Amirrajab, S., Sun, C., Breeuwer, M., Pluim, J., Elen, B., Ertaylan, G., & Dumontier, M. (2024). Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges (arXiv:2407.00116). arXiv. https://doi.org/10.48550/arXiv.2407.00116

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 2. https://doi.org/10.1038/s41592-020-01008-z

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and improving the image quality of StyleGAN (arXiv:1912.04958). arXiv. https://doi.org/10.48550/arXiv.1912.04958

Karras, T., Aittala, M., Aila, T., & Laine, S. (2022). Elucidating the design space of diffusion-based generative models (arXiv:2206.00364). arXiv. https://doi.org/10.48550/arXiv.2206.00364

Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D. (2023). Diffusion models in medical imaging: A comprehensive survey. Medical Image Analysis, 88, 102846. https://doi.org/10.1016/j.media.2023.102846

Article  PubMed  Google Scholar 

Khader, F., Müller-Franzes, G., Tayebi Arasteh, S., Han, T., Haarburger, C., Schulze-Hagen, M., Schad, P., Engelhardt, S., Baeßler, B., Foersch, S., Stegmaier, J., Kuhl, C., Nebelung, S., Kather, J. N., & Truhn, D. (2023). Denoising diffusion probabilistic models for 3D medical image generation. Scientific Reports, 13(1), 7303. https://doi.org/10.1038/s41598-023-34341-2

Article  PubMed  PubMed Central  Google Scholar 

Khosravi, B., Li, F., Dapamede, T., Rouzrokh, P., Gamble, C. U., Trivedi, H. M., Wyles, C. C., Sellergren, A. B., Purkayastha, S., Erickson, B. J., & Gichoya, J. W. (2024). Synthetically enhanced: Unveiling synthetic data’s potential in medical imaging research. eBioMedicine, 104, 105174. https://doi.org/10.1016/j.ebiom.2024.105174

Article  PubMed  PubMed Central  Google Scholar 

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S. A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A., & Gowal, S. (2024). Generative models improve fairness of medical classifiers under distribution shifts. Nature Medicine, 30(4), 1166–1173. https://doi.org/10.1038/s41591-024-02838-6

Article  PubMed  PubMed Central  Google Scholar 

Kuppa, A., Aouad, L., & Le-Khac, N. A. (2021). Towards improving privacy of synthetic DataSets. In N. Gruschka, L. F. C. Antunes, K. Rannenberg, & P. Drogkaris (Eds.), Privacy technologies and policy (pp. 106–119). Springer International Publishing. https://doi.org/10.1007/978-3-030-76663-4_6

Legido-Quigley, C., Wewer Albrechtsen, N. J., Bæk Blond, M., Corrales Compagnucci, M., Ernst, M., Herrgård, M. J., Minssen, T., Ottosson, F., Pociot, F., Rossing, P., & Sulek, K. (2025). Data sharing restrictions are hampering precision health in the European union. Nature Medicine, 1–2. https://doi.org/10.1038/s41591-024-03437-1

Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., … Starmans, M. P. A. (2025). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. https://doi.org/10.1136/bmj-2024-081554

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 55–55.

Google Scholar 

Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024). Segment anything in medical images. Nature Communications, 15(1), 654. https://doi.org/10.1038/s41467-024-44824-z

Article  PubMed  PubMed Central  Google Scholar 

Packhäuser, K., Gündel, S., Münster, N., Syben, C., Christlein, V., & Maier, A. (2022). Deep Learning-based patient Re-identification is able to exploit the biometric nature of medical chest X-ray data. Scientific Reports, 12(1), 14851. https://doi.org/10.1038/s41598-022-19045-3

Article  PubMed  PubMed Central  Google Scholar 

Packhäuser, K., Folle, L., Thamm, F., & Maier, A. (2023). Generation of anonymous chest radiographs using latent diffusion models for training thoracic abnormality classification systems. 2023 IEEE 20th international symposium on biomedical imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI53787.2023.10230346

