For years, the healthcare industry has had the reputation of a digital laggard. It has been slow to adopt new tech, often trailing behind other industries.
But according to Menlo Ventures’ State of AI in Healthcare Report, that narrative is officially outdated.
In fact, healthcare is now outpacing most of the economy when it comes to deploying AI.
Healthcare is adopting AI faster than you think
Menlo Ventures’ report shows that healthcare organisations are currently adopting AI 2.2 times faster than the broader economy. While only 9% of companies in other sectors have implemented AI, the health sector is rapidly moving past pilot projects and into real-world production deployments.
What’s even more striking is that healthcare went from roughly 3% adoption just two years ago to becoming one of the most active arenas for AI today.
22% of healthcare organizations are deploying domain-specific AI solutions, representing a 7-fold increase in a year and a 10-fold increase since 2023.
While the adoption rate varies across sectors, the momentum is universal. The pilot phase is officially over.
- The health system leads with a 27% adoption rate.
- Outpatient facilities follow at 18%.
- Insurers lag at 14%.
AI spending in healthcare is exploding
The dollars are following the uptake. According to the report:
- AI spending in healthcare has reached $1.4 billion in 2025.
- The figure has tripled since last year alone.
- The $4.9 trillion healthcare industry now accounts for a significant portion of America’s AI power.
This surge in investment is due to the ability of AI to address real operational problems that have become unbearable under the traditional process.
Administrative pain points drive the biggest spend
Menlo’s report highlights that the biggest AI investments are solving today’s operational headaches rather than pure clinical outcomes. These include:
- Ambient clinical documentation is the standout category, attracting $600 million in spending. These AI tools tackle clinician burnout by automatically capturing and documenting patient notes during visits. This allows doctors to look their patients in the eye rather than staring at a computer screen for documentation.
- Coding and billing automation: A $450 million investment aimed at recovering lost revenue and reducing human error.
- Patient engagement: This sector saw “explosive” growth up to 20 times year-on-year.
- Prior authorization: Tools that automate complex insurance approvals saw 10x growth.
Providers are racing while payers wait
The report also uncovered a clear split in how organizations approach AI procurement:
Providers (especially health systems) have shortened their buying cycles by up to 22%. It is a race to deploy AI for immediate return on investment to solve chronic workforce shortages and protect thin margins.
On the other hand, payers and some life sciences groups are moving more cautiously, extending procurement cycles and often still piloting solutions rather than deploying.
This gap reflects differing risk calculations. While providers see AI as a survival tool, payers fear that AI-driven tools, especially in claims and authorisations, may drive up costs. As they make it easier for providers to justify more expensive treatments.
Startups are dominating the AI surge
The report highlights startups as the biggest winners of AI in healthcare. Currently, startups capture 85% of all AI spending in healthcare, far outpacing legacy incumbents and established giants.
And the main reason for this is that AI-native startups are agile. They:
- Don’t carry legacy technology debt,
- Can iterate quickly,
- And can often build for a single high-value use case (deep domain expertise)
That agility has allowed them to win trust and contracts faster than established competitors.
Pharma and life sciences are building their own path
The pharmaceutical industry has a different strategy for using AI. Instead of buying AI tools, the pharma and biotech companies are building their own proprietary AI models, leveraging their internal data as the most valuable asset.
By using AI to ingest their vast internal datasets, they can design experiments and identify new drug targets. It can also help measure the drug development timelines.
Key uses of AI in the sector include:
- Propriety model building: Fine-tuning LLMs on internal chemical and biological data.
- Experiment design: Using AI to predict which compounds are most likely to succeed in trials.
- Clinical trial recruitment: Accelerating the process of finding the right patient for specific studies.
The untapped AI opportunity
Despite all this momentum, Menlo’s report notes that roughly 80% of the healthcare AI market remains untapped. Particularly in administrative services that historically weren’t part of tech budgets.
Looking ahead, AI will not just be about automation or data analysis; the sector is moving towards an agentic AI future. Where AI agents can think and act autonomously, performing multi-step tasks simultaneously.
In an agentic workflow, an AI agent could:
- Audit records: Automatically check for compliance across thousands of patient files.
- Manage logistics: Coordinate patient referrals, transport, and follow-up care without human intervention.
- Integrate data: Act as a bridge between siloed point solutions to create a cohesive patient history.
What once felt like a slow-moving industry has become one of the most dynamic arenas for AI adoption. According to Menlo Ventures’ research, healthcare is setting the pace by solving real problems and reinventing itself with AI and that’s already delivering value.
-By Dr Rohini Devi and the AHT Team
