헬스케어 및 생명과학 분야 자연어처리(NLP) 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the NLP in healthcare and life science market size is expected to increase from USD 5.67 billion in 2025 to USD 7.13 billion in 2026 and reach USD 24.80 billion by 2031, growing at a CAGR of 28.33% over 2026-2031.

This report is Segmented by Offering, Deployment Mode (Cloud-Based, On-Premises, Hybrid), NLP Type (NLU, NLG), NLP Technique (NER, OCR, Sentiment Analysis, Text Classification, Topic Modeling, and More), Application, End User, Organization Size (Large, SMEs), and Geography (North America, Europe, Asia-Pacific, MEA, South America). The Market Forecasts are Provided in Terms of Value (USD).

Global NLP In Healthcare and Life Science Market Trends and Insights

Rising Unstructured Clinical and Research Text Volumes

The core growth engine is no longer basic EHR digitization because that transition had already matured in many developed markets before 2026. What is driving demand now is the rising volume of unstructured content coming from ambient documentation tools, telehealth transcripts, remote monitoring logs, and AI-assisted clinical notes. Amazon Connect Health entered the market in 2026 with ambient documentation support across more than 22 specialties, which shows how quickly new text streams are moving into production care workflows. Netsmart also reported a 275% increase in ambient documentation adoption across its network of more than 1,300 client organizations after deployment, which points to a much larger base of machine-generated notes entering provider systems. That combination keeps the NLP in healthcare and life science market on a long demand cycle because buyers now need extraction pipelines for diagnoses, medications, and findings from both traditional clinician notes and newly generated documentation streams.

Accelerating Demand for Automated Clinical Documentation and Scribing

Documentation burden remains one of the clearest commercial entry points for clinical NLP. A 2026 systematic review and meta-analysis found that AI tools, including NLP and large language models, reduce documentation burden when supported by practical quality control. Microsoft stated in 2026 that Dragon Copilot was being used by more than 100,000 clinicians each day across 9 countries and that it could capture multilingual conversations in 58 languages and convert them into structured notes. Oracle also reported that its Clinical AI Agent had saved U.S. doctors more than 200,000 documentation hours, and AtlantiCare achieved a 41% reduction in documentation time in ambulatory care after deployment. As this use case scales, the NLP in healthcare and life science market is shifting from simple transcription value toward code suggestion, diagnostic support, and risk workflows that create a deeper platform relationship with providers.

Model Hallucination and Clinical Liability Concerns

Hallucination remains the most visible barrier for generative clinical NLP in high-stakes settings. A 2026 study in npj Digital Medicine found that large language models gave unsafe responses to patient medical questions at rates that still require strong human oversight before they can sit inside routine workflows. A 2026 review in Frontiers in Digital Health reached a similar conclusion and noted that even medically tuned models can behave unsafely in specific clinical contexts. This matters because medication reconciliation, diagnostic support, and summary generation all depend on factual precision rather than fluent output. For that reason, the NLP in healthcare and life science market is rewarding platforms with audit trails, source grounding, and review controls, while buyers remain cautious toward thin wrappers around general-purpose models.

Other drivers and restraints analyzed in the detailed report include:

1. Clinical Trial Matching and Real-World Evidence Extraction at Scale
2. GenAI-Enabled Medical Coding and Summary Generation
3. Data Privacy and Sovereignty Constraints

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

Segment Analysis

Solutions held 65.12% of the NLP in healthcare and life science market share in 2025, which reflected the installed base of software used for documentation, coding, and analytics across large integrated delivery systems. That lead came from established software contracts and the central role of packaged NLP tools inside provider workflows. Clinical documentation, coding support, and analytics remain the core software use cases that support this segment's scale. The installed base still matters because hospitals tend to prefer proven systems when text extraction touches reimbursement, compliance, and care workflows.