Pan, S., Wang, T., Qiu, R. L. J., Axente, M., Chang, C. W., Peng, J., Patel, A. B., Shelton, J., Patel, S. A., Roper, J., & Yang, X. (2023). 2D medical image synthesis using transformer-based denoising diffusion probabilistic model. Physics in Medicine & Biology, 68(10), 105004. https://doi.org/10.1088/1361-6560/acca5c

Article  Google Scholar 

Park, H. Y., Bae, H. J., Hong, G. S., Kim, M., Yun, J., Park, S., Chung, W. J., & Kim, N. (2021). Realistic High-Resolution body computed tomography image synthesis by using progressive growing generative adversarial network: Visual turing test. JMIR Medical Informatics, 9(3), e23328. https://doi.org/10.2196/23328

Article  PubMed  PubMed Central  Google Scholar 

Paul, W., Cao, Y., Zhang, M., & Burlina, P. (2021). Defending medical image diagnostics against privacy attacks using generative methods: Application to retinal diagnostics. In C. Oyarzun Laura, M. J. Cardoso, M. Rosen-Zvi, G. Kaissis, M. G. Linguraru, R. Shekhar, S. Wesarg, M. Erdt, K. Drechsler, Y. Chen, S. Albarqouni, S. Bakas, B. Landman, N. Rieke, H. Roth, X. Li, D. Xu, M. Gabrani, E. Konukoglu, & J. Passerat-Palmbach (Eds.), Clinical Image-Based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and Privacy-Preserving machine learning (pp. 174–187). Springer International Publishing. https://doi.org/10.1007/978-3-030-90874-4_17

Peng, J., Chen, G., Saruta, K., & Terata, Y. (2023). 2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model. Medical Imaging Process & Technology, 6(1), 1. https://doi.org/10.24294/mipt.v6i1.2518

Pinaya, W. H. L., Tudosiu, P. D., Dafflon, J., da Costa, P. F., Fernandez, V., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022). Brain imaging generation with latent diffusion models (arXiv:2209.07162). arXiv. http://arxiv.org/abs/2209.07162

Saha, A., Bosma, J. S., Twilt, J. J., van Ginneken, B., Bjartell, A., Padhani, A. R., Bonekamp, D., Villeirs, G., Salomon, G., Giannarini, G., Kalpathy-Cramer, J., Barentsz, J., Maier-Hein, K. H., Rusu, M., Rouvière, O., van den Bergh, R., Panebianco, V., Kasivisvanathan, V., Obuchowski, N. A., & Huisman, H. (2024). Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study. The Lancet Oncology, 25(7), 879–887. https://doi.org/10.1016/S1470-2045(24)00220-1

Article  PubMed  PubMed Central  Google Scholar 

Salinas, M. P., Sepúlveda, J., Hidalgo, L., Peirano, D., Morel, M., Uribe, P., Rotemberg, V., Briones, J., Mery, D., & Navarrete-Dechent, C. (2024). A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. Npj Digital Medicine, 7(1), 1–23. https://doi.org/10.1038/s41746-024-01103-x

Article  Google Scholar 

Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024). The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review. Npj Digital Medicine, 7(1), 1–30. https://doi.org/10.1038/s41746-024-01196-4

Article  Google Scholar 

Somepalli, G., Singla, V., Goldblum, M., Geiping, J., & Goldstein, T. (2023). Understanding and mitigating copying in diffusion models (arXiv:2305.20086). arXiv. https://doi.org/10.48550/arXiv.2305.20086

Tanioka, S., Aydin, O. U., Hilbert, A., Ishida, F., Tsuda, K., Araki, T., Nakatsuka, Y., Yago, T., Kishimoto, T., Ikezawa, M., Suzuki, H., & Frey, D. (2024). Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network. Scientific Reports, 14(1), 16465. https://doi.org/10.1038/s41598-024-67365-3

Article  PubMed  PubMed Central