Artificial intelligence (AI) is transforming the life sciences and it's evolving at breakneck speed. For people waiting for a new medicine or access to better healthcare, that’s great news. For technology companies, the urgent demand for AI innovation presents tremendous opportunities. There are also challenges, like security, data bias and accessibility—to name a few. So it won’t surprise you when we say the solution to these challenges is open source development.
Open source is more than open code. That’s why Red Hat launched InstructLab, to make sure transparency, a heterogeneous community and an easy on-ramp are baked into fine tuning models and building use cases for generative AI (gen AI). It’s also why we’re all in on advancing a technology platform where domain researchers, engineers and global hardware and software vendors can collaborate to develop new AI tools and solutions and improve those already in use.
Through the longtime collaboration between Red Hat Research and the Mass Open Cloud Alliance (MOC-A), we’re building an ecosystem to drive breakthroughs in the life sciences with the power of AI through open source. The MOC-A is a collaboration among universities, government agencies and industry leaders to build and maintain an open cloud and provide researchers with compute resources most could not access otherwise—things like CPUs, GPUs, storage, large and diverse data sets, AI tools and AI models–all housed in a carbon-neutral data center.
Open source AI practices in life sciences in action
In November 2024, Red Hat joined with Boston University, IBM Research, the MOC-A and other research institutions to sponsor an open forum on AI for drug discovery, where we launched the AI Alliance AI for Drug Discovery Working Group. On average, discovering and developing a new drug takes 10-15 years—which may as well be an eternity for the sickest patients—with costs measured in billions of dollars. Streamlining drug discovery with AI has the potential to change lives while dramatically cutting healthcare costs.
We created accessibility by using Red Hat OpenShift AI on the MOC, so participants at the forum could test and interact with open source models in an environment made user-friendly for domain scientists. Researchers built models, scaled resources and experimented with data in the MOC environment, which is designed to facilitate transparency. Researchers could maintain access to their work after the event, and anyone with an MOC account can still explore and experiment with the same models. The economy of scale makes it possible to democratize the availability of powerful research tools, and both researchers and developers—and ultimately, patients themselves—benefit from broader participation.
This event and the seeds of innovation that it planted, however, didn’t just happen. It was built on the established and evolving foundation of the MOC-A, which we see only growing in importance as AI concepts and technologies take hold of the research world.
Creating the conditions for innovation
Red Hat’s partnership with the MOC-A is a vital component of our strategy for applying the power of AI and open source to major life science challenges. Successful development, implementation and adoption of AI solutions in this high-stakes field requires accessibility, transparency and scale.
Accessibility
What makes a technology platform usable? It depends on the user. Researchers and clinicians need to focus on bringing their domain knowledge to bear on difficult problems, not mastering the care and feeding of an AI model. They also need to work efficiently, not jump in and out of tools. If a solution doesn’t make a user more productive, they won’t adopt it.
Accessibility can also be as simple as availability. Today, only a handful of national research hospitals and preeminent drug companies have ready access to AI tools and platforms. University researchers and regional hospitals, on the other hand, are often constrained by a lack of resources, whether that means computing resources, infrastructure or funding.
Because open source development insists on including all stakeholders, we involve domain experts from the beginning so we know the end result will be something they can use. We saw a powerful example of this collaborative approach at Red Hat Summit 2024 when Leigh Day, Red Hat senior vice president and chief marketing officer and Dr. Ellen Grant, director of the Fetal Neonatal Neuroimaging and Development Science Center at Boston Children’s Hospital (BCH) discussed how Red Hat OpenShift on the MOC can be used to reduce interpretation time for radiologists. These advances aren’t limited to a single hospital–Red Hat engineers and BCH have created a self-provisioning system that allows users with limited resources anywhere in the world to deploy open source medical analytics tools on any edge device, even a Raspberry Pi.
Transparency
Transparency is critical to driving AI adoption in life sciences, and the bar is high. Patients want to know how their data is used and protected, and researchers want to know that datasets are meaningful and models are relevant. No one is going to trust a closed black box in domains where life-or-death decisions are being made. Are clinicians going to dive in and review open sourced code or models for themselves? Probably not. But tools that can be examined, customized and even improved through collaboration can earn trust in a way closed systems cannot.
That said, you can’t hand someone a fully developed solution and call it transparent, even if it’s open source. Just as we need domain stakeholders to drive usability, we also need them as an ongoing part of the development process. Working this way also provides the flexibility needed to solve specific problems. One-size-fits-all commercial solutions aren’t going to work for all life science challenges and workflows. Working in an open source ecosystem makes it possible to tune the capabilities of a base set of tools to the needs of an individual user, and when those are contributed back to the community, others can build on them. When we work transparently, new solutions are driven by users’ needs, not a product roadmap focused solely on the bottom line. Most critically for life science use cases, innovations reach deployment stage at an accelerated pace that proprietary software would find difficult to beat.
Scale
Fortunately, the days of scaling for scale’s sake are over. One of the critical lessons of pre-taining, for example, is that using smaller datasets and a smaller infrastructure footprint can produce faster, better outcomes for specific real-world use cases. So what do we mean when we say scale is required to develop AI applications for life sciences in an open source way?
First, we need economies of scale. AI development and workloads are resource intensive, and high-quality life science datasets exceed the capabilities of research IT at many institutions. But those resources don’t have to exist in silos. Initiatives like the MOC-A that enable resource sharing can provide the scale that makes accessibility a reality.
Second, we need to galvanize the power of the many. There’s no democratization without propagation—in other words, getting tools in as many hands, in as many places, as possible. Red Hat is committed to supporting the growth of an ecosystem of users, developers, hardware and software vendors, life science industry groups and nonprofits that reaches across disciplines and catalyzes the collaborations needed to build transformational solutions to previously unsolvable problems.
Finally, a flourishing ecosystem won’t just speed up innovation and discovery. Collaboration with industry, government and research institutions can also foster new possibilities and plant seeds for startups, fueling a virtuous cycle that creates new opportunities for both research and business growth.
We believe it’s vital that high quality open source models and tools dominate AI for life sciences. We’re proud that through solutions like Red Hat Enterprise Linux AI, Red Hat OpenShift and Red Hat OpenShift AI and initiatives like MOC-A, Red Hat is helping to lead the evolution of an open source ecosystem with the potential to make this happen.
About the author
Orran Krieger is the Director of Red Hat Research (while on sabbatical). He is also a Professor in the Department of Electrical and Computer Engineering at Boston University.
He is a founding lead on the Mass Open Cloud/Mass Open Cloud Alliance. Before joining Boston University, Orran was a Hariri Institute Fellow. He spent five years at VMware, where he launched and worked on vCloud. Prior to that, he was a researcher and manager at IBM T. J. Watson, leading the Advanced Operating System Research Department. Orran obtained his PhD and MASc degrees in Electrical Engineering at the University of Toronto.
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