Wearable Cameras Allow AI to Detect Medication Errors
In a test whose results were published today, the video system recognized and identified, with high proficiency, which medications were being drawn in busy clinical settings.
In a test whose results were published today, the video system recognized and identified, with high proficiency, which medications were being drawn in busy clinical settings.
The study results, published in the October 21, 2024 online edition of the New England Journal of Medicine (NEJM) AI,
The design, validation and comparative assessment of this computational suite, NeoDisc, are detailed in the current issue of Nature Biotechnology in a publication led by Florian Huber and Michal Bassani-Sternberg of the Lausanne Branch of the Ludwig Institute for Cancer Research.
The researchers said that, while the model could be prompted in ways that make its responses more accurate, it's still no match for the clinical judgment of a human doctor.
CellChorus Inc., a spinoff from the University of Houston, is commercializing the UH-developed Time-lapse Imaging Microscopy In Nanowell Grids™ platform for dynamic single-cell analysis with label-free analysis. Now they've received a $2.5 million grant from the National Center for Advancing Translational Sciences of the National Institutes of Health to fast-track the development of an advanced "label-free" version of this technology in partnership with the University of Houston.