Vision–language models can be trained to read cardiac ultrasound images with implications for improving clinical workflows, but additional development and validation will be required before such models can replace humans.
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References
Christensen, M., Vukadinovic, M., Yuan, N. & Ouyang, D. Nat. Med. https://doi.org/10.1038/s41591-024-02959-y(2024).
Radford, A. et al. Preprint at https://doi.org/10.48550/arXiv.2103.00020 (2021).
Ferreira, D. & Arnaout, R. Preprint at https://doi.org/10.48550/arXiv.2311.04847 (2023).
Thawkar, O. et al. Preprint at https://doi.org/10.48550/arXiv.2306.07971 (2023).
Li, Y., Jia, S., Song, G., Wang, P. & Jia, F. Quant. Imaging Med. Surg. 13, 6989–7001 (2023).
Chang, B. S. JAMA 330, 1521–1522 (2023).
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Arnaout, R. Adapting vision–language AI models to cardiology tasks. Nat Med (2024). https://doi.org/10.1038/s41591-024-02956-1
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DOI: https://doi.org/10.1038/s41591-024-02956-1
