AI has shown strong potential for predicting which recently hospitalized patients will develop pressure injuries (PIs), also known as pressure ulcers or bedsores, if they aren’t treated early with preventive medicine.
After training deep neural networks on around 4,000 slide images from around 40 biopsied kidney patients, UCLA engineers have virtually re-stained tissue images for speedier high-accuracy diagnostics than a human histotechnologist could support.
Screening for sepsis in children and babies has grown quickly over the past several years. As methods and approaches multiply, machine learning continues looking like an eventual first-line diagnostic option.
When a black-box algorithm guides a physician’s diagnostic or therapeutic judgments, its intrinsic opaqueness can confound subsequent steps toward clinical safety and efficacy—and that’s just for starters.