AWS for Industries

Highlights from the AWS Healthcare and Life Sciences Executive Symposium 2023 at re:Invent

As a prelude to re:Invent 2023, AWS hosted the Healthcare and Life Sciences (HCLS) Executive Symposium in Las Vegas on Monday, November 27. The half-day, in-person event, attended by over 300 leaders from 180 organizations, centered around harnessing the power of data, analytics, machine learning (ML), and generative AI to accelerate innovation.

This year’s symposium was markedly different from previous years due to the transformative shift in our industry brought about by the possibilities of generative AI – from turbocharging R&D and improving business operations, to improving workforce satisfaction and patient experiences. The primary aim of this year’s symposium was only to not only to delve into these use cases, but also reinforce that the key to building powerful ML and generative AI applications lies in fluid access to high-quality data.

Here are the key highlights and notable insights from the sessions.

Highlights from the Opening Keynote

The symposium commenced with a keynote address, jointly presented by AWS and Merck. In a nutshell, the key takeaway from the keynote was – Your data is your differentiator for generative AI. Companies that will be successful in building generative AI applications that are truly able to transform their organizations are those that start with a solid, end-to-end data strategy.

To bring it to life, Dave Williams, CIO, Merck, joined on stage to show how they are reinventing themselves on the cloud with data, and building their data foundations on AWS to unlock  ML and AI for powering leading-edge science. Their session highlighted how the organization is reimagining itself with a CEO-sponsored digital transformation initiative, and emphasized how their cloud acceleration program, BlueSky, is playing a pivotal role in driving this transformation. In addition, they also walked the audience through their One Merck Data strategy to fuel frictionless flow of data throughout the organization, and high-impact ML and AI use cases being catalyzed by this strategic approach. Read more about Merck’s collaboration with AWS here.


The keynote set the stage for the forthcoming sessions.

  • Part 1 of the symposium focused on what an end-to-end data strategy looks like, and how AWS’ comprehensive health data portfolio helps organizations build that robust data foundation to unlock the full potential of ML and generative AI.
  • Part 2 of the day shifted focus to approaches for unleashing ML and generative AI breakthroughs on AWS. It explored relevant use cases for immediate impact and established guidelines for building applications using ML and generative AI responsibly.


Highlights from Part 1: Leveraging Data and Analytics to Scale Innovation

The opening AWS-led session on data provided the attendees a comprehensive overview of what an end-to-end data strategy entails. From unifying data across organizational silos, to facilitating secure and seamless access to third-party/real-world datasets, to developing capabilities for extracting insights from multimodal data, the presentation explained how these different components needs to integrate in harmony for that robust data backbone.

The session also dived into the practical aspects of the how’. The audience got a view into how AWS’ comprehensive Health Data Portfolio helps organizations create a hub for innovation, with a peek into our purpose-built solutions like AWS HealthOmics, AWS HealthImaging, AWS HealthLake, AWS HealthScribe, and more. The session also provided top-level views into our fit-for-purpose solutions like AWS Data Exchange, AWS Clean Rooms, and Amazon DataZone, for accessing and collaborating with data, both internally and externally.

Participants had the opportunity to learn from their colleagues actively building various elements of an end-to-end data strategy on AWS, gaining valuable insights to make the information more practical and applicable.

  • Genomics England shared how they are using AWS for transforming the storage, search, and governance of multimodal data. The session covered how are building one of the world’s largest multimodal data platform for cancer on AWS, linking whole genome cancer sequences with clinical, pathology, and radiology data for 16,000+ participants, to get answers for previously unanswered questions on cancer progression.
  • Bristol Myers Squibb presented how they are breaking internal data silos using AWS to make their data actionable. Attendees got a view into their data mesh architecture, and into their future plans of creating a data marketplace using Amazon DataZone, to improve data access and discovery across the organization, while ensuring the right levels of compliance.
  • Inovalon’s session dived into fueling external data collaborations using AWS, where they spoke about how their Inovalon ONE Platform is helping HCLS organizations access proprietary datasets to make better-informed decisions to improve the patient experience. Inovalon has moved apps and services managing 377+ million patient lives and 79+ billion medical events to AWS, and is currently experimenting with AWS Clean Rooms to fuel secure data collaborations with their customers. Read more in the Press Release.
  • Boehringer Ingelheim shared their journey of accelerating innovation with real-world data, with a view into their RWE Center of Excellence. The session delved into their usage of Amazon Data Exchange to discover and evaluate new RWD sources across the globe, fuel enterprise RWD data collaborations, and accelerate insight generation with near real-time integration of datasets.

