NVIDIA: How are 70% of Healthcare Organisations Using AI?

NVIDIA is planning to support the advance of healthcare with AI, aiming to adapt across science, robotics and intelligent agents. 

In NVIDIA’s most recent “State of AI in Healthcare and Life Sciences” survey report, it finds the healthcare industry is transitioning from AI experimentation to full-scale implementation, achieving ROI in key areas such as medical imaging and drug discovery.

From the lab and genomic testing to diagnostic imaging, the company is planning to empower healthcare partners to “accelerate discovery, improve care and drive innovation with scalable, high-performance solutions.”

AI adoption across healthcare and life sciences

AI is rapidly transforming healthcare and life sciences, with adoption reaching its biggest peak to date in 2026. 

According to the survey of more than 600 industry professionals, 70% of organisations are actively using AI, up from 63% in 2025. 

Additionally, 69% are using generative AI and large language models, marking a sharp rise from 54% the previous year. 

Adoption spans every major industry segment, including digital healthcare (78% active use), pharma and biotech (74%) and medical technology (70%). 

Even payers and providers, historically slower to adopt new technologies, saw a 13% increase in AI use year over year, rising from 43% to 56%.

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

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

“Open models will shape the intellectual field.

“They are essential for exploration and for keeping the field honest. 

“But in clinical environments where safety, liability and accountability are non-negotiable, 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.”

The survey finds that predictive and data analytics remain foundational, with 65% of organisations using AI for data analytics and data science and 51% leveraging predictive analytics. 

Clinical integration is advancing as well, 42% cite clinical decision support as their top AI use case, while 38% report using AI for medical imaging and 38% for administrative workflow optimisation.

Targeted AI use for measurable ROI

The report highlights that AI generates the strongest returns when applied to specific, well-defined healthcare use cases. 

In the medical technology segment, 57% report achieving ROI from AI in medical imaging.

Similarly, 46% of pharmaceutical and biotech organisations report ROI from AI in drug discovery and development. 

Digital healthcare organisations identify virtual health assistants and chatbots as top ROI drivers, while payers and providers emphasise administrative task automation and workflow optimisation.

Across the broader industry, the top ROI-generating applications include medical imaging, workflow optimisation and natural language processing (NLP) for clinical documentation.

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

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

“Healthcare organisations 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.” 

From a business performance perspective, the survey finds that 85% of management respondents say AI has increased annual revenue and 80% report reduced annual costs.

Notably, 44% state that AI increased revenue by more than 10%, with small companies benefiting significantly, 56% report revenue growth exceeding 10%.

On the cost side, 35% overall and 44% of small companies report cost reductions greater than 10%.

The rise of agentic AI and open-source strategy

A notable trend in 2026 is the emergence of agentic AI, advanced systems capable of autonomous reasoning and task execution.

NVIDIA's data shows that 47% of respondents say they are actively using or assessing AI agents, including 22% who have already deployed them and 19% planning deployment within the next year.

The top use cases include:

• Knowledge management and retrieval: 46%
• Literature review and analysis: 38%
• Internal process optimisation: 37%.

In pharma and biotech, 55% use agentic AI for literature review and nearly half deploy it for drug discovery and biomarker identification.

Open-source tools are central to this expansion. 

The survey found that 82% of respondents say open-source models and software are moderately to extremely important to their AI strategy, enabling organisations to fine-tune models for specialised clinical and research tasks. 

Hybrid computing is also increasing, with 43% of organisations using hybrid infrastructure for AI projects, up from 35% the previous year.

Investment momentum and future outlook

Strong financial returns are driving continued investment. 

NVIDIA’s results show that 85% of respondents say their AI budgets will increase in 2026 and nearly half anticipate growth exceeding 10%. 

Spending priorities are shifting toward scaling proven solutions: 47% plan to focus on optimising AI workflows and production cycles, compared to 34% the previous year. 

Additionally, 34% cite building or expanding AI infrastructure as a key investment priority, up from 24% in 2025.

Despite momentum, challenges remain. Smaller organisations report budget constraints (40%) and insufficient data for training (33%) as top barriers, while larger enterprises cite data-related concerns such as privacy and security (39%) and regulatory and ethical issues (37%). 

For agentic AI specifically, 40% say compliance with healthcare regulations, including HIPAA, FDA approval processes and GDPR, strongly influences implementation strategies.

Overall, the data indicate that AI in healthcare and life sciences has moved beyond experimentation. 

With high adoption rates, measurable revenue gains and increasing budget allocations, AI is becoming embedded in clinical workflows, research pipelines and operational systems, positioning the industry for even broader transformation in the years ahead.