How AI Has Transformed Healthcare’s Future
Rangan Sukumar, a distinguished technologist for HPE GreenLake, explains, “AI’s integration into medical research is transforming the landscape by providing unprecedented access to data and computing power, allowing for rapid processing and analysis that were previously unachievable.”
How Medical Imaging Fuels Innovation in Medicine
The process of analyzing images always requires a human in the loop, be it for sports medicine or any medical condition, Sukumar says. For example, a radiologist must mark and visually inspect X-rays, CT scans, and MRIs and write a report describing the case and the diagnosis. This can take multiple days to weeks in some cases.
With the advent and adoption of computer vision technologies, this process of automatically segmenting regions of interest in medical images, classifying pathology, and inferring disease state and condition is all possible in a few seconds. Through HPE GreenLake, researchers have access to vast amounts of data, enabling them to draw insights faster and more efficiently. Sukumar notes, “In sports medicine, where timely interventions can mean the difference between recovery and prolonged injury, the ability to analyze and act on data rapidly is crucial. This principle extends to all medical research where timely insights can lead to quicker advancements and better patient outcomes.”
HPE GreenLake enhances medical imaging technology by providing the necessary computing power to process and analyze images quickly. This is particularly impactful in sports medicine, where quick and accurate imaging can lead to better diagnosis and treatment of injuries. “Injury management in sports medicine relies heavily on accurate and timely imaging. By reducing the time taken to analyze these images, we can significantly improve recovery times for athletes,” says Sukumar.
Making innovation possible with AI
According to Sukumar, many organizations need help in integrating or launching AI solutions, so HPE GreenLake offers a suite of end-to-end solutions designed to make innovation accessible for any company.
Sukumar states that the company tailors its solutions offerings based on what they’ve gleaned from years of clients communicating what they need to be successful in integrating AI into their workflows.
“We give them access to GPUs, to software platforms, (and) the ability to set up software-as-a-service,” says Sukumar. In addition to supporting organizations in the later stages of AI-driven innovation, Sukumar states, HPE GreenLake allows organizations to access essential AI development functions on demand, due to the complex and resource-intensive process of framing initial AI models.
“AI, in its workflow, is sometimes going to require hundreds of GPUs for multiple weeks to frame the model,” Sukumar says. “And once you’re in production, you’re going to (have) millions of people pinging queries to an infrastructure that they won’t need for more than a second,” Sukumar states. That scale of activity and pool of data requires an agile system that is secure and scalable in the critical stages when innovative companies are tailoring AI solutions to their needs, Sukumar says.
“HPE comes out of the box with security pre-packaged,” Sukumar states. “We know how to handle scale, and we reduce the complexity for the customer.”
That capability to manage scale seamlessly is critical for clients in areas like the life sciences, Sukumar says, where the need to scale quickly can complicate another key component of research and development–precision.
“So, if you are a pharma company, you shouldn't be worried about the computing and the IT parts of infrastructure that goes behind innovation, right?” Sukumar asks. “You should worry about the pharmacology of biology and (your) patients.”
Sukumar states that HPE GreenLake prepares their clients to scale well before they need to so they can focus on innovation.
“So, when we think about (AI and) scale, it's not just the type of model, it's not just the number of parameters they have modeled, you're also starting to think about how to scale the number of queries.” Sukumar says. “If success means 80 million users pinging 10 million queries a day, you're not going to be able to get started on that kind of infrastructure from day one, right?”
Sukumar says that in industries like pharma where scalable precision is essential for positive outcomes, HPE GreenLake is a valuable partner due to its commitment to responsible AI governance and its ability to provide access to scale from day one.
“Our labs (teams) work to make sure that if we do partner with somebody and develop AI, our AI is safe, secure, and explainable,” Sukumar says.
According to Sukumar, that means organizations can scale AI development with confidence in their foundational models and the security of their data.
One example shared by Sukumar is how HPE GreenLake supports AI-driven medical research.
“HPE comes out of the box with security pre-packaged,” Sukumar states. “We know how to handle scale, and we reduce the complexity for the customer.”
RANGAN SUKUMAR
Technologist for HPE GreenLake
Searching for the “What-Is,” “What-Else,” and “What-Could-Be” in Medicine Through AI
In the realm of AI-powered scientific research, the “what-is,” “what-else,” and “what-could-be” frameworks are crucial to accelerating scientific innovation and discovery. This approach uses AI to explore vast data sets, identify new possibilities for treatments, and hypothesize future shifts in population health.
WHAT-IS
Searching for the “what-is” involves using AI to aggregate and analyze existing data to understand the current state of knowledge. For instance, in medical research, AI can sift through vast amounts of scientific literature and experimental data to create a comprehensive view of current findings.
WHAT-ELSE
The “what-else” exploration seeks to uncover connections and insights in data that may not be immediately apparent, even for teams of highly trained researchers. AI can link seemingly unrelated information to reveal new research pathways that yield better results. For example, AI models have identified potential correlations between tetanus vaccination rates and reduced COVID-19 severity, showing a possible new area for researchers to investigate, Sukumar explains.
WHAT-COULD-BE
Finally, the “what-could-be” step involves using AI to predict future developments and propose new hypotheses. This forward-looking approach allows researchers to generate and test new ideas in silico (or by computer) before moving to clinical trials. AI’s ability to model complex interactions and predict outcomes is essential in fields like drug discovery and personalized medicine, where lost time can mean lost lives.
HPE GreenLake – Accelerating Medical Innovation Past Complexity
HPE GreenLake is playing a crucial role in the future of healthcare innovation. HPE GreenLake helps organizations tackle global and small-scale medical challenges by providing scalable solutions. “Our goal with HPE GreenLake is to make AI and large-scale data processing accessible and effective for all healthcare organizations, enabling them to achieve their research and clinical objectives,” states Sukumar.
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