Services are projected to grow at 29.67% CAGR through 2031 as buyers ask for deployment support, EHR integration, model tuning, and ongoing governance rather than only a software license. Health systems increasingly want vendors to adapt models to proprietary clinical corpora and maintain them after go-live. John Snow Labs said in 2026 that its Healthcare NLP platform includes more than 2,800 pre-trained models and regularly updated pipelines tied to changing ontologies and use cases. That type of service-heavy relationship increases recurring revenue and raises switching costs once a system is embedded into clinical and life sciences workflows. The NLP in healthcare and life science market is therefore shifting toward end-to-end accountability, which puts pressure on software-only vendors that cannot support customization, retraining, and governance over time.

Cloud-based deployment accounted for 61.82% of the 2025 market, supported by large hyperscaler investments and the practical ease of scaling model training and inference through shared infrastructure. Microsoft Azure, AWS HealthLake, and Google Cloud helped shape this lead by making healthcare-focused AI tooling easier to deploy inside enterprise environments. Cloud also suits organizations that want faster implementation and lower upfront infrastructure costs. That remains especially relevant for broad provider networks and multisite life sciences programs that need centralized model management.

Hybrid deployment is forecast to advance at 30.82% CAGR through 2031 because many health systems want cloud flexibility without moving identifiable data outside approved environments. The pressure is strongest in Europe, Japan, and Gulf markets where sovereignty and localization policies limit how patient data can be transmitted or stored. The 2026 Scientific Reports paper on Japanese medical PHI extraction showed that optimized local models can approach cloud-level performance, which lowers the penalty of keeping sensitive workloads on site. On-premises systems still retain a role in military health networks and large institutions with older infrastructure, but their share is likely to decline as hybrid models offer a more practical middle path. The NLP in healthcare and life science market is therefore moving toward mixed architectures where sensitive inference stays local and broader orchestration or model management sits in the cloud.

Natural Language Understanding held 60.14% share in 2025, which kept it in the leading position because most mature healthcare NLP workflows still depend on the extraction, classification, and interpretation of existing text. NLU remains central to clinical concept extraction, named entity recognition, and assertion detection inside EHR-linked systems. Those functions support diagnosis capture, medication extraction, adverse event review, and structured documentation. This gives NLU a broad installed role across both provider and research settings.

Natural Language Generation is projected to grow at 31.91% CAGR through 2031 as generative models become standard for drafting discharge summaries, patient communications, and clinical notes. Microsoft reported in 2026 that Dragon Copilot could turn patient-clinician conversations into structured EHR notes in 58 languages, which illustrates the commercial pull behind generation-led tools. Buyers now evaluate generated content on fluency, factual accuracy, and alignment with existing EHR templates, not only on traditional precision metrics. That changes procurement criteria because a generated summary must fit directly into the care workflow and stand up to review. The NLP in healthcare and life science market is rewarding vendors that can deliver generation with strong clinical grounding, while general-purpose models without healthcare-specific controls face a harder path into enterprise care environments.

Complete Report Scope:

• By Offering
  • Solutions
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premises
  • Hybrid
• By NLP Type
  • Natural Language Understanding
  • Natural Language Generation
• By NLP Technique
  • Named Entity Recognition
  • Optical Character Recognition
  • Sentiment Analysis
  • Text Classification
  • Topic Modeling
  • Text Summarization
  • Predictive Risk Analytics
• By Application
  • Clinical Operations and Decision Support
  • Patient Care and Engagement
  • Biomedical Research and Drug Development
  • Administrative and Operations Management
  • Genomics and Precision Medicine
  • Clinical Trial Matching
  • Medical Education and Knowledge Dissemination
  • Risk and Compliance Management
• By End User
  • Healthcare Providers
  • Healthcare Payers
  • Pharmaceutical and Biotechnology Companies
  • Healthcare Researchers
  • Public Health and Government Agencies
  • Medical Device Companies
• By Organization Size
  • Large Enterprises
  • Small and Midsize Enterprises
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East & Africa
    • GCC
    • South Africa
    • Rest of Middle East & Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America accounted for 43.23% of the 2025 market, which kept it in the leading regional position for healthcare NLP adoption. The United States remains the center of demand because procurement is supported by deep EHR penetration, large provider networks, and broad vendor activity across provider, payer, and life sciences use cases. Microsoft and Oracle both expanded their healthcare AI offerings in 2026, which reinforced the region's role as the main commercial proving ground for enterprise clinical NLP. AWS also added support in 2026 for the CMS Interoperability and Prior Authorization Final Rule inside HealthLake, which gives U.S. payers and connected vendors a direct compliance-driven use case for NLP-enabled authorization workflows. The NLP in healthcare and life science market remains most mature in North America because infrastructure readiness, reimbursement pressure, and vendor presence all align more clearly there than in most other regions.

