AI 간호 보조 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI-based nursing assistant market is expected to grow from USD 1.06 billion in 2025 to USD 1.21 billion in 2026 and is forecasted to reach USD 2.48 billion by 2031 at 15.38% CAGR over 2026-2031.

This report is Segmented by Component (Software, Services), Deployment Mode (Cloud-Based, On-Premises, Hybrid), AI Capability (Natural Language Processing, and Others), Application (Medication Support, and Escalation, and Others), End-User (Hospitals and Clinics, and Others) and Geography (North America, Europe, Asia-Pacific, and Others). The Market Forecasts are Provided in Terms of Value (USD).

Global AI-Based Nursing Assistant Market Trends and Insights

Rising Nursing Shortages and Care Delivery Bottlenecks

The AI-based nursing assistant market continues to draw its strongest support from a labor gap that remains structurally difficult to close across both advanced and emerging healthcare systems. The World Health Organization reported a global nursing shortage of 5.8 million in 2023 and projected only a partial improvement to 4.1 million by 2030, with the burden still concentrated in regions that are also trying to expand care access and hospital capacity. The National Council of State Boards of Nursing also found that more than 138,000 nurses left the workforce since 2022 and that nearly 40% of the remaining workforce intend to leave by 2029, which reinforces the pressure on hospitals to find alternatives that extend staff capacity without waiting for labor supply to normalize. In this setting, the AI-based nursing assistant market is being evaluated less as a convenience layer and more as a source of usable nursing time inside each shift. That is why providers are paying close attention to tools that can absorb education, discharge, and intake tasks that do not always need direct manual repetition from bedside staff.

Hospital Interest in Workflow Automation and Time Savings

The AI-based nursing assistant market is also being pushed forward by hospital demand to recover time now lost to charting, handovers, and repetitive administrative work. Microsoft stated in October 2025 that nurses can spend 25% to 41% of their shift on documentation and administrative tasks, which has made workflow improvement central to purchasing decisions rather than a side benefit. This pressure is changing procurement in the AI-based nursing assistant market from isolated pilots toward platform decisions tied to the broader EHR environment. Dragon Copilot for nurses, launched with Epic Rover integration, showed how ambient AI is moving into the core clinical stack rather than staying in a standalone application category. A 2026 JMIR study across three hospitals in Taiwan found that an LLM-based handover documentation system saved 474 to 981 nursing hours per month, which gave finance and operations teams a direct way to compare AI deployment with agency staffing and overtime costs. As more of these gains are proven inside live hospital settings, the AI-based nursing assistant market is likely to favor vendors that can show measurable time recovery at enterprise scale instead of only offering narrow feature improvements.

Data Privacy and Clinical Governance Concerns

The AI-based nursing assistant market faces a slower rollout path when solutions handle protected health information through ambient listening, chart summarization, or patient-facing voice interaction. These tools sit close to real clinical documentation and real patient communication, which raises the standard for auditability, governance, and accountability inside the procurement process. The American Nurses Association stated in 2025 that the ethical use of AI in nursing requires transparency, oversight, and an ongoing approach to governance because opaque systems are difficult to evaluate and supervise in practice. This slows the AI-based nursing assistant market because product value alone is not enough when hospitals also need clear policies for data handling, human review, and clinical accountability. It also creates a wider separation between vendors that treat compliance as a core product design principle and those that try to address governance after deployment. As regulation of clinical AI becomes more defined, buyers are likely to place greater weight on documentation controls, audit support, and traceability before scaling any nursing AI program.

Other drivers and restraints analyzed in the detailed report include:

1. Need for Continuous Patient Monitoring and Escalation
2. Expansion of Home-Based and Remote Care Models
3. Integration Friction with EHR and Nursing Workflows

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

Segment Analysis

Software held 57.47% of AI-based nursing assistant market share in 2025, while services are projected to grow at a 15.59% CAGR through 2031. Software leads the AI-based nursing assistant market because documentation platforms, predictive analytics engines, and decision-support modules are the products that sit closest to daily nursing work. This lead also reflects the fact that many providers want capabilities that can plug into existing EHR workflows instead of adding a separate operating layer for staff to manage. The AI-based nursing assistant market is, therefore, still anchored by software value capture, especially where hospitals want immediate impact on charting time, handovers, and clinical coordination.

Providers are moving from one-time software procurement toward implementation support, change management, staff training, workflow redesign, and model tuning that stay active after go-live. This makes sense because nursing AI usually changes routines, documentation habits, and supervision structures rather than simply adding a new digital feature. As a result, service revenue is rising not because software is weakening, but because the AI-based nursing assistant market is moving from pilot activity into operational deployment that needs ongoing support.

Cloud deployment held 48.38% of revenue in 2025, which keeps it at the center of the AI-based nursing assistant market because cloud systems support continuous model updates, lower upfront infrastructure demands, and easier scaling across multi-site provider networks. Cloud also fits well with health systems that have already moved large portions of their clinical data stack into modern hosted environments. This has made cloud the default starting point for many vendors competing in the AI-based nursing assistant market. At the same time, on-premises deployment still matters in systems with tighter data residency expectations or stricter internal control preferences. That ongoing relevance prevents the market from settling into a single infrastructure model.

