의료 정보 시스템용 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in healthcare information systems market size is projected to be USD 9.94 billion in 2025, USD 12.48 billion in 2026, and reach USD 38.89 billion by 2031, growing at a CAGR of 25.53% from 2026 to 2031.

This report is Segmented by Component (Software, Hardware, and Services), Deployment (Cloud-Based, On-Premise, and Hybrid), Technology (Machine Learning, Natural Language Processing, and Other), Application (Clinical Intelligence, and Other), End User (Hospitals & Health Systems, and Other), and Geography (North America, Europe, and Other). The Market Forecasts are Provided in Value (USD).

Global AI In Healthcare Information Systems Market Trends and Insights

Explosion of Multimodal Clinical and Operational Data

The AI in healthcare information systems market is being pushed by a much broader data mix that now includes imaging, genomics, pathology, wearable signals, and voice-based documentation instead of only claims and lab values. A multimodal temporal foundation model trained on 7.2 million patients across 28 medical modalities showed the ability to predict new disease onset up to 5 years in advance across 95 clinical tasks. That result matters because clinical AI quality improves when platforms can connect long-time series records with several data formats inside one workflow. The CLIMB benchmark presented at ICML 2025 showed that multitask pre-training on multimodal datasets improved ultrasound AI performance by 29% and ECG analysis by 23% over single-modality training. Tempus AI reported more than 450 petabytes of multimodal healthcare data and more than 45 million patient records, showing how data scale is turning into a platform advantage for companies that serve both clinical and pharmaceutical users. As a result, the AI in the healthcare information systems market increasingly rewards vendors that can aggregate, normalize, and reuse data across many care settings.

AI-Led Clinical and Administrative Cost Compression

The AI in healthcare information systems market is also gaining support from a clearer financial case for reducing documentation load and repetitive administrative work. Epic stated in February 2026 that early users of AI Charting saved up to 60 minutes per physician per day and reduced after-hours documentation by 26%. Northwell Health deployed Abridge across 28 hospitals and 1,000 outpatient facilities in October 2025 and cited published data pointing to projected clinician burnout reductions of up to 67%. Abridge then extended ambient documentation into real-time order generation for labs, imaging, referrals, and medications, showing how a documentation tool can move into direct workflow execution. athenahealth said in February 2026 that AI systems handling unstructured fax and scanned documents can reduce manual review time and improve claim quality at enterprise scale. This pattern is lifting the AI in healthcare information systems market because savings from documentation, coding quality, and intake workflows can be redirected into broader clinical and operational deployment.

Privacy, Cybersecurity, and AI Compliance Burden

Privacy and security rules are slowing parts of the AI in healthcare information systems market because regulated health data requires tighter governance than many general enterprise AI deployments. The proposed HIPAA Security Rule update published on January 6, 2025, explicitly brings AI tools into the compliance scope for risk analysis, technology asset inventories, and vendor safeguard verification. Covered entities would need to identify AI software that touches ePHI and maintain written verification from vendors on technical safeguard deployment. That raises the cost of adoption for provider organizations that want rapid deployment but still need evidence, documentation, and continuous oversight. The burden falls hardest on mid-size and rural systems because they often lack dedicated AI governance teams and cybersecurity staffing. These requirements do not stop growth in the AI in healthcare information systems market, but they lengthen procurement cycles and narrow the field of vendors that can pass enterprise review.

Other drivers and restraints analyzed in the detailed report include:

1. Interoperability, FHIR APIs, and Cloud-Native Data Foundations
2. Ambient Documentation Becoming the Entry Wedge for Enterprise AI
3. Legacy Integration and Data Normalization Complexity

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

Segment Analysis

Software held 58.64% share of the AI in healthcare information systems market size in 2025, making it the largest component segment. That lead reflects deep AI feature integration within EHRs and the rapid spread of standalone tools for documentation, clinical review, coding, and administrative workflows. Epic said in February 2026 that more than 175 generative AI use cases were either released or in active development, which shows how software vendors are embedding multiple functions into core platforms instead of selling only isolated tools. The same update said Penny, its revenue cycle AI, was being used by more than 200 organizations and had helped some users reduce coding-related claim denials by more than 20%. Software vendors also benefit from strong renewal economics because AI functions can be bundled into broader information system contracts across the AI in healthcare information systems market.

Services are projected to grow at 26.32% CAGR through 2031, the fastest pace among component segments. That trajectory reflects rising demand for implementation, integration, governance, workflow redesign, training, and post-deployment monitoring as buyers move from tool selection to enterprise execution. The service requirement becomes larger when providers need data mapping, model oversight, and user adoption support across many specialties and care sites. In the AI in healthcare information systems industry, service intensity rises further when organizations are working across several EHR instances, shared service centers, and hybrid deployment models. Over time, the gap between software and services should narrow because new rollouts increasingly require both packaged applications and hands-on operational support.

