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Midjourney goes from generating cat images to full-body ultrasound scans

Midjourney goes from generating cat images to full-body ultrasound scans

Midjourney has moved well beyond AI-generated imagery with the announcement of The Midjourney Scanner, a full-body ultrasound device that represents the company's first foray into physical hardware. CEO David Holz introduced the product, acknowledging the sharp departure from the "cat pictures" the company became known for. The scanner uses a ring of ultrasound sensors to capture vertical cross-sections of the body, building a picture of internal composition - muscle, fat, bone, and organ structure.

Holz described the device as targeting image quality comparable to MRI in certain respects, which would be a notable achievement for an ultrasound-based system. MRI remains the gold standard for soft tissue imaging, but it is expensive, slow, and requires large clinical infrastructure. A practical ultrasound alternative that approaches MRI fidelity could make routine body composition monitoring accessible in a way that clinical MRI never realistically could be.

The intended use cases lean toward longitudinal personal health tracking rather than acute medical diagnosis. Holz suggested users might scan themselves once a year or even daily to monitor how their body is changing over time - for example, tracking muscle development or the effects of diet. This positions the product closer to consumer wellness than traditional medical imaging, though the underlying technology clearly draws on clinical ultrasound methods. Midjourney also plans to house the scanner inside a San Francisco spa, suggesting an early distribution model built around in-person wellness visits rather than home ownership.

The announcement raises natural questions about regulatory pathways, clinical validation, and how the device handles the substantial data processing required to reconstruct detailed internal images from raw ultrasound signals. Midjourney has significant experience building and deploying generative AI models, and that expertise in training neural networks on image data may translate meaningfully to the reconstruction pipeline that would sit at the core of a product like this. How the company navigates the medical device landscape will be worth watching closely as more details emerge.

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