Europe continues to advance under a stricter compliance model that shapes both deployment timing and vendor positioning. GDPR Article 9 rules and the EU AI Act's high-risk obligations for clinical AI require stronger evidence around oversight, governance, and documentation before large deployments can scale. Germany and the United Kingdom remain the main national demand centers, while Nordic systems stand out as strong environments for governance-led clinical AI programs because of high digitization and stronger institutional trust. The NLP in healthcare and life science market in Europe is therefore moving forward with a more measured pace, as interoperability gaps and regulatory diligence slow near-term rollout even while they strengthen the long-run quality bar for approved solutions.

Asia-Pacific is projected to grow at 32.53% CAGR through 2031, making it the fastest-expanding regional cluster in this space. Growth is being supported by large patient populations, clinician shortages, stronger digital health investment, and the need to process healthcare content across multiple languages and fragmented care settings. Japan is emerging as an important case because local deployment is gaining technical credibility and because RIKEN published a Japanese medical LLM in May 2026 that achieved 90.8% accuracy on specialist licensing benchmarks in hospital-oriented environments. That kind of local model development fits sovereignty-driven procurement patterns and makes deployment more realistic where institutions prefer on-premises or tightly controlled environments. Middle East and Africa remains an earlier-stage opportunity led by Gulf initiatives, while South America is still concentrated in private provider networks in countries such as Brazil and Argentina. The NLP in healthcare and life science market in these regions is still smaller than in North America or Europe, but local-language requirements and public system modernization continue to create a longer runway for adoption.

1. 3M
2. Amazon Web Services, Inc.
3. Averbis GmbH
4. Cerner
5. Clinithink Ltd.
6. CORTI AI ApS
7. Deep 6 AI, Inc.
8. Dolbey Systems, Inc.
9. Edifecs, Inc.
10. Epic Systems
11. Google LLC
12. IBM Corporation (International Business Machines Corporation)
13. Inovalon Holdings, Inc.
14. IQVIA
15. John Snow Labs Inc.
16. Lexalytics, Inc.
17. Microsoft
18. Notable Health, Inc.
19. Nuance Communications, Inc.
20. Optum
21. Oracle
22. SAS Institute
23. Suki AI, Inc.
24. Syapse, Inc.
25. Tempus AI, Inc.
26. Xerox Holdings Corporation

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 Rising Unstructured Clinical and Research Text Volumes
  • 4.2.2 Accelerating Demand for Automated Clinical Documentation and Scribing
  • 4.2.3 Clinical Trial Matching and Real-World Evidence Extraction at Scale
  • 4.2.4 GenAI-Enabled Medical Coding and Summary Generation
  • 4.2.5 AI Governance, Auditability, and Traceability Requirements
  • 4.2.6 Multilingual Healthcare Content Processing Across Fragmented Care Settings
• 4.3 Market Restraints
  • 4.3.1 Interoperability Gaps With Legacy EHR and Claims Stacks
  • 4.3.2 Limited Domain-Labeled Training Data for Specialty Medicine
  • 4.3.3 Model Hallucination and Clinical Liability Concerns
  • 4.3.4 Data Privacy and Sovereignty Constraints
• 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 Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 Market Size & Growth Forecasts

