NVIDIA survey: 70 pct of healthcare firms now actively using AI

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Artificial intelligence (AI) adoption in healthcare and life sciences has reached an inflection point, with 70 percent of organizations now actively using AI, up from 63 percent in 2024, and a strong majority reporting measurable returns on investment, according to NVIDIA’s second annual “State of AI in Healthcare and Life Sciences: 2026 Trends” survey report.

The survey, fielded from August to September 2025 and drawing responses from more than 600 industry professionals globally, found that 69 percent of respondents are now using generative AI and large language models, up from 54 percent the previous year.

The respondents included a 60/40 split between management (including executives) and AI practitioners, with a third reporting annual revenues of over USD 20 million and 40 percent from companies with over 1,000 employees.

AI adoption increased across every industry segment, with digital healthcare leading at 78 percent (up from 70 percent), followed by pharma and biotech at 74 percent, and medical technology, tools, and diagnostics at 70 percent.

The payers and providers segment, which includes hospitals, primary care doctors, and insurance companies, registered the strongest year-over-year gain, jumping 13 percentage points from 43 percent to 56 percent.

“Over the next 12-18 months, the most visible and scalable impact of AI will come from logistics and administrative streamlining,” said John Nosta, president of NostaLab, a healthcare think tank.

“That’s where adoption curves are already steep — scheduling, documentation, coding, utilization management, and care coordination.”

“Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself,” said Dr. Annabelle Painter, clinical AI strategy lead at Visiba U.K.

“The organizations seeing impact are those that embed AI into existing workflows instead of layering AI on top as a separate tool.”

The top AI workload across the industry was generative AI and large language models at 69 percent, overtaking data analytics and data science at 65 percent as the leading area of focus. Predictive analytics followed at 51 percent, with agentic AI at 47 percent.

Organizations are deploying AI across a range of use cases specific to their functions.

The overall top use case was clinical decision support at 42 percent, followed by medical imaging and administrative tasks and workflow optimization, both at 38 percent.

By sector, 57 percent of pharmaceutical and biotech respondents cited drug discovery and development as their top AI use case, followed by genomic applications at 44 percent across areas including NICU, cancer, rare diseases, population studies, and research.

For medical technology, medical imaging led at 61 percent, while digital healthcare’s top use case was virtual health assistants and chatbots at 52 percent.

For payers and providers, administrative tasks and workflow optimization topped the list at 52 percent.

The survey found that AI deployed toward specific use cases is delivering clear financial results.

Among management-level respondents, 85 percent said AI has helped increase their annual revenue, while 80 percent said it has helped decrease annual costs.

Overall, 44 percent of management respondents said AI has helped increase annual revenue by more than 10 percent, with small companies benefiting the most — 56 percent reported revenue increases exceeding 10 percent.

For cost reduction, 35 percent of respondents overall and 44 percent of small companies said AI has helped decrease costs by 10 percent or more.

From the top ROI use cases, 57 percent of medtech respondents reported returns from AI for medical imaging, while 46 percent of pharmaceutical and biotech respondents achieved ROI from AI for drug discovery and development.

Digital healthcare providers cited virtual health assistants and chatbots as their top ROI use case, and payers and providers pointed to administrative tasks and workflow optimization.

Improved employee productivity (19 percent), accelerated research and development (19 percent), and competitive advantage (18 percent) were identified as the top ways AI has improved organizations.

As a result, 85 percent of respondents said their AI budgets would increase in 2026, with 12 percent saying budgets would stay the same.

For nearly half of all respondents, budgets will grow by more than 10 percent year over year, including 50 percent of small companies and 40 percent of large companies.

The top spending priority for 2026 was optimizing AI workflows and production cycles, cited by 47 percent of respondents, up from 34 percent last year.

Spending directed toward identifying additional AI use cases fell from 47 percent to 37 percent, reflecting a shift from experimentation toward scaling proven solutions.

Building or gaining access to AI infrastructure rose to 34 percent, up from 24 percent.

Agentic AI — advanced AI systems designed to autonomously reason, plan, and execute complex tasks — made a notable debut in the survey, with 47 percent of respondents saying they are already using or assessing AI agents.

Of those, 22 percent said agents are already deployed, while another 19 percent said deployment will happen within the next year.

Medium-to-larger organizations (above 100 employees) showed higher agentic AI adoption at 52 percent, compared to 43 percent for smaller companies.

The top use cases for AI agents were knowledge management and retrieval at 46 percent, literature review and analysis at 38 percent, and internal process optimization at 37 percent.

Pharma and biotech’s top agentic AI use cases were literature review at 55 percent and drug discovery and biomarker identification at 48 percent.

Patient-facing sectors valued chatbots and digital agents for patient interaction, cited by 49 percent of digital healthcare and 39 percent of payers and providers respondents.

In the digital healthcare sector, 44 percent of organizations use agentic AI specifically for summarizing clinical notes.

Open-source software and models have emerged as central to the industry’s AI strategy, with 82 percent of respondents saying open source is moderately to extremely important.

Among small organizations, 64 percent rated open source as very or extremely important, compared to 49 percent of large organizations.

“Open models will shape the intellectual field,” said Nosta.

“They are essential for exploration and for keeping the field honest. But in clinical environments where safety, liability, and accountability are nonnegotiable, proprietary systems will remain necessary for validation, integration, and trust. The key insight here is that discovery will be open, and deployment will demand stewardship.”

“Healthcare organizations that successfully integrate AI are those that explicitly fund and prioritize evaluation as a core operational function, ensuring AI delivers measurable improvements in safety, quality, and patient care over time,” said Painter.

The survey also revealed a shift toward hybrid computing architectures for AI workloads, rising from 35 percent last year to 43 percent this year, while cloud computing fell from 41 percent to 35 percent.

When running inference, 38 percent of respondents cited model performance and benchmarking, as well as data residency and compliance, as the most important factors, with 37 percent citing cost efficiency and total cost of ownership.

Challenges varied by organization size. For small businesses, 40 percent cited lack of budget as a top challenge (compared to 20 percent of large companies), while 33 percent said insufficient data sizes for model training were a concern.

Large organizations, meanwhile, faced talent shortages, with 33 percent citing a lack of AI experts and data scientists.

Data-related issues such as privacy, location, and sovereignty were the top challenge for 39 percent of large companies, with 37 percent citing regulatory and ethical concerns.

For agentic AI specifically, performance and reliability were the top concerns at 27 percent, with 40 percent citing compliance with healthcare regulations such as HIPAA, FDA approval, and GDPR as the main factor influencing their approach to implementing agent solutions.

The report noted that AI has also been used to create digital twins of the human body to map tumors and help treat cancer, and that it has helped medical device manufacturers maintain regulatory readiness as devices are updated to new designs and best practices.

Looking ahead, the report suggested that by 2027, healthcare AI will likely shift from primarily predictive analytics toward more consistent deployment of agentic systems capable of reasoning across patient populations, trials, and care workflows.