Artificial intelligence use is expected to grow exponentially larger in a relatively short amount of time. A recent report from Bain estimates the global AI market could reach nearly $1 trillion by 2027.
The meteoric rise of generative AI and natural language processing tools has meant an increased demand for the semiconductors that power that technology. Graphics processing units are considered the best chips for high computing performance. Multiple sectors use GPU chips, including the automotive, financial and blockchain industries.
As AI-powered technologies continue to grow in healthcare, with uses in medical diagnosis, drug discovery, robotic surgery and data management, health systems are also relying on the power of GPUs. Widespread demand for GPUs has led to a global shortage, prompting questions about the impact to medical AI.
“I think of GPUs as the brains for AI,” says Bill Lynch, head of life sciences strategic alliances and genomics for Pure Storage. “You need GPUs to make AI work, so you’re going to see continued demand for them.”
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GPUs in Healthcare Applications
While the debut of ChatGPT in 2022 accelerated the global conversation about AI, GPUs have had a presence in healthcare far longer than that. They’ve been used to enhance medical imaging for more than two decades, helping physicians make more precise diagnoses.
GPUs support advancements in medical research. They’re used to accelerate genomic sequencing, a method researchers use to study how and why diseases such as cancer form.
AI technologies fueled by GPUs have enhanced drug discovery. With help from generative AI platforms such as NVIDIA’s BioNeMo, “scientists can design drugs with desired characteristics that are more likely to lead to success in the clinic,” says Rory Kelleher, global business development lead for healthcare and life sciences at NVIDIA.
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