• 5.1 By Offering
  • 5.1.1 Solutions
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premises
  • 5.2.3 Hybrid
• 5.3 By NLP Type
  • 5.3.1 Natural Language Understanding
  • 5.3.2 Natural Language Generation
• 5.4 By NLP Technique
  • 5.4.1 Named Entity Recognition
  • 5.4.2 Optical Character Recognition
  • 5.4.3 Sentiment Analysis
  • 5.4.4 Text Classification
  • 5.4.5 Topic Modeling
  • 5.4.6 Text Summarization
  • 5.4.7 Predictive Risk Analytics
• 5.5 By Application
  • 5.5.1 Clinical Operations and Decision Support
  • 5.5.2 Patient Care and Engagement
  • 5.5.3 Biomedical Research and Drug Development
  • 5.5.4 Administrative and Operations Management
  • 5.5.5 Genomics and Precision Medicine
  • 5.5.6 Clinical Trial Matching
  • 5.5.7 Medical Education and Knowledge Dissemination
  • 5.5.8 Risk and Compliance Management
• 5.6 By End User
  • 5.6.1 Healthcare Providers
  • 5.6.2 Healthcare Payers
  • 5.6.3 Pharmaceutical and Biotechnology Companies
  • 5.6.4 Healthcare Researchers
  • 5.6.5 Public Health and Government Agencies
  • 5.6.6 Medical Device Companies
• 5.7 By Organization Size
  • 5.7.1 Large Enterprises
  • 5.7.2 Small and Midsize Enterprises
• 5.8 By Geography
  • 5.8.1 North America
    • 5.8.1.1 United States
    • 5.8.1.2 Canada
    • 5.8.1.3 Mexico
  • 5.8.2 Europe
    • 5.8.2.1 Germany
    • 5.8.2.2 United Kingdom
    • 5.8.2.3 France
    • 5.8.2.4 Italy
    • 5.8.2.5 Spain
    • 5.8.2.6 Rest of Europe
  • 5.8.3 Asia-Pacific
    • 5.8.3.1 China
    • 5.8.3.2 Japan
    • 5.8.3.3 India
    • 5.8.3.4 Australia
    • 5.8.3.5 South Korea
    • 5.8.3.6 Rest of Asia-Pacific
  • 5.8.4 Middle East & Africa
    • 5.8.4.1 GCC
    • 5.8.4.2 South Africa
    • 5.8.4.3 Rest of Middle East & Africa
  • 5.8.5 South America
    • 5.8.5.1 Brazil
    • 5.8.5.2 Argentina
    • 5.8.5.3 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, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 3M
  • 6.3.2 Amazon Web Services, Inc.
  • 6.3.3 Averbis GmbH
  • 6.3.4 Cerner Corporation
  • 6.3.5 Clinithink Ltd.
  • 6.3.6 CORTI AI ApS
  • 6.3.7 Deep 6 AI, Inc.
  • 6.3.8 Dolbey Systems, Inc.
  • 6.3.9 Edifecs, Inc.
  • 6.3.10 Epic Systems Corporation
  • 6.3.11 Google LLC
  • 6.3.12 IBM Corporation (International Business Machines Corporation)
  • 6.3.13 Inovalon Holdings, Inc.
  • 6.3.14 IQVIA Holdings Inc.
  • 6.3.15 John Snow Labs Inc.
  • 6.3.16 Lexalytics, Inc.
  • 6.3.17 Microsoft Corporation
  • 6.3.18 Notable Health, Inc.
  • 6.3.19 Nuance Communications, Inc.
  • 6.3.20 Optum, Inc.
  • 6.3.21 Oracle Corporation
  • 6.3.22 SAS Institute Inc.
  • 6.3.23 Suki AI, Inc.
  • 6.3.24 Syapse, Inc.
  • 6.3.25 Tempus AI, Inc.
  • 6.3.26 Xerox Holdings Corporation

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment