From research and discovery to patient care and administrative tasks, AI is showing transformative potential across nearly every part of healthcare and life sciences.
For example, generative AI can be used to help automate repetitive, time-consuming tasks such as summarizing and creating documents and extracting and analyzing data from reports. It can also aid in drug discovery by finding new protein structures and offer assistance to patients through chatbots and AI assistants, easing the burden on clinical and administrative staff.
This wide range of applications was among key insights of NVIDIA’s inaugural “State of AI in Healthcare and Life Sciences” survey.
The survey — which polled more than 600 professionals across the globe from fields spanning digital healthcare, medical tools and technologies, pharmaceutical and biotech, and payers and practitioners — revealed robust AI adoption in the industry, with about two-thirds of respondents saying their companies are actively using the technology.
AI is also having a tangible impact on the industry’s bottom line, with 81% of respondents saying AI has helped increase revenue, and 45% percent realizing these benefits in less than a year after implementation.
Here are some of the key insights and use cases from the survey:
- 83% of overall respondents agreed with the statement that “AI will revolutionize healthcare and life sciences in the next three to five years”
- 73% said AI is helping to reduce operational costs
- 58% cited data analytics as the top AI workload, with generative AI second at 54%, and large language models third at 53%
- 59% of respondents from pharmaceutical and biotech companies cited drug discovery and development among their top AI use cases
Business Impact of AI in Healthcare and Life Sciences
The healthcare and life sciences industry is seeing how AI can help increase annual revenue and reduce operational costs. Forty-one percent of respondents indicated that the acceleration of research and development has had a positive impact. Thirty-six percent of respondents said AI has helped create a competitive advantage. And 35% have said it’s helped reduce project cycles, deliver better clinical or research insights, and enhance precision and accuracy, respectively.
Given the positive results across a broad range of AI use cases, it comes as no surprise that 78% of respondents said they intend to increase their budget for AI infrastructure this year. In addition, more than a third of respondents noted their investments in AI will increase by more than 10%.
The survey also revealed the top three spending priorities: identifying additional AI use cases (47%), optimizing workflow and production cycles (34%) and hiring more AI experts (26%).
AI Applied Across Healthcare
Each industry segment in the survey had differing priorities in AI implementation. For instance, in the payers and providers industry segment, which includes health insurance companies, hospitals, clinical services and home healthcare, 48% of respondents said their top AI use case was administrative tasks and workflow optimization.
For the medical tools and technologies field, 71% of respondents said their top AI use case was medical imaging and diagnostics, such as using AI to analyze MRI or CAT scans. And for digital healthcare, 54% of respondents said their top use case was clinical decision support, while 54% from the pharmaceutical and biotech fields prioritized drug discovery and development.
AI use cases expected to have the most significant impact in healthcare and life sciences in the next five years include advanced medical imaging and diagnostics (51%), virtual healthcare assistants (34%) and precision medicine — treatment tailored to individual patient characteristics — (29%).
A Growing Dose of Generative AI
Overall, 54% of survey respondents said they’re using generative AI. Of these users, 63% said they’re actively using it, with another 36% assessing the technology through pilots or trials.
Digital healthcare was the leader in generative AI use, according to 71% of respondents from the field. Second was pharmaceutical and biotech at 69%, then medical technologies at 60%, and payers and providers at 44%.
Among all generative AI use cases, coding and document summarization — specific to clinical notes — was the top use case, at 55%. Medical chatbots and AI agents were second, at 53%, and literature analysis was third, at 45%. One notable exception was within the pharmaceutical biotech industry segment, in which respondents stated that drug discovery was the top generative AI use case, at 62%.
Download the “State of AI in Healthcare and Life Sciences: 2025 Trends” report for in-depth results and insights.
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