While healthcare and life science organizations have been using AI and ML for years, generative AI brings new possibilities to accelerate innovations, increase efficiencies, and improve outcomes across the health continuum. From generating new therapeutic candidates, to better matching patients with the right clinical trials, to powering patient engagement applications, AWS makes it easier to access the services, data, models, and secure infrastructure needed to scale generative AI across your organization.
Latest generative AI news in healthcare & life sciences
Virgin Pulse collaborates with AWS to accelerate Homebase for Health® platform innovation and user experience
Virgin Pulse is leveraging AWS's cloud capabilities, including generative artificial intelligence (generative AI), machine learning, and analytics, to deliver an innovative roadmap and enable advanced personalization across its Homebase for Health® platform.
CrowdStrike to accelerate development of AI in cybersecurity with AWS
CrowdStrike is working with AWS to develop powerful new generative AI applications that help customers accelerate their cloud, security and artificial intelligence (AI) journeys. These include both cybersecurity-related generative AI applications, as well as cloud-plus-cloud security solutions designed to help customers build and secure their own generative AI applications.
Philips joins forces with AWS to bring Philips HealthSuite Imaging PACS to the cloud and advance AI-enabled tools in support of clinicians
Market-leading Philips PACS is now available on AWS for industry-leading availability, reliability, security, and AI-supported workflow enhancements. Expansion of the collaboration with AWS will support the development and deployment of generative AI applications that further support efficient clinical workflows and enhance diagnostic capabilities.
Harnessing the power of enterprise data with generative AI: Insights from Amazon Kendra, LangChain, and large language models
This post explores how Retrieval-Augmented Generation (RAG), combined with Amazon Kendra, can incorporate external knowledge to augment initial trainings on massive datasets to provide refined responses to natural language queries.
Pieces Pioneers "Sculpted AI" for Health Systems using Amazon Bedrock
Pieces Technologies announced it has fully incorporated the latest generative artificial intelligence services from AWS into its healthcare solutions supporting providers at the point of care. Pieces is producing "Sculpted AI"—AI technology tailored to health systems' specifications through a highly iterative, granular process down to the unit, clinical specialty, or physician level—using Amazon Bedrock, Amazon EC2, and Amazon SageMaker Canvas.
Get started faster with purpose-built generative AI services such as AWS HealthScribe and Amazon Bedrock — the easiest way to build and scale generative AI applications with foundational models.
Choose the right model for your use case
Choose from a wide selection of industry leading foundation models from Amazon, AI21 Labs, Anthropic, Cohere, Meta, and Stability AI in Amazon Bedrock. And customize foundation models with your own data to build more differentiated, personalized experiences.
Security and privacy from day one
With security and privacy built-in, your data remains protected and private when you customize foundation models. Learn more about responsible generative AI practices and what can be done to reduce the risks.
Leverage generative AI to develop new and adapt existing promotional content. Bring together core messaging, claims, references, and relevant imagery to create engaging collateral that aligns with regulations.
Healthcare organizations are being asked to control costs while also improving outcomes and patient and care team experiences. The use of generative AI has the potential to inform actions to achieve these goals and realize health equity.
Ambient digital scribe
Leverage automatic speech recognition and generative AI to create transcripts, extract key details, and create summaries from clinician-patient interactions.
Enhance and reconstruct medical images to aid in diagnosis or generate medical images to be used as synthetic data for refinement of ML models. Create automated reports from images, speeding clinical decisions and reducing clinician workload.
Summarize and generate insights from health documents such as medical papers and therapeutic research to help readers focus on key points of a document, transform unstructured text into standardized formats, and highlight important attributes to drive better decisions.
ScribeEMR’s goal is to help increase practice efficiency, maximize revenue, and reduce clinician burnout in the healthcare industry. With AWS HealthScribe, our advanced processes can now capture and interpret patient visits more effectively and optimize EMR workflows, coding, and reimbursement processes. This breakthrough represents our relentless pursuit of improving efficiency, profitability, and most importantly, patient care."
— Daya Shankar, Co-Founder and General Manager, ScribeEMR
Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
Find additional resources to educate you on generative AI on AWS for healthcare and life sciences.
AWS BUSINESS INTELLIGENCE BLOG
Announcing Generative BI capabilities in Amazon QuickSight
The new LLM capabilities available through Amazon Bedrock provide Generative BI capabilities in QuickSight to enable analysts to build and refine visuals as well as create calculated fields using natural language.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Stability AI, and Amazon, along with a broad set of capabilities to build generative AI applications, simplifying the development while maintaining privacy and security.
Unlocking the potential of generative AI to accelerate innovation
This level 200 series will dive beyond the fundamentals of Generative AI into specific considerations in creating a LLM. Additionally, the series will highlight real-life applications that have utilized foundational models to accelerate protein engineering and structure-based drug development, provide clinical decision support for more accurate diagnoses, and more.
Amazon and Anthropic announce strategic collaboration to advance generative AI
Anthropic selects AWS as its primary cloud provider and will train and deploy its future foundation models on AWS Trainium and Inferentia chips, taking advantage of AWS’s high-performance, low-cost machine learning accelerators.
Announcing new tools for building with Generative AI on AWS
Announcing Amazon Bedrock, a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. Bedrock is the easiest way for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders. Bedrock will offer the ability to access a range of powerful FMs for text and images—including Amazon’s Titan FMs, which consist of two new LLMs we’re also announcing today—through a scalable, reliable, and secure AWS managed service.
An introduction to generative AI with Swami Sivasubramanian
Watch Werner Vogels interview Swami Sivasubramanian, VP of database, analytics and machine learning services at AWS, speak about the broad landscape of generative AI, what we’re doing at Amazon to make large language and foundation models more accessible, and last, but not least, how custom silicon can help to bring down costs, speed up training, and increase energy efficiency.
Demystifying LLMs with Amazon distinguished scientists
Werner Vogels dives deep into LLMs with Amazon's distinguished scientists, Sudipta Sengupta and Dan Roth, both of whom are deeply knowledgeable on machine learning technologies. The trio cover topics from from word representations as dense vectors to specialized computation on custom silicon.