Generative AI applications or workloads are being leveraged in 99% of healthcare organizations surveyed, more than any other industry, according to a report from Nutanix.
This includes a mix of applications from AI-powered chatbots to code co-pilots and clinical development automation. However, 96% share that their current data security and governance measures are insufficient to fully support generative AI at scale.
“In healthcare, every decision we make has a direct impact on patient outcomes — including how we evolve our technology stack,” said Jon Edwards, Director IS Infrastructure Engineering at Legacy Health.
“We took a close look at how to integrate generative AI responsibly, and that meant investing in infrastructure that supports long-term innovation without compromising on data privacy or security,” said Edwards.
Nutanix commissioned Vanson Bourne for the research. In the Fall of 2024, it surveyed 1,500 IT and DevOps/Platform Engineering decision-makers around the world.
This year’s report revealed that healthcare leaders are adopting generative AI at record rates while concerns remain. The number one issue flagged by healthcare leaders is the ability to integrate it with existing IT infrastructure (79%) followed closely by the fact that healthcare data silos still exist (65%), and development challenges with cloud native applications and containers (59%) are persistent.
“While healthcare has typically been slower to adopt new technologies, we’ve seen a significant uptick in the adoption of generative AI, much of this likely due to the ease of access to generative AI applications and tools,” said Scott Ragsdale, senior director at Nutanix.
“Even with such large adoption rates by organizations, there continue to be concerns given the importance of protecting healthcare data,” said Ragsdale. “Although all organizations surveyed are using generative AI in some capacity, we’ll likely see more widespread adoption within those organizations as concerns around privacy and security are resolved.”
Healthcare survey respondents were asked about GenAI adoptions and trends, Kubernetes and containers, how they’re running business and mission critical applications today, and where they plan to run them in the future.
Findings suggest that the adoption and deployment of generative AI solutions across healthcare will need a more comprehensive approach to data security. The No. 1 challenge faced by healthcare organizations when it comes to leveraging or expanding utilization of generative AI is privacy and security concerns of using large language models (LLMs) with sensitive company data.
Results also highlighted the need to prioritize infrastructure modernization to support generative AI at scale across healthcare organizations. However, 99% of healthcare respondents admit they face challenges when scaling GenAI workloads from development to production – with the No. 1 issue being integration with existing IT infrastructure.
Further, generative AI solution adoption in the healthcare sector continues at a rapid pace, but there are still challenges to overcome. Most healthcare organizations believe GenAI solutions will help improve levels of productivity, automation, and efficiency.
Meanwhile, real-world generative AI use cases across healthcare segments gravitate towards generative AI-based customer support and experience solutions (like chatbots), and code generation and code co-pilots. However, healthcare organizations also note a range of challenges and potential hindrances regarding generative AI solution development and deployment, including patient data security and privacy, scalability, and complexity.
In addition, application containerization and Kubernetes deployments are expanding across the healthcare industry. This is pervasive across industry sectors and is set to expand in adoption across healthcare as well, with 99% of industry respondents saying their organization is at least in the process of containerizing applications.
