Google Cloud today announced a partnership with Mayo Clinic to transform healthcare with generative AI, beginning with Enterprise Search in Generative AI App Builder (Gen App Builder), to improve clinical workflow efficiency, make it easier for clinicians and researchers to find the information they need, and ultimately help improve patient outcomes. Furthermore, Google Cloud announced today that its Enterprise Search on Gen App Builder is now HIPAA compliant.
Healthcare practitioners use information from a variety of sources, such as medical records, research papers, and clinical guidelines, to assist them with everything from condition definitions to diagnosis and treatment options. However, healthcare data is frequently stored in a variety of formats and places, making it challenging for physicians to obtain the information they require when they require it. Enterprise Search in Gen App Builder combines data from disparate sources like as documents, databases, and intranets, making it easy to search, analyze, and discover the most relevant results.
“Generative AI has the potential to transform healthcare by enhancing human interactions and automating operations like never before,” said Thomas Kurian, Google Cloud CEO. “Mayo Clinic is a world leader in leveraging AI for good, and they are a critical partner as we identify responsible ways to bring this transformative technology to healthcare.”
To continue developing all of these capabilities in a clinical setting, Google Cloud is collaborating with Mayo Clinic and several healthcare organizations.
“Our prioritization of patient safety, privacy, and ethical considerations, means that generative AI can have a significant and positive impact on how we work and deliver healthcare,” said Cris Ross, Mayo Clinic’s Chief Information Officer. “Google Cloud’s tools have the potential to unlock sources of information that typically aren’t searchable in a conventional manner, or are difficult to access or interpret, from a patient’s complex medical history to their imaging, genomics, and labs. Accessing insights more quickly and easily could drive more cures, create more connections with patients, and transform healthcare.”
Google Cloud’s generative AI products are based on years of research and proven AI applications, and will provide healthcare companies with new capabilities to generate real-time, personalized, and unique experiences that were not previously conceivable. Previously, AI analyzed large amounts of data to uncover patterns that would allow for increased efficiencies in existing activities. Data may now be examined in more complex ways, information can be condensed and processed, and new visuals can be created, among other things, thanks to generative AI.
The generative AI tools provided by Google Cloud, such as Gen App Builder and Generative AI support in Vertex AI, can assist healthcare organizations in optimizing workforce productivity, streamlining administrative processes, and leveraging technology to automate repetitive tasks, allowing caregivers to focus on higher-value patient interactions.
Mayo Clinic is an early adopter of Google Cloud’s Enterprise Search in Gen App Builder and is investigating how the combination of Google-quality search with generative AI can deliver critical information to doctors, clinicians, and other staff in a fast, seamless, and conversational manner.
Today’s announcement expands on Mayo Clinic’s prior collaboration with Google Cloud on digital transformation initiatives like as analytics, artificial intelligence, and machine learning technologies.
The way Google Cloud takes to data governance and privacy policies ensures that its clients retain control over their data. Access to and usage of patient data in healthcare settings is suitably safeguarded through the installation of Google Cloud’s dependable infrastructure and secure data storage, which enable HIPAA compliance, as well as each customer’s security, privacy controls, and protocols. Customers can also use Google Cloud’s responsible approach to generative AI to directly modify big language models and assess model replies for biased or unvalidated content, instructing the model to avoid incorrect outputs.
