Advancing Multimodal Medical Image Generation: A Self-Improving Generative Foundation Model

Yongjian Chen; Lui Ng

doi:10.1002/mco2.70490

MedComm ›› 2025, Vol. 6 ›› Issue (12) :e70490 DOI: 10.1002/mco2.70490

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Advancing Multimodal Medical Image Generation: A Self-Improving Generative Foundation Model

Yongjian Chen ¹
, Lui Ng ²

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Yongjian Chen, Lui Ng. Advancing Multimodal Medical Image Generation: A Self-Improving Generative Foundation Model. MedComm, 2025, 6(12): e70490 DOI:10.1002/mco2.70490

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References

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[1]	J. Wang, K. Wang, Y. Yu, et al., “Self-improving Generative Foundation Model for Synthetic Medical Image Generation and Clinical Applications,” Nature Medicine 31, no. 2 (2025): 609–617.

[2]	I. Ktena, O. Wiles, I. Albuquerque, et al., “Generative Models Improve Fairness of Medical Classifiers Under Distribution Shifts,” Nature Medicine 30, no. 4 (2024): 1166–1173.

[3]	C. Bluethgen, P. Chambon, J. B. Delbrouck, et al., “A Vision–language Foundation Model for the Generation of Realistic Chest X-ray Images,” Nature Biomedical Engineering 9, no. 4 (2024): 494–506.

[4]	H. Chen, R. Lin, Y. Yu, et al., “MetaPath Chat: Multimodal Generative Artificial Intelligence Chatbot for Clinical Pathology,” Medical Communications 5, no. 10 (2024): e769.

[5]	N. Hollmann, S. Müller, L. Purucker, et al., “Accurate Predictions on Small Data With a Tabular Foundation Model,” Nature 637, no. 8045 (2025): 319–326.

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