Adapting vision–language AI models to cardiology tasks
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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| Veröffentlicht in: | Nature medicine 2024-05, Vol.30 (5), p.1245-1246 |
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| container_issue | 5 |
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| container_title | Nature medicine |
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| creator | Arnaout, Rima |
| description | 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. |
| doi_str_mv | 10.1038/s41591-024-02956-1 |
| format | Article |
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| source | MEDLINE; Nature Journals Online; Alma/SFX Local Collection |
| subjects | 631/114/1305 692/700/1421/1860/1445 Artificial Intelligence - trends Biomedical and Life Sciences Biomedicine Cancer Research Cardiology Cardiology - trends Classification Ejection fraction Humans Infectious Diseases Language Machine learning Measurement techniques Medical imaging Medicine Metabolic Diseases Molecular Medicine Neurosciences News & Views news-and-views Pacemakers Preprints Ultrasonic imaging |
| title | Adapting vision–language AI models to cardiology tasks |
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