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2025-03-26 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > IT Information >
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Shulou(Shulou.com)11/24 Report--
CTOnews.com, July 11 (Xinhua)-- Google has partnered with several hospitals to test its Med-PaLM 2-based diagnosis and treatment robot, according to the Wall Street Journal.
It is reported that Google's diagnosis and treatment robot for medical use is based on the Med-PaLM 2 model, which is the "medical version" of the Google PaLM 2 model previously reported by CTOnews.com. Google uses "questions and answers for the physician license examination" as a training set, and has been tested many times by relevant experts in the hospital to enter the offline clinical testing phase.
▲ Picture Source Google said that the medical diagnosis and treatment robot model based on Med-PaLM 2 can better understand the medical situation than competing products such as Bing and ChatGPT, and the model has more advantages in answering medical queries, making important summaries of medical documents and collating medical research data.
Greg Corrado, director of research at Google, believes that although Med-PaLM 2 is still in the early stages of development, he believes that the model can be fully integrated into the medical field in the future, increasing the benefits of hospitals more than tenfold.
▲ source Med-PaLM but according to a paper released by Google in May, Google 2 still has the "hallucinations" and "inaccuracy" questions of general large language models, such as general answers or irrelevant information. However, the paper also says that Med-PaLM 2 is not far different from human doctors in reasoning and judging simple diseases.
At present, many technology giants are gradually integrating AI into the medical level. Microsoft also launched a LLaVA-Med model in June this year, which can infer the pathological condition of patients based on CT and X-ray pictures. This kind of medical special AI model is expected to promote the medical industry to improve the accuracy of symptomatic treatment of lesions and further reduce the cost of diagnosis and treatment.
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