AI Is Paying Off in Healthcare, Survey Finds: From Imaging to Drug Discovery

From Radiology to Drug Discovery: AI Is Delivering Clear ROI in Healthcare

AI has moved past proofs of concept and into day-to-day operations. NVIDIA's second annual State of AI in Healthcare and Life Sciences survey shows organizations getting real returns across imaging, drug discovery, administrative workflows and even new treatment planning with digital twins.

The headline is simple: adoption is up, budgets are rising and leaders are prioritizing use cases that hit revenue, cost and clinician efficiency.

Key Findings at a Glance

• 70% of organizations are actively using AI (up from 63%).
• 69% are using generative AI and LLMs (up from 54%).
• 82% say open source software and models are important to strategy.
• 47% are using or assessing agentic AI.
• 85% of executives report AI is increasing revenue; 80% say it's reducing costs.

Where Adoption Is Rising

Every segment is accelerating. Digital health leads at 78%, with medical technology at 74%. Generative AI tops the workload list, followed by data analytics and predictive analytics. Agentic AI entered the rankings quickly, with 47% using or evaluating it.

"Over the next 12-18 months, the most visible and scalable impact of AI will come from logistics and administrative streamlining," said John Nosta, president of NostaLab. "That's where adoption curves are already steep - scheduling, documentation, coding, utilization management and care coordination."

What's Working Right Now

• Medical imaging: 61% of medtech respondents use AI here - think faster reads and flagged findings to support radiologists.
• Drug discovery: 57% of pharma/biotech respondents report AI driving discovery and development workflows.
• Top cross-industry use cases: clinical decision support, medical imaging and workflow optimization.

"Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself," said Dr. Annabelle Painter, clinical AI strategy lead at Visiba U.K. "The organizations seeing impact are those that embed AI into existing workflows instead of layering AI on top as a separate tool."

ROI Is Clear - And Budgets Are Following

AI is lifting core metrics: revenue up, costs down and back-office productivity rising. The payoffs are specific and measurable by segment.

• Medtech: 57% report ROI from AI in medical imaging.
• Pharma/biotech: 46% cite drug discovery and development as a top ROI area.
• Digital health: Virtual assistants and chatbots lead ROI for 37%.
• Payers/providers: 39% see top ROI from administrative tasks and workflow optimization.

Budgets reflect the momentum: 85% plan to increase AI spend this year; 12% will hold steady. Nearly half (46%) expect increases above 10%.

As Painter noted, "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."

Open Source, Agentic AI and the Path to Deployment

Open source is now a strategic lever for domain-specific builds: 82% rate it important to their AI strategy. Teams are using open models for exploration and prototyping, then hardening deployments as use cases move closer to patient care.

"Open models will shape the intellectual field," said Nosta. "But in clinical environments where safety, liability and accountability are nonnegotiable, 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."

Agentic AI is gaining traction for knowledge retrieval and research paper analysis. Combined with digital twins, organizations are testing new treatment planning methods that can compress cycle time from idea to intervention.

What Healthcare Leaders Should Do Next

• Pick high-ROI, low-friction use cases first. Imaging triage, prior auth automation, coding, care coordination and patient messaging are proven starting points.
• Embed AI into existing workflows. Avoid "yet another app." Integrate into the EHR, RIS, LIS, PACS and RCM tools your teams already use.
• Fund evaluation as a product discipline. Define safety, quality and equity metrics up front. Track model drift, false positives/negatives and clinician overrides.
• Establish data and MLOps foundations. Guardrails for PHI, audit trails, versioning and human-in-the-loop review are non-negotiable.
• Use open source for exploration; harden for deployment. Pilot with open models, then validate and govern for production in clinical settings.
• Upskill cross-functional teams. Clinicians, QA, compliance, data engineers and operations should share one backlog and success metrics.
• Plan the budget like a flywheel. Reinvest early ROI from admin and imaging wins into more complex clinical and R&D programs.

Role-Specific Moves

• Hospitals and providers: Target documentation, coding, utilization management and care coordination to free clinician time and reduce denials.
• Payers: Automate intake, prior auth, claims triage and fraud detection with clear escalation paths to human reviewers.
• Medtech: Focus on imaging assist, device quality analytics and post-market surveillance with strong validation and vigilance.
• Pharma/biotech: Scale AI across target ID, molecule design, trial matching and literature review with secure data pipelines.

Resources

For practical training and implementation ideas, explore AI for Healthcare and, for revenue-cycle teams, the AI Learning Path for Medical Billers.

For deeper data and segment breakdowns, look for the State of AI in Healthcare and Life Sciences: 2026 Trends report.