From Radiology to Drug Discovery, Survey Reveals AI Is Delivering Clear Return on Investment in Healthcare
Source: NVIDIA AI Blog
AI is Accelerating Every Aspect of Healthcare
From radiology and drug discovery to medical‑device manufacturing and new treatment methods enabled by digital twins of the human body, AI is reshaping the entire health‑care ecosystem.
NVIDIA’s 2025 “State of AI in Healthcare and Life Sciences” Survey
NVIDIA’s second‑annual report (see the full survey here) shows the industry moving from AI experimentation to execution, delivering measurable ROI on core applications such as medical imaging and drug discovery.
Key take‑aways:
| Insight | 2025 | 2024 |
|---|---|---|
| Organizations actively using AI | 70 % | 63 % |
| Using generative AI / LLMs | 69 % | 54 % |
| Open‑source software & models are moderately to extremely important | 82 % | — |
| Using or assessing agentic AI | 47 % | — |
| Executives reporting AI increases revenue | 85 % | — |
| Executives reporting AI reduces costs | 80 % | — |
“Over the next 12‑18 months, the most visible and scalable impact of AI will come from logistics and administrative streamlining,”
— John Nosta, President, NostaLab (health‑care think tank)
“That’s where adoption curves are already steep — scheduling, documentation, coding, utilization management and care coordination.”
What’s Driving the Momentum?
- Open‑source ecosystems: 82 % of respondents say open‑source tools and models are crucial to their AI strategy.
- Agentic AI: Nearly half of organizations are already using or evaluating autonomous agents for knowledge retrieval and research‑paper analysis.
- Generative AI & LLMs: Adoption has jumped from 54 % to 69 % in just one year, powering everything from clinical note generation to drug‑candidate ideation.
Read More
Explore the full report for deeper insights into:
- Clinical‑imaging breakthroughs
- Accelerated drug‑discovery pipelines
- AI‑driven manufacturing of medical devices
- Operational efficiencies in scheduling, documentation, and care coordination
🔗 Access the full NVIDIA “State of AI in Healthcare and Life Sciences” report
AI Adoption Ramps Up Across Healthcare and Life Sciences
AI adoption is up across every industry segment in this year’s survey — spanning digital healthcare, pharmaceutical and biotechnology, payers and providers, and medical technology and tools — with digital healthcare leading at 78 %, followed by medical technology at 74 %.
Top AI Workloads
| Rank | Workload | % of Respondents |
|---|---|---|
| 1 | Generative AI & Large Language Models | 69 % |
| 2 | AI for Data Analytics & Data Science | — |
| 3 | Predictive Analytics | — |
| 4 | Agentic AI (new to the survey) | 47 % |
Expert Insight
“Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself. The organizations seeing impact are those that embed AI into existing workflows instead of layering AI on top as a separate tool.”
— Dr. Annabelle Painter, Clinical AI Strategy Lead, Visiba U.K.
Use‑Case Highlights
- Medical Technology – 61 % of respondents use AI for medical imaging (e.g., radiologists working faster and more efficiently).
- Pharmaceutical & Biotechnology – 57 % report AI‑driven drug discovery.
Across the entire industry, the most common AI use cases are:
- Clinical Decision Support – e.g., radiologists receiving AI‑highlighted areas of concern on scans.
- Medical Imaging – automated analysis and interpretation.
- Workflow Optimization – streamlining operational processes.
AI Budgets to Increase With Strong ROI
AI is helping healthcare and life‑sciences organizations sharpen their core competencies, delivering a clear return on investment.
- Revenue & Cost Impact – AI drives higher annual revenue while cutting costs.
- Back‑Office Productivity – Workflow‑optimization tools boost efficiency across the organization.
- Operational Scaling – AI is expanding into patient interaction, administrative tasks, and other key business functions.
Key ROI Findings
| Segment | Top ROI Use Case(s) | % Reporting ROI |
|---|---|---|
| Medical‑technology | AI for medical imaging | 57 % |
| Pharma & Biotechnology | AI for drug discovery & development | 46 % |
| Digital healthcare providers | Virtual health assistants & chatbots | 37 % |
| Payers & providers (hospitals, primary‑care, insurers) | Administrative tasks & workflow optimization | 39 % |
Budget Outlook
- 85 % of respondents expect their AI budgets to increase this year.
- 12 % say budgets will stay the same.
- 46 % anticipate a significant rise—more than 10 % growth in AI spending.
“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.” – Painter
This section has been reformatted for clarity while preserving the original data and insights.
Using Open Source for Domain‑Specific AI Deployment
Leaning into open‑source models and software allows enterprises to build domain‑specific applications, giving them greater flexibility and efficiency while boosting business returns.
The healthcare industry has embraced open source, with 82 % of survey respondents stating it’s moderately to extremely important to their AI strategy.
“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 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.”
Download the
State of AI in Healthcare and Life Sciences: 2026 Trends report for in‑depth results and insights.
Sign up for NVIDIA’s Healthcare and Life Sciences newsletter.