- NVIDIA announced nearly 20 new tools focused on AI-powered healthcare at the 2024 GTC AI conference last week, joining forces with Johnson & Johnson and GE Healthcare in surgical and medical imaging. We are conducting a transaction.
- For the AI chip leader, the move into healthcare has been a decade in the making and has significant revenue potential.
- AI for drug discovery is a process that can take up to 12 years and cost billions of dollars, but it is being deployed rapidly.
Nvidia CEO Jensen Huang delivers the keynote address at the Nvidia GTC conference at SAP Center on March 18, 2024 in San Jose, California.
Justin Sullivan | Getty Images
Last week, Nvidia announced a deal with Johnson & Johnson for the use of generative AI in surgery and a deal with GE Healthcare to improve medical images. Healthcare Advancements at his 2024 GTC AI Conference also included the announcement of about 20 new tools focused on AI-powered healthcare, and his future Nvidia non-tech division shows how important healthcare is to revenue opportunities.
“The reason why Nvidia is so popular today is because it wasn't easy to do before, or if you had to do something like this, it probably would have taken several times more time, money, and cost. Because we basically provided the plumbing and the technology to make it happen,” said Raj Joshi, technology analyst and senior vice president at Moody's Ratings. “Healthcare is a very strong field, including biotechnology, chemicals, and drug discovery.”
Nvidia stock is up nearly 100% since the beginning of the year, and the biotech industry is an example of untapped potential that investors continue to bet on. AI can speed up the drug discovery process and even find uses for drugs that may not have yielded results in the disease for which they were originally developed.
“Over the past 18 months or so, we have built on the hype due to tangible results and very compelling use cases of how AI has helped in the pharmaceutical, medtech, and biotech industries. I tended to believe that it was more of a hope,” Arda Ural said. EY America's health and life sciences market leader.
Ural said developing new drugs is a risky process that can take at least 10 years from concept to clinical research. It's also a process that has a high chance of failure and costs billions of dollars.
Some 41% of biotech CEOs surveyed by EY in late 2023 said they were considering “tangible” ways their companies could use generative AI. “From my experience of working in this industry for 30 years, this is a very high number,” Ural said. “This is a very unique feature that we’re seeing in AI that was recognized much earlier than other technologies.”
Nvidia's focus on healthcare at the conference doubled down on the company's long-held ambitions. During an earnings call with investors in February, Nvidia mentioned several ways its technology could be adapted to the medical field. Companies like Recursion Pharmaceuticals and Generate: Biomedicines are scaling up their biomedical research with the help of hyperscale or GPU-focused cloud providers, and they need Nvidia AI infrastructure to facilitate that process. I am.
“In healthcare, digital biology and generative AI are helping reinvent drug discovery, surgery, medical imaging, and wearable devices,” said Colette Kress, Nvidia's chief financial officer. “Over the past 10 years, we have built deep expertise in the healthcare space and are proud to offer the NVIDIA Clara healthcare platform and generative AI services to develop, customize, and deploy AI-based models for computer-aided drug discovery. He has created NVIDIA BioNeMo.”
Last year, NVIDIA invested $50 million in Recursion for drug discovery projects. Recursion inputs biological and chemical data to train his NVIDIA AI models on a cloud platform. The company is also working with Roche's Genentech to develop new drugs and better treatment protocols. In 2021, we also partnered with Schrödinger in the drug discovery field.
One of NVIDIA's greatest strengths in healthcare to date is its BioNeMo platform, a generative AI cloud service purpose-built for drug discovery and development.
“It's one thing to design a semiconductor or a computing platform so that someone else can do something; it's quite another to be able to build a full-fledged technology package that you can sell to customers,” Joshi said. “If you're a biotech company, you take a complete technology from Nvidia and instead of thinking, 'How do I use this information technology?' you just start working on it.”
Biotechnology-focused generative AI platforms have the ability to reduce costs for pharmaceutical companies beyond the drug development process. To save costs, many companies have offshored back-office processes, not only for manufacturing, but also for supply chain, finance, and administrative functions. However, as geopolitical tensions increase and there is an emphasis on bringing jobs back to the United States, the costs of moving jobs overseas are increasing.
“Thanks to AI-powered robotic process automation, we can now leverage AI to do it at home at a much lower cost,” Ural said. “So not only does it help speed up drug development, but it also helps reduce operating costs for companies, which means they can put more capital into drug development and find more treatments faster.” It is.”
The healthcare space is an example of how far a company that was designing gaming graphics cards a decade ago has come. “To their credit, Jensen had the foresight when he saw some people at Stanford University actually use his graphics card to solve some kind of mathematical problem in 2012. I have to say,” Joshi said. “He said, 'You know, this can actually be used to do what we call general computing, which is what we all do every day on a daily basis.' ”
But to fully realize the benefits of AI that are emerging in healthcare, leaders will need even more support from one of the nation's largest workforces. According to EY's 'AI Concerns in Business' survey, more than two-thirds of employees in the health sciences and wellness sector are concerned about the use of AI, and seven in 10 are concerned about the use of AI in the workplace. I am worried about the introduction of
