임상 케어 분야 AI : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in clinical care market size is expected to increase from USD 10.17 billion in 2025 to USD 12.44 billion in 2026 and reach USD 34.11 billion by 2031, growing at a CAGR of 22.35% over 2026-2031.

This report is Segmented by Component (Software and Services), Deployment Mode (Cloud-Based and On-Premise), Primary AI Modality (Machine Learning, Natural Language Processing, and More), Application (Medical Diagnosis and More), End User (Hospitals & Clinics, Research & Academic Institutes, and More), and Geography (North America, Europe, Asia-Pacific and More). The Market Forecasts are Provided in Value (USD).

Global AI In Clinical Care Market Trends and Insights

Expansion of EHR-Embedded AI Clinical Decision Support

The AI in clinical care market is benefiting from the shift toward tools that work inside the clinician's normal EHR workflow rather than outside it. A 2026 study in Scientific Reports described AIDx as a locally deployable system that retrieves patient context from the EHR and produces structured recommendations inside the user's native environment. That matters because it reduces switching between screens, which has long limited the real use of clinical software even when technical performance was strong. A pragmatic cluster-randomized trial published in Nature Medicine also showed that generative AI-enabled decision support in primary care improved guideline adherence, which supports broader rollout in routine care settings. Technology is no longer the main barrier in many health systems, while governance, training, and policy awareness have become the harder part of scaling adoption, with only 27% of clinicians aware of a formal organizational AI policy in 2026.

Burnout Relief Via Ambient Clinical Documentation

The AI in clinical care market is also being lifted by the strong commercial case for ambient documentation. A multicenter quality improvement study across 6 U.S. health systems found that 30 days of ambient AI scribe use reduced clinician burnout from 51.9% to 38.8%, while also improving cognitive task load and after-hours documentation time. Mass General Brigham expanded its program to more than 3,000 providers by April 2025 and reported a 21.2% absolute reduction in burnout at 84 days, which gave health systems a concrete deployment model. The commercial model is also widening because Abridge said in June 2026 that its platform connected documentation, payer workflows, and evidence-based treatment and was deployed across more than 300 health systems supporting over 100 million annual clinical conversations. This moves the value proposition from narrow scribing into enterprise clinical intelligence, which supports larger contracts and deeper system integration.

Data Privacy, Cybersecurity, and AI Governance Burden

The AI in clinical care market still faces a heavy trust burden because protected health data sits at the center of every deployment. Healthcare data breaches reached a record 772 large incidents listed on the HHS OCR breach portal in 2025, which kept cybersecurity risk high for hospital buyers. Vendors that build HIPAA-ready architectures with audit trails, role-based access, and continuous monitoring hold an advantage in regulated environments, but those controls also raise product cost and lengthen procurement cycles. Formal governance on paper does not fully solve the issue because clinician awareness of organizational AI policy remained only 27% in 2026. This gap between enterprise controls and frontline behavior can slow deployment even when the underlying technology is ready.

Other drivers and restraints analyzed in the detailed report include:

1. Need to Cut Diagnostic Errors and Care Variation
2. Value-Based Care and Precision-Treatment Economics
3. Workflow Integration Friction and Alert-Fatigue Risk

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software held 72.34% of revenue in 2025, which made it the largest part of the AI in clinical care market size by component. The scale advantage is clear because one cloud update can reach many hospital users at once, while subscription pricing fits annual provider budgeting better than large one-time purchases. This has made software the fastest route for hospitals that want to adopt clinical AI without a major infrastructure change. In the AI in clinical care industry, software also benefits from shorter release cycles that let vendors improve models and interfaces continuously.

Services are projected to grow at 23.56% CAGR through 2031, which makes them the fastest-growing component. Buyers are moving toward managed AI relationships that include retraining, integration, auditing, and governance support because deployment now extends far beyond initial licensing. The American Medical Association said in 2026 that more than 75% of physicians saw AI as offering a patient-care advantage, up from 65% in 2023, which helps internal champions support these longer service engagements. This shift suggests that many health systems now want accountability for outcomes and model performance, not only access to software. In practical terms, services are becoming the layer that helps vendors defend margins as the AI in clinical care market matures.

Cloud-based systems commanded 68.43% of 2025 revenue and represented the largest share of the AI in clinical care market by deployment mode. Their lead reflects lower upfront capital needs, faster implementation, and easier support for distributed care settings. Cloud delivery also fits the product logic of continuous updates, centralized monitoring, and multi-site health system rollout. For many buyers, that mix of speed and cost control keeps cloud as the default procurement path in the AI in clinical care market.

On-premise deployment is forecast to grow at 24.91% CAGR through 2031, which makes it the fastest-moving model despite its smaller base. The driver is not volume alone, but the tighter control it offers over data perimeters, oversight, and compliance workflows in sensitive clinical environments. This is why some providers accept a premium for on-premise deployment when governance demands are high or infrastructure rules are strict. On-premise systems, therefore, hold a strategically important place even though cloud still dominates the AI in clinical care market share in current revenue terms. Vendors that can deliver similar functionality across both models are better positioned to win hybrid health system contracts.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premise
• By Primary AI Modality
  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Generative AI
• By Application
  • Medical Diagnosis
  • Treatment Planning and Personalization
  • Patient Monitoring and Early Warning
  • Alerts, Reminders, and Risk Prediction
  • Others
• By End User
  • Hospitals & Clinics
  • Research & Academic Institutes
  • Pharmaceutical & Biotechnology Companies
  • Others
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 39.47% of 2025 revenue, which gave it the largest regional position in the AI in clinical care market. The region leads because it has a dense installed base of EHR-connected hospital systems, a mature regulatory path for clinical AI, and early movement on reimbursement. As of January 2026, 26 CPT codes existed for clinical AI solutions, including 3 Category I codes for tools that assess cardiovascular risk and diabetic retinopathy. That coding base helps reduce provider uncertainty because it gives hospitals a clearer route to payment and adoption. Coverage expansion also matters, and HeartFlow said in January 2026 that its Plaque Analysis tool had gained Aetna coverage across all lines of business, extending access through another major national insurer.

