Hologic Inc. is teaming up with Google Cloud to use machine learning technologies to improve the accuracy and timeliness of cytology for cervical cancer screening. Marlborough, Mass.-based Hologic, which makes both Pap and human papillomavirus (HPV) assays, is already using artificial intelligence (AI) and machine learning in its new digital cytology platform that is available in Europe. The multiyear collaboration with Mountain View, Calif.-based Google Cloud will start with innovating that product further by enhancing the deep learning component of the system.
Google sister company Verily Life Sciences LLC has been under an unprecedented amount of scrutiny since it was promoted over the weekend by President Donald Trump as responsible for a nationwide information and testing program for the emerging novel coronavirus.
Tech giant Google LLC is taking aim at the Apple Watch with its plan to acquire wearables pioneer Fitbit Inc., of San Francisco, for $2.1 billion, or $7.35 per share, in an all-cash transaction expected to close sometime next year.
Mayo Clinic has entered a 10-year partnership with Google "to expand on the more than 200 projects already incorporating artificial intelligence (AI) and machine learning," Mayo Chief Medical Information Officer Steve Peters told BioWorld MedTech. The Rochester, Minn.-based health care organization expects Google's expertise in data science and search technology will help the clinic improve treatment and outcomes by developing machine learning models.
Information technology (IT) has been promising for decades, largely since the advent of electronic medical records (EMR), to improve and streamline health care as it has multiplied productivity in countless other industries. In addition to the long-standing problems with EMRs, more recently there have been early disappointments with the latest iteration of IT focused on artificial intelligence (AI) and machine learning (ML), as big players like IBM Watson and Google have tended to over-promise and under-deliver with algorithms that are poorly matched to the data or the patient need.