From Popular Science:
Google believes that mobile and digital-first experiences will be the future of health, and it has stats to back it up—namely the millions of questions asked in search queries, and the billions of views on health-related videos across its video streaming platform, YouTube.
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The tech giant has nonetheless had a bumpy journey in its pursuit to turn information into useful tools and services. Google Health, the official unit that the company formed in 2018 to tackle this issue, dissolved in 2021. Still, the mission lived on in bits across YouTube, Fitbit, Health AI, Cloud, and other teams.
Google is not the first tech company to dream big when it comes to solving difficult problems in healthcare. IBM, for example, is interested in using quantum computing to get at topics like optimizing drugs targeted to specific proteins, improving predictive models for cardiovascular risk after surgery, and cross-searching genome sequences and large drug-target databases to find compounds that could help with conditions like Alzheimer’s.
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In Google’s third annual health event on Tuesday, called “The Check Up,” company executives provided updates about a range of health projects that they have been working on internally, and with partners. From a more accurate AI clinician, to added vitals features on Fitbit and Android, here are some of the key announcements.
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Even more ambitiously, instead of using AI for a specific healthcare task, researchers at Google have also been experimenting with using a generative AI model, called Med-PaLM, to answer commonly asked medical questions. Med-PaLM is based on a large language model Google developed in-house called PaLM. In a preprint paper published earlier this year, the model scored 67.6 percent on a benchmark test containing questions from the US Medical License Exam.
At the event, Alan Karthikesalingam, a senior research scientist at Google, announced that with the second iteration of the model, Med-PaLM 2, the team has bumped its accuracy on medical licensing questions to 85.4 percent. Compared to the accuracy of human physicians, sometimes Med-PaLM is not as comprehensive, according to clinician reviews, but is generally accurate, he said. “We’re still learning.”
Link to the rest at Popular Science