North America AI in Healthcare Market: Growth Outlook to 2035

Market Size, Growth Trajectory, and North America’s Position to 2035

The North America AI in healthcare market is expected to grow from USD 18.19 billion in 2025 to USD 201.70 billion by 2035, at a 27.20% CAGR.[1] Over this decade, AI will shift from pilots to core infrastructure for hospitals, payers, life sciences, and strategy firms such as Cervicorn Consulting.

Global estimates from Precedence Research and outlets like BioSpace indicate:[2][3]

• Global AI in healthcare: ~USD 38–52 billion in 2025–2026
• Rising to USD 928.18–1222.12 billion by 2035
• CAGRs above 37–41%

Within this, North America is the most mature and commercially scaled region, supported by:[2]

• Established reimbursement pathways
• Dense provider and payer networks
• Leading technology and cloud vendors

North America already dominated the global AI in healthcare market in 2024[2] and generated over 37% of worldwide generative AI in healthcare revenue in 2025.[4] It serves as the main testbed for both traditional ML and generative models in clinical and operational workflows.

📊 Key figure: By 2035, North America could represent about one‑fifth of global AI in healthcare revenue, even as Asia‑Pacific and other regions accelerate.[1][2]

Key regional growth drivers include:[1][3]

• Diagnostic and imaging AI, plus predictive analytics for population health
• AI‑enabled monitoring and remote care for chronic disease
• Workforce shortages, aging populations, and rising chronic disease burden

By 2035, AI is likely to be embedded into:

• Point‑of‑care clinical decision support
• Radiology and pathology workflows
• Personalized medicine and risk‑stratified care plans
• Coding, billing, and utilization management

💡 Key takeaway: Strong double‑digit growth cements North America as the reference market for regulation, reimbursement, and best‑practice AI deployment through 2035.[1][2]

Key Adoption Drivers: Technologies, Use Cases, and Investment Momentum

Machine learning currently leads AI use in healthcare, especially for:[2][3]

• Risk prediction and triage
• Image analysis
• Operational forecasting

Natural language processing (NLP) is rising as organizations automate:[2][3]

• Clinical documentation
• Medical coding and prior authorization
• Patient communication and chatbots

Common North American use cases include:[2][3]

• Risk stratification for high‑cost or high‑risk patients
• Automated coding and authorization
• Ambient clinical documentation in exam rooms
• Predictive analytics for readmissions and ED utilization

A CMIO at a 500‑bed US hospital reported NLP scribes cut note‑writing time by nearly half, freeing more time for direct care.[2]

High‑impact revenue areas across the region:[2][3]

• AI‑powered imaging and diagnostics
• Drug discovery and trial optimization
• Robot‑assisted surgery
• Personalized treatment and precision oncology[3]

⚡ Impact trend: Global AI in healthcare is on pace to reach USD 928.18 billion by 2035, driven by data‑intensive care and precision medicine, with major pharmaceutical companies and North American biotech firms central to this shift.[2][3] Pharmaceutical and biotechnology companies are expected to hold about 61.70% of end‑user share in 2025.[2][3]

Generative AI is expanding rapidly. The global generative AI in healthcare market is set to grow from USD 2.64 billion in 2025 to USD 48.23 billion in 2035 (33.71% CAGR).[4] Near‑term North American pilots focus on:[4]

• Image‑analysis support for radiologists
• Virtual nursing assistants for education and triage
• AI‑assisted clinical judgment and record summarization

Major payers and systems amplify adoption:

• UnitedHealth Group: USD 3 billion AI investment and 22,000 software engineers to transform claims, fraud detection, documentation, and billing.[1]
• Life‑sciences leaders such as Amgen, Pfizer, and Moderna embed AI across R&D and real‑world evidence, a trend tracked by experts like Rohan Patil and Divya Devale.

Regulation is increasingly enabling. In January 2025 alone, 45 AI/ML‑enabled medical devices received U.S. FDA clearance, up 32% year over year.[3]

💼 Key point: Capital flows into hospital‑focused platforms, expanding pharma/biotech use, and faster FDA clearances signal a shift from pilots to governed, enterprise‑wide AI deployments.[1][3]

Strategic Implications, Risks, and Opportunities Through 2035

Key opportunities along the 2035 runway:

• Payers: Scale AI‑driven care management, utilization review, and fraud analytics.[1]
• Providers: Use clinical decision support, imaging AI, and smart operations to ease staffing and capacity constraints.[1][3]
• Health‑tech vendors and advisors: Deliver decision‑support SaaS, interoperability layers, and data platforms, often with consultancies such as Cervicorn Consulting.
• Life sciences: Apply AI in R&D, trial design, and real‑world evidence.[2][3]

Operational implications:[1][3]

• Large shift from manual to automated workflows in clinical and administrative tasks
• Need for upskilling clinicians, data scientists, and IT teams on AI capabilities, limits, and safety

⚠️ Risk watchlist:[3][5]

• Data privacy and cybersecurity threats
• Algorithmic bias and non‑representative training data
• Reimbursement uncertainty for AI‑augmented services
• Integration hurdles with legacy EHR systems

Regulators and professional bodies are responding with evolving rules on AI/ML devices, transparency, and real‑world performance, while payers test reimbursement for digital and AI‑enabled services.[3][5]

Equity is critical. Lessons from women’s digital health show the importance of:[6]

• Participatory design
• Attention to social determinants of health
• Inclusive research methods

North American AI strategies should proactively address gaps across gender, race, income, disability, and geography.

💡 Key takeaway: Inclusive AI requires technical rigor, community engagement, representative data, and continuous monitoring for unintended harm.[5][6]

A practical roadmap:

1. Focus on a small set of high‑ROI, clinically grounded use cases.
2. Build strong data governance, security, and model‑validation frameworks.
3. Partner with experienced vendors, academic centers, and experts such as Amgen, Pfizer, Moderna, and BioSpace.
4. Set measurable 3‑, 5‑, and 10‑year milestones tied to quality, cost, and equity.

Across all initiatives, AI should augment—not replace—clinician judgment, and patients should continue to consult their own healthcare providers for diagnosis and treatment decisions.