Cloud-based deployment held 48.29% share of the AI in healthcare information systems market size in 2025, giving it the largest position among deployment models. Cloud leads because ambient documentation, large-scale analytics, and payer-provider API exchange all require elastic compute and low-friction scaling. CMS interoperability requirements are reinforcing that pattern by pushing the sector toward standardized digital data exchange across impacted plans and provider workflows. The cloud model also fits the procurement logic of health systems that want faster upgrades, centralized monitoring, and multi-site rollout without large local infrastructure refreshes. For many organizations in the AI in healthcare information systems market, cloud is now the default route for new AI functions unless governance rules point in another direction.

On-premise deployment is projected to grow at 27.51% CAGR through 2031, the fastest pace among deployment options. That growth reflects rising attention to data residency, infrastructure control, and governance around sensitive clinical workloads. CMS guidance on AI use in federal infrastructure has sharpened attention on model provenance, operating environment, and oversight, which supports demand for tightly governed deployment architectures. Hybrid models remain important for academic centers and complex providers that keep sensitive clinical data local while using cloud resources for selected administrative or research workloads. Across the AI in healthcare information systems market, deployment choice is becoming a governance decision as much as a pure technology decision.

Complete Report Scope:

• By Component
  • Software
  • Hardware
  • Services
• By Deployment
  • Cloud-Based
  • On-Premise
  • Hybrid
• By Technology
  • Machine Learning
  • Natural Language Processing
  • Context-Aware Computing
  • Generative AI
  • Others
• By Application
  • Clinical Intelligence
  • Administrative and Financial Intelligence
  • Longitudinal Data and Interoperability Intelligence
  • Research and Commercial Intelligence
  • Others
• By End User
  • Hospitals & Health Systems
  • Pharmaceutical & Biotechnology Companies
  • Healthcare Payers
  • 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 35.73% of the AI in healthcare information systems market share in 2025, making it the largest regional segment. The region benefits from a dense installed base of EHR platforms, a strong concentration of established vendors, and an active policy environment around interoperability and prior authorization. CMS is pushing impacted payers toward FHIR-based APIs and electronic prior authorization workflows, with major milestones continuing through 2027. That regulatory activity creates direct demand for workflow automation, payer connectivity, and data-layer modernization across the AI in healthcare information systems market. The United States remains the regional center of adoption because many leading EHR, ambient documentation, and clinical AI vendors scale first inside provider networks that already have large digital footprints.

Europe represented the second-largest regional position in 2025, supported by large public health systems and rising policy attention to responsible AI deployment. A European Commission study published in March 2026 found that 94% of EU providers were using or planning to adopt AI and projected strong uptake for clinical decision support systems by 2029. The UK 10-Year Health Plan, published in July 2025, identified a digital shift as 1 of 3 core pillars, which supports future enterprise procurement for software, interoperability, and workflow modernization. Europe also benefits from a stronger collaborative data culture in digital health, particularly where cross-system data use and standards-based modernization are already in motion. This creates a region that combines strong demand with tighter governance expectations, which can slow procurement but favor vendors that support open standards and auditable deployment.

Asia-Pacific is projected to grow at 29.81% CAGR through 2031, the fastest regional pace in the AI in healthcare information systems market. Growth in the region is tied to large-scale digital health buildouts, provider capacity pressure, and a shift from small pilots to operational use as national infrastructure matures. The Middle East, Africa, and South America remain early in adoption. Yet, both regions are gaining ground as public programs and private hospital groups look for automation in documentation, care coordination, and revenue workflows. This regional mix means vendors that can adapt deployment, governance, and pricing models to local infrastructure conditions should have the broadest expansion runway.

1. Abridge AI, Inc.
2. Aidoc Medical Ltd.
3. Amazon Web Services, Inc.
4. Athenahealth
5. Cognizant
6. Epic Systems
7. GE HealthCare Technologies Inc.
8. Google LLC
9. IBM
10. Innovaccer
11. IQVIA
12. Koninklijke Philips
13. Meditech
14. Microsoft
15. NVIDIA
16. Oracle
17. Qventus, Inc.
18. Siemens Healthineers
19. Tempus AI, Inc.
20. Veradigm
21. Viz.AI, Inc.

