Chennai Stock:Artificial intelligence models provide support, Google intends to provide disease screening services such as tuberculosis in India
IT House on March 19th, Google today officially announced a series of plans to deploy the AI model in the healthcare industry.According to the plan, the Google Research team and the Fitbit company they have are developing a series of new artificial intelligence functions: to extract data from the wristband and guide users’ personal healthChennai Stock. The tool is supported by Google artificial intelligence model Gemini.New Delhi Investment
In addition, Google also announced a cooperation with the Indian Medical Center Apollo Radiology International to provide AI -driven lung tuberculosis, lung cancer and breast cancer screening services in India.
In detail, Google claims to develop a set of AI systems to interpret the chest X -ray scan to discover the early symptoms of tuberculosis.AI will be able to make screening more widely and provide additional inspections to identify occasional nodules and perform necessary follow -up treatment.It is reported that this move is to deal with the lack of local radiologists and hospitals in rural areas that are unable to provide laboratory.
In the next 10 years, Apollo Radiology International will use these models to provide 3 million free tuberculosis, lung cancer and breast cancer screening by artificial intelligence, help hundreds of thousands of people in India for timely treatment.Kolkata Stocks
Last week, Google began to launch an artificial intelligence tool called "MEDLM" to help doctors to screen surgery, radiation examination and diagnosis in the chest X -ray examination.The company also announced that its special customized version of its Gemini model scored 91.1%in the benchmark test based on the US medical license test.New Delhi Stock Exchange
According to IT House previously reported, the new diagnostic dialogue AI "AMIE" released by the Google DeepMind team defeated the doctor in the test and passed the Turing test.AMIE uses a "self -game" method in enhanced learning algorithms that can play self -play in an simulated environment and through automatic feedback mechanisms to expand and learn in various diseases, medical specialties and environments.
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