From Radiology to Drug Discovery, Survey Reveals AI Is Delivering Clear Return on Investment in Healthcare

2026-02-24 · Source: NVIDIA Blog · Field: Health & Wellbeing — Healthcare Systems & Policy, Pharmaceuticals & Biotechnology, Medical Devices & Health Technology · Depth: Intermediate, quick

Summary

NVIDIA's second annual "State of AI in Healthcare and Life Sciences" survey report indicates a significant shift from AI experimentation to execution within the industry. The report, based on a survey, reveals that 70% of organizations are actively using AI, up from 63% in 2024, with 69% utilizing generative AI and large language models, an increase from 54%. Open-source software and models are deemed moderately to extremely important by 82% of respondents, and 47% are using or assessing agentic AI. Executives report strong ROI, with 85% citing increased revenue and 80% reduced costs. AI adoption is highest in digital healthcare (78%) and medical technology (74%), with top use cases including clinical decision support, medical imaging, and workflow optimization. Consequently, 85% of organizations plan to increase their AI budgets this year, with 46% expecting an increase of over 10%.

Key takeaway

For CTOs and VPs of Engineering evaluating AI strategy in healthcare, this report underscores the critical need to move beyond pilots to integrated, workflow-embedded AI solutions. Your organization should prioritize funding for AI evaluation to ensure measurable improvements in safety, quality, and patient care, while strategically leveraging open-source models for discovery and proprietary systems for clinical deployment where accountability is paramount.

Key insights

Healthcare and life sciences are rapidly moving from AI experimentation to execution, driven by strong ROI and open-source adoption.

Principles

• Embed AI into existing workflows for impact.
• Prioritize evaluation for measurable AI improvements.
• Open models drive discovery; proprietary systems ensure clinical safety.

In practice

• Focus AI on clinical and operational problems.
• Utilize open-source for domain-specific applications.
• Fund AI evaluation as a core operational function.

Topics

• AI in Healthcare
• Generative AI Adoption
• AI ROI
• Open-Source AI
• Agentic AI

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Executive, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Blog.