Additional Benefits:

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

TABLE OF CONTENTS

1 AI in Healthcare Information Systems Market

2 Introduction

• 2.1 Study Assumptions & Market Definition
• 2.2 Scope of the Study

3 Research Methodology

4 Executive Summary

5 Market Landscape

• 5.1 Market Overview
• 5.2 Market Drivers
  • 5.2.1 Explosion of Multimodal Clinical and Operational Data
  • 5.2.2 AI-Led Clinical and Administrative Cost Compression
  • 5.2.3 Interoperability, FHIR Apis, and Cloud-Native Data Foundations
  • 5.2.4 Prior Authorization and Clinical Attachment Automation Mandates
  • 5.2.5 Ambient Documentation Becoming the Entry Wedge for Enterprise AI
  • 5.2.6 Clinical decision support demand for real-time personalization and risk prediction
• 5.3 Market Restraints
  • 5.3.1 Privacy, Cybersecurity, and AI Compliance Burden
  • 5.3.2 Legacy Integration and Data Normalization Complexity
  • 5.3.3 EHR Bundling and Workflow Access Lock-In
  • 5.3.4 Evidence-Traceability and Reimbursement Audit Risk
• 5.4 Value / Supply-Chain Analysis
• 5.5 Regulatory Landscape
• 5.6 Technological Outlook
• 5.7 Porter's Five Forces Analysis
  • 5.7.1 Threat of New Entrants
  • 5.7.2 Bargaining Power of Buyers
  • 5.7.3 Bargaining Power of Suppliers
  • 5.7.4 Threat of Substitutes
  • 5.7.5 Competitive Rivalry

6 Market Size & Growth Forecasts (Value, USD)

• 6.1 By Component
  • 6.1.1 Software
  • 6.1.2 Hardware
  • 6.1.3 Services
• 6.2 By Deployment
  • 6.2.1 Cloud-Based
  • 6.2.2 On-Premise
  • 6.2.3 Hybrid
• 6.3 By Technology
  • 6.3.1 Machine Learning
  • 6.3.2 Natural Language Processing
  • 6.3.3 Context-Aware Computing
  • 6.3.4 Generative AI
  • 6.3.5 Others
• 6.4 By Application
  • 6.4.1 Clinical Intelligence
  • 6.4.2 Administrative and Financial Intelligence
  • 6.4.3 Longitudinal Data and Interoperability Intelligence
  • 6.4.4 Research and Commercial Intelligence
  • 6.4.5 Others
• 6.5 By End User
  • 6.5.1 Hospitals & Health Systems
  • 6.5.2 Pharmaceutical & Biotechnology Companies
  • 6.5.3 Healthcare Payers
  • 6.5.4 Others
• 6.6 By Geography
  • 6.6.1 North America
    • 6.6.1.1 United States
    • 6.6.1.2 Canada
    • 6.6.1.3 Mexico
  • 6.6.2 Europe
    • 6.6.2.1 Germany
    • 6.6.2.2 United Kingdom
    • 6.6.2.3 France
    • 6.6.2.4 Italy
    • 6.6.2.5 Spain
    • 6.6.2.6 Rest of Europe
  • 6.6.3 Asia-Pacific
    • 6.6.3.1 China
    • 6.6.3.2 India
    • 6.6.3.3 Japan
    • 6.6.3.4 Australia
    • 6.6.3.5 South Korea
    • 6.6.3.6 Rest of Asia-Pacific
  • 6.6.4 Middle East and Africa
    • 6.6.4.1 GCC
    • 6.6.4.2 South Africa
    • 6.6.4.3 Rest of Middle East and Africa
  • 6.6.5 South America
    • 6.6.5.1 Brazil
    • 6.6.5.2 Argentina
    • 6.6.5.3 Rest of South America

7 Competitive Landscape

• 7.1 Market Concentration
• 7.2 Market Share Analysis
• 7.3 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)
  • 7.3.1 Abridge AI, Inc.
  • 7.3.2 Aidoc Medical Ltd.
  • 7.3.3 Amazon Web Services, Inc.
  • 7.3.4 athenahealth, Inc.
  • 7.3.5 Cognizant Technology Solutions Corporation
  • 7.3.6 Epic Systems Corporation
  • 7.3.7 GE HealthCare Technologies Inc.
  • 7.3.8 Google LLC
  • 7.3.9 IBM Corporation
  • 7.3.10 Innovaccer Inc.
  • 7.3.11 IQVIA Holdings Inc.
  • 7.3.12 Koninklijke Philips N.V.
  • 7.3.13 MEDITECH
  • 7.3.14 Microsoft Corporation
  • 7.3.15 NVIDIA Corporation
  • 7.3.16 Oracle Corporation
  • 7.3.17 Qventus, Inc.
  • 7.3.18 Siemens Healthineers AG
  • 7.3.19 Tempus AI, Inc.
  • 7.3.20 Veradigm LLC
  • 7.3.21 Viz.ai, Inc.

8 Market Opportunities & Future Outlook

• 8.1 White-space & Unmet-need Assessment