A new interim analysis from Ovation Fertility and Alife Health is exploring the potential of artificial intelligence (AI) to improve embryo selection during in vitro fertilization (IVF) procedures.
Researchers from the University of Washington Medicine have developed the first wearable camera system that uses artificial intelligence (AI) to detect potential medication delivery errors. Their findings were published in npj Digital Medicine on October 22, 2024.
Anitoa Systems, LLC., a Silicon Valley biotechnology company and market leader in rapid, portable molecular testing, today announced the launch of a computer vision guided collaborative robot (Cobot) system for automating qPCR sample preparation.
HeartFlow, Inc., the global leader in non-invasive artificial intelligence (AI) heart care solutions, today announced that the American Medical Association (AMA) has issued a new Category I Current Procedural Terminology (CPT®) code for AI-enabled plaque quantification technology, including HeartFlow Plaque Analysis effective January 2026.
In a recent study published in the journal New England Journal of Medicine, researchers from the University of California San Diego School of Medicine used advanced artificial intelligence to make hospital quality reporting simpler, quicker, and more effective while maintaining high accuracy, which could improve the delivery of healthcare.
King Faisal Specialist Hospital & Research Centre (KFSHRC) continues to contribute to advancing healthcare through pioneering advancements in AI-driven diagnostics and minimally invasive surgeries, advancing precision and outcomes while contributing to global innovation.
According to a study presented at the 128th annual meeting of the American Academy of Ophthalmology (AAO) 2024, daily self-imaging can improve care while reducing financial strain on patients and the healthcare system.
In a study that was published in Scientific Reports, scientists from Addenbrooke’s Hospital, Anglia Ruskin University, Check4Cancer, and the University of Essex used combination theory and machine learning to narrow down 22 clinical features to the seven most crucial ones that could indicate whether or not a skin lesion is suspicious.
Researchers at the University of Zurich used artificial intelligence to assist in the identification of bacteria that are resistant to antibiotics.
Mark Yatskar, Chris Callison-Burch, and Yue Yang have created neural networks for medical image recognition by emulating the training pathways of human physicians.
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