The human hardware bias is hindering AI adoption in the health industry

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  1. As already said, ultrasound devices output images. This is to be taken literally: you cannot access the ultrasound data without opening up and modifying the device, thus voiding the certification (as of this writing, there seems to be only one commercially available device which can output raw data; it’s fully programmable, meant for research purposes and not to be found on typical hospitals). That’s obviously a deal-breaker in medical contexts.
  2. People want you to base your diagnosis on images. Yes, absurd as it is in the dawn of the AI era. Since we’re used to diagnosing with images, it has made it into the very call for tenders the fact that diagnosing software must rely on images as input data. Now, asking for an output image for explainability purposes is quite reasonable; on the contrary, restricting the means for producing those images to artificially reconstructed visual data is not. Who said it’s not possible for a tumor to produce an acoustic signature that’s discarded or dampened upon passing through the standard, ray-based image reconstruction algorithms? At the very least, that grants some unbiased exploration.

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We see beyond today’s needs focusing on R&D to bring innovative, human-driven and tech-driven solutions to the market. https://ennova-research.com

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Ennova Research

Ennova Research

We see beyond today’s needs focusing on R&D to bring innovative, human-driven and tech-driven solutions to the market. https://ennova-research.com

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