To close, we hosted a panel discussion on ‘unlocking data for ML and generative AI use cases’ featuring data leaders from Stanford Health Care to discuss the data foundations they have put in place to move generative AI use cases from a proof of concept to org-wide capability The session provided a fitting conclusion to the first half of the symposium, and showed how it really looks like when all the different pieces work together in harmony to create a data-driven organization, ready to fully unlock the powers of ML and generative AI.

Highlights from Part 2: ML and Generative AI in Healthcare and Life Sciences

The second part of the event opened with an AWS-led session on how HCLS organizations can easily get started with ML and generative AI on AWS. The session demonstrated how AWS’ comprehensive set of AI and ML capabilities gives organizations the flexibility to build, buy, or customize the right tools for your business – making it easy, cost effective, and practical for them to leverage technology.

The audience got a view into new capabilities from Amazon Bedrock, that makes it really easy for builders to create and scale generative AI applications responsibly, with a choice of the latest foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI. The session also covered a quick overview of Bedrock Agents to show how, with a few clicks, Agents for Amazon Bedrock configures your FMs to automatically to break down and orchestrate tasks – without any manual code.

To enhance practicality, we showcased quick demos of a variety of pertinent generative AI use cases that are currently available for utilization – including RAG based chatbots for population health and oncology, pharma marketing content localization generator, drug discovery workbench, pharma manufacturing compliance audit, and agents that execute tasks using your enterprise systems and data sources.

The session also announced support of NVIDIA BioNemo on AWS, to accelerate R&D into advanced therapies using generative AI. NVIDIA BioNeMo, a generative AI platform for drug discovery, is now available on Amazon SageMaker via AWS ParallelCluster—and on the NVIDIA DGX Cloud on AWS. With this, researchers and developers at pharmaceutical organizations can now easily speed up drug discovery by simplifying and accelerating the training of foundation models using their own proprietary data.

The conversation around building ML and generative AI applications on AWS extended into the subsequent series of customer lightning talks.

  • On the life sciences side, Genentech (Roche) shared how they are using generative AI on AWS across three use cases within their commercial value chain–1/ personalized customer engagement, 2/sensing actionable customer needs, and 3/measurement automation and revenue simulation. The audience not only got a peek into the AWS-powered tech stack that brings these use cases to life, but also into the OneRoche Responsible AI Framework, which helps the organization build solutions responsibly with enterprise grade security.
  • On the healthcare side, Radiology Partners, the nation’s largest radiology practice, showcased how purpose-built services from AWS, like AWS HealthImaging, powers their AI-orchestration platform, RPX AI, enabling the swift deployment of AI medical imaging tools for hospitals and health systems, catering to the needs of the 3600+ physicians and 3300+ healthcare facilities they serve. Read more in the Press Release.

The rapid expansion of ML and generative AI promises innovative breakthroughs, but, at the same time, it also raises new challenges around privacy, security, governance, and fairness. So, for the last session of the day, we hosted a fireside chat on ‘Building generative AI responsibly and securely’, with leaders from Anthropic, Baxter, and Deloitte. The conversation delved into how each these organizations ‘defines’ responsible AI, and outlined their distinctive approaches towards establishing guardrails in the face of this rapidly evolving landscape. The audience took back valuable learnings on data security, privacy, IP protection, and fidelity, to better navigate the complexities and promote the use of AI in their organizations as a true force for good.

Healthcare and life sciences organizations were at the forefront of cloud adoption when we launched AWS 17 years ago. And, witnessing the industry leaders unite and take significant steps towards personalized health and precision medicine using ML and generative AI at our symposium was inspiring—it was a true depiction of what we call ‘the art of the possible’.

To learn more about our comprehensive set of data, ML, and generative AI offerings, visit the AWS for Healthcare and Life Sciences website.

To learn about other announcements and highlights for HCLS at re:Invent, check out this blog.

Kelli Jonakin, Ph.D.

Kelli Jonakin, Ph.D.

Kelli Jonakin is the Worldwide Head of Marketing for Healthcare, Life Sciences, and Genomics Industry verticals at AWS. She comes with a background in pharmaceutical research, with a special focus on development and commercialization of biologics. Kelli received her Ph.D. in Pharmacology and Systems Biology from the University of Colorado, and received an NIH post-doctoral fellowship grant to study Biochemistry at the University of Wisconsin-Madison.