Google Cloud has announced new features for its Vertex AI Search platform, which aims to provide a comprehensive and context-aware search capability across a broad spectrum of healthcare data sources. The new features include support for clinical data such as FHIR data and clinical notes, as well as integration with other Google Cloud solutions such as the Healthcare API and Healthcare Data Engine.
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What is Vertex AI Search?
Vertex AI Search is a generative AI-powered search platform that leverages Google’s foundation models to understand user intent and surface relevant information from various data sources. Vertex AI Search can handle text, voice, images, and video inputs, and support multi-modal and multi-turn interactions.
Vertex AI Search also offers intelligent search and summarization features from Google Health’s Care Studio pilot product, which helps healthcare professionals navigate complex medical records.
Techno Grinder shared a post on Twitter:
Google Cloud expands Vertex AI Search with features to help health care workers pull info from clinical notes, scanned documents, and electronic health records (Ashley Capoot/CNBC) https://t.co/YJid9v5hp3 pic.twitter.com/IAKN4G39rk
— TechnoGrinder (@TechnoGrinder) October 9, 2023
Why is Vertex AI Search Important for Healthcare and Life Sciences?
Organizations in the healthcare and life sciences sector confront substantial difficulties in maintaining and gaining access to the enormous amounts of data they produce and use. These data include crucial clinical information that is necessary for patient care and decision-making, such as FHIR data and clinical notes.
However, these data are often siloed, unstructured, or incomplete, making it difficult to find the right information at the right time. Vertex AI Search aims to address these challenges by providing a medically tuned generative AI search capability that can ingest and understand diverse healthcare data sources.
By using Vertex AI Search, healthcare and life sciences organizations can improve the efficiency and quality of their data search experience, reduce administrative burdens, and enhance patient outcomes.
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How Can Healthcare and Life Sciences Organizations Use Vertex AI Search?
Vertex AI Search offers a flexible and easy-to-use platform for building and deploying search and conversational applications for healthcare and life sciences. Depending on their development needs, organizations can use the following options:
- Out-of-the-box capabilities: Organizations can use out-of-the-box capabilities to build and deploy chatbots and search engines in hours and days with little or no coding and no prior machine learning experience. They can easily ingest data across various internal and external sources to build applications that understand user intent via natural language and surface the right information with associated citations and attributions from their public and private data.
- API integration: Organizations can use the API for direct integration to combine multiple capabilities into enterprise workflows. For example, they can integrate Vertex AI Search with other Google Cloud solutions such as the Healthcare API and Healthcare Data Engine to enable seamless data ingestion, transformation, analysis, and visualization.
- Customization: Organizations can make deep customizations via Vertex AI offerings such as Vector Search or Enterprise Knowledge Graph or Contact Center AI to tailor their applications to their specific needs. For example, they can use Vector Search to create custom embeddings for their domain-specific data or use Enterprise Knowledge Graph to create a unified view of their data across different sources.
How to Get Started With Vertex AI Search?
Vertex AI Search is now generally available for healthcare and life sciences organizations. New customers can enjoy a one-time $1,000 credit per Google Cloud billing account. To get started, visit the Vertex AI Search website or contact sales. You can also watch the Vertex AI Search breakout sessions at Next ’23 to learn more about the platform and its use cases.