Asia-Pacific is the fastest-growing region with a projected 27.94% CAGR through 2031, which shows how quickly the AI in clinical care market is widening outside its most mature bases. The region's growth reflects strong digital health investment, rising openness to AI-enabled care delivery, and a practical need to stretch clinical capacity. Adoption also benefits from the fact that many systems in the region are building new digital layers now rather than modifying older legacy environments. This creates room for AI vendors that can localize clinical workflows and meet regional policy expectations. The result is that Asia-Pacific is becoming one of the most important expansion corridors in the AI in clinical care market.

Europe remains a significant revenue contributor to the AI in clinical care market size because Germany, the United Kingdom, and France continue to anchor procurement and validation activity. Germany's KHZG hospital digitalization program has directed EUR 4.3 billion into hospital IT modernization, which supports a real pipeline for clinical AI buying. The United Kingdom's NHS AI Lab continues to back imaging and pathology deployments, which gives vendors a structured entry path into public system use. Beyond Europe, the Middle East and Africa region is expanding from a smaller base through sovereign digital health investment, while South America is building gradual momentum through digital health modernization and interest in value-based care models.

1. Abridge AI, Inc.
2. Aidoc Medical Ltd.
3. Athenahealth
4. Bayesian Health, Inc.
5. Epic Systems
6. GE HealthCare Technologies Inc.
7. HeartFlow, Inc.
8. iSchemaView, Inc. (RapidAI)
9. Koninklijke Philips
10. Logical Images, Inc. (VisualDx)
11. Medical Information Technology, Inc. (MEDITECH)
12. Merative, L.P.
13. Microsoft
14. Oracle
15. PathAI, Inc.
16. Qure.AI Technologies Pvt. Ltd.
17. Siemens Healthineers
18. Tempus AI, Inc.
19. Veradigm
20. Viz.AI, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Expansion of EHR-embedded AI Clinical Decision Support
  • 4.2.2 Burnout Relief via Ambient Clinical Documentation
  • 4.2.3 Need to Cut Diagnostic Errors and Care Variation
  • 4.2.4 Value-based Care and Precision-treatment Economics
  • 4.2.5 Deployment of FDA-cleared Imaging and Triage AI in Frontline Care
  • 4.2.6 Multilingual Specialty-tuned Clinical AI for Underserved Settings
• 4.3 Market Restraints
  • 4.3.1 Data Privacy, Cybersecurity, and AI Governance Burden
  • 4.3.2 Workflow Integration Friction and Alert-fatigue Risk
  • 4.3.3 Payer Downcoding and Reimbursement Recalibration of AI-Enabled Documentation
  • 4.3.4 Model Drift, Monitoring Gaps, and liability exposure
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Bargaining Power of Suppliers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premise
• 5.3 By Primary AI Modality
  • 5.3.1 Machine Learning
  • 5.3.2 Natural Language Processing
  • 5.3.3 Computer Vision
  • 5.3.4 Generative AI
• 5.4 By Application
  • 5.4.1 Medical Diagnosis
  • 5.4.2 Treatment Planning and Personalization
  • 5.4.3 Patient Monitoring and Early Warning
  • 5.4.4 Alerts, Reminders, and Risk Prediction
  • 5.4.5 Others
• 5.5 By End User
  • 5.5.1 Hospitals & Clinics
  • 5.5.2 Research & Academic Institutes
  • 5.5.3 Pharmaceutical & Biotechnology Companies
  • 5.5.4 Others
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 India
    • 5.6.3.3 Japan
    • 5.6.3.4 Australia
    • 5.6.3.5 South Korea
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East and Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East and Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as Available, Strategic Information, Market Rank/Share for Key Companies, Products & Services, and Recent Developments)
  • 6.3.1 Abridge AI, Inc.
  • 6.3.2 Aidoc Medical Ltd.
  • 6.3.3 athenahealth, Inc.
  • 6.3.4 Bayesian Health, Inc.
  • 6.3.5 Epic Systems Corporation
  • 6.3.6 GE HealthCare Technologies Inc.
  • 6.3.7 HeartFlow, Inc.
  • 6.3.8 iSchemaView, Inc. (RapidAI)
  • 6.3.9 Koninklijke Philips N.V.
  • 6.3.10 Logical Images, Inc. (VisualDx)
  • 6.3.11 Medical Information Technology, Inc. (MEDITECH)
  • 6.3.12 Merative, L.P.
  • 6.3.13 Microsoft Corporation
  • 6.3.14 Oracle Corporation
  • 6.3.15 PathAI, Inc.
  • 6.3.16 Qure.ai Technologies Pvt. Ltd.
  • 6.3.17 Siemens Healthineers AG
  • 6.3.18 Tempus AI, Inc.
  • 6.3.19 Veradigm LLC
  • 6.3.20 Viz.ai, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment