How and Why 70% of Healthcare Companies Are Implementing AI

AI is fast cementing its position as a transformative force in healthcare and life sciences, with adoption rates reaching unprecedented levels in recent months.

According to NVIDIA's most recent "State of AI in Healthcare and Life Sciences" survey report, 70% of organisations across the sector are actively deploying AI technologies, representing a marked increase from 63% the previous year.

The findings, based on responses from more than 600 industry professionals, suggest the industry has moved decisively beyond pilot projects towards full-scale implementation with measurable return on investment (ROI).

Why Gen AI adoption has surged across industries

The uptake of Gen AI and LLMs has proven particularly dramatic across all the subsectors of healthcare, with 69% of organisations now utilising these technologies compared to just 54% the previous year.

Digital healthcare leads at 78% active use, followed by pharmaceuticals and biotech at 74% and medical technology at 70%.

Even traditionally slower-moving segments are catching up. Payers and providers experienced a 13% year-on-year increase in AI use, rising from 43% to 56%.

Clinical integration is advancing alongside foundational applications too. According to the report, 42% cite clinical decision support as their top AI use case, while 38% report using AI for medical imaging and an equal proportion for administrative workflow optimisation.

John Nosta, President of NostaLab, a healthcare think tank, believes the most immediate impact will come from operational improvements. "The most visible and scalable impact of AI will come from logistics and administrative streamlining," says John.

Targeted applications drive measurable returns

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.

Dr Annabelle Painter, Clinical AI Strategy Lead at Visiba UK, emphasises the importance of integration over innovation for its own sake. "Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself," says Annabelle.

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 as 56% report revenue growth exceeding 10%.

Healthcare organisations are finding that targeted AI applications deliver faster time-to-value compared to broad, unfocused deployments.

By concentrating resources on high-impact use cases with clear metrics, organisations can demonstrate tangible benefits to stakeholders whilst building the infrastructure and expertise needed for wider adoption across their operations.

Agentic AI emerges as next frontier

A notable trend 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.

The top use cases for agentic AI include knowledge management and retrieval at 46%, literature review and analysis at 38% and internal process optimisation at 37%. In pharma and biotech specifically, 55% use agentic AI for literature review and nearly half deploy it for drug discovery.

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.

Strong financial returns are driving continued investment. NVIDIA's results show that 85% of respondents say their AI budgets will increase and nearly half anticipate growth exceeding 10%. Spending priorities are shifting toward scaling proven solutions, with 47% planning to focus on optimising AI workflows.

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

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.