Hybrid deployment is forecasted to grow at a 15.76% CAGR through 2031, which shows that speed and security needs are pushing the AI-based nursing assistant market toward blended architecture rather than pure cloud dependence. The reason is practical, because ambient listening, fall detection, and vital-sign alerting lose value if latency delays a clinically meaningful response. That gives edge processing and local inference a larger role in workflows that are time-sensitive and data-sensitive at the same time. That is why the AI-based nursing assistant market is likely to keep cloud in the lead while allowing hybrid models to gain faster traction where clinical responsiveness and privacy controls must be managed together.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premises
  • Hybrid
• By AI Capability
  • Natural Language Processing
  • Predictive Analytics
  • Computer Vision
  • Speech Recognition
  • Other AI Capabilities
• By Application
  • Medication Support
  • Patient Monitoring and Escalation
  • Virtual Patient Interaction
  • Falls and Safety Management
  • Documentation Assistance
• By End-User
  • Hospitals and Clinics
  • Long-Term Care Facilities
  • Home Healthcare Providers
  • Ambulatory Care Centers
  • Rehabilitation Centers
• 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 and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 43.41% of AI-based nursing assistant market share in 2025, while Asia-Pacific is forecasted to grow at an 18.43% CAGR through 2031. North America leads the AI-based nursing assistant market because it combines acute nurse staffing pressure with mature EHR ecosystems and a provider base willing to operationalize AI inside standard workflows. The United States remains the center of this regional position, supported by documented workforce strain and faster movement from evaluation into active deployment. Microsoft's work with Mercy hospitals on Dragon Copilot and Suki's October 2025 nursing consortium with health systems on Epic, MEDITECH, and Oracle Health showed that the AI-based nursing assistant market in North America is moving toward enterprise commitment rather than isolated trials. Canada and Mexico are also participating in the AI-based nursing assistant market, though adoption remains more measured because governance and EHR fragmentation create a slower path to scale.

Europe remains the second-largest regional cluster in the AI-based nursing assistant market, with activity concentrated in markets such as Germany, the United Kingdom, and France. The region's progress is tied to digital infrastructure improvement and a gradual move from experimentation toward structured clinical AI strategy. NHS Grampian's partnership with Corti in 2025 also showed that the AI-based nursing assistant market is gaining traction in public health settings that have traditionally moved more cautiously on commercial AI tools.

Asia-Pacific is the fastest-growing region in the AI-based nursing assistant market because hospital digitization is accelerating across China, Japan, South Korea, and India. The region is attractive because providers are expanding digital infrastructure while also facing staffing strain, aging populations, and pressure to improve clinical throughput. The Middle East and Africa remain earlier in development, with Gulf markets showing the clearest institutional budget support while other areas still face infrastructure gaps. South America is also earlier in the AI-based nursing assistant market, with demand centered in major urban providers and private hospital groups as EHR modernization gradually improves the base for wider deployment.

1. Abridge AI, Inc.
2. Amelia US LLC
3. Baxter
4. Cerner
5. DeepScribe Inc.
6. Epic Systems
7. GE Healthcare
8. HealthTap, Inc.
9. Hippocratic AI, Inc.
10. IBM
11. Infermedica Sp. z o.o.
12. Koninklijke Philips
13. Microsoft
14. Moving Analytics GmbH
15. Oracle
16. Sensely, Inc.
17. Siemens Healthineers
18. Suki AI, Inc.
19. Teladoc Health

Additional Benefits:

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

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions and 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 Nursing Shortages and Care Delivery Bottlenecks
  • 4.2.2 Need for Continuous Patient Monitoring and Escalation
  • 4.2.3 Hospital Interest in Workflow Automation and Time Savings
  • 4.2.4 Expansion of Home-Based and Remote Care Models
  • 4.2.5 Liability-Reduction Through Standardized Digital Triage
  • 4.2.6 Under-Documented Demand from Night-Shift Coverage Gaps
• 4.3 Market Restraints
  • 4.3.1 Data Privacy and Clinical Governance Concerns
  • 4.3.2 Integration Friction with EHR and Nursing Workflows
  • 4.3.3 Reimbursement Uncertainty for AI-Augmented Nursing Services
  • 4.3.4 Limited Acceptance in High-Acuity and Safety-Critical Use Cases
• 4.4 Supply/Value 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 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-Premises
  • 5.2.3 Hybrid
• 5.3 By AI Capability
  • 5.3.1 Natural Language Processing
  • 5.3.2 Predictive Analytics
  • 5.3.3 Computer Vision
  • 5.3.4 Speech Recognition
  • 5.3.5 Other AI Capabilities
• 5.4 By Application
  • 5.4.1 Medication Support
  • 5.4.2 Patient Monitoring and Escalation
  • 5.4.3 Virtual Patient Interaction
  • 5.4.4 Falls and Safety Management
  • 5.4.5 Documentation Assistance
• 5.5 By End-User
  • 5.5.1 Hospitals and Clinics
  • 5.5.2 Long-Term Care Facilities
  • 5.5.3 Home Healthcare Providers
  • 5.5.4 Ambulatory Care Centers
  • 5.5.5 Rehabilitation Centers
• 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 Japan
    • 5.6.3.3 India
    • 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, Recent Developments)
  • 6.3.1 Abridge AI, Inc.
  • 6.3.2 Amelia US LLC
  • 6.3.3 Baxter International Inc.
  • 6.3.4 Cerner Corporation
  • 6.3.5 DeepScribe Inc.
  • 6.3.6 Epic Systems Corporation
  • 6.3.7 GE HealthCare
  • 6.3.8 HealthTap, Inc.
  • 6.3.9 Hippocratic AI, Inc.
  • 6.3.10 IBM
  • 6.3.11 Infermedica Sp. z o.o.
  • 6.3.12 Koninklijke Philips N.V.
  • 6.3.13 Microsoft Corporation
  • 6.3.14 Moving Analytics GmbH
  • 6.3.15 Oracle
  • 6.3.16 Sensely, Inc.
  • 6.3.17 Siemens Healthineers AG
  • 6.3.18 Suki AI, Inc.
  • 6.3.19 Teladoc Health, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment