China Medical University Hospital in Taiwan upgrades smart healthcare with Gen AI.

China Medical University Hospital (CMUH) in Taiwan made an announcement in mid-December 2023 regarding its collaboration with Google Cloud.

China Medical University Hospital (CMUH) in Taiwan made an announcement in mid-December 2023 regarding its collaboration with Google Cloud. CMUH has utilized Google Cloud’s generative AI technology, including MedLM—a large language model based on Med-PaLM 2—to develop a comprehensive AI-assisted Physician system. This system is designed to support healthcare professionals in various aspects, including disease diagnosis, treatment planning, patient education, and medical research.

The joint effort between CMUH and Google Cloud is focused on introducing assistive tools that specifically aid medical professionals in precision cancer treatment. Notable tools such as ‘Customised Cancer Treatment Guidelines’ and ‘Cancer Therapy Q&As’ are part of this initiative. The objective is to streamline information retrieval, reduce time spent searching for data, and enable the efficient gathering of accurate information. This, in turn, assists professionals in formulating personalized cancer treatment plans, delivering tailored treatment-related information to patients, and promptly responding to inquiries related to patient health education.

CMUH stands as one of the pioneering university hospitals in Asia to explore the capabilities of MedLM. Through its collaboration with Google Cloud, CMUH is committed to spearheading AI innovation in smart healthcare, contributing to the future of healthcare in Taiwan. The utilization of MedLM will facilitate CMUH in accessing timely and accurate medical information, aiming to establish top-notch healthcare AI models tailored for the Chinese-speaking market in Asia.

Furthermore, in the realm of new drug development, CMUH is leveraging Google’s specialized AI accelerators, known as Tensor Processing Units (TPU). These accelerators are employed for calculations related to protein folding and the development of new drugs. Initial tests at CMUH have demonstrated that utilizing these resources can significantly reduce computation time for related programs by more than tenfold.

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