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시장보고서
상품코드
2088850
로봇 간호 도우미 시장 : 컴포넌트별, 모빌리티별, 자율 레벨별, 용도별, 최종 사용자별 시장 예측(2026-2032년)Robotic Nurse Assistant Market by Component, Mobility, Autonomy Level, Application, End User - Global Forecast 2026-2032 |
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360iResearch
로봇 간호 도우미 시장은 2032년까지 연평균 복합 성장률(CAGR) 15.06%로 성장이 전망되며, 38억 8,000만 달러 규모로 확대될 것으로 예측됩니다.
| 주요 시장 통계 | |
|---|---|
| 기준 연도 : 2025년 | 14억 5,000만 달러 |
| 추정 연도 : 2026년 | 16억 8,000만 달러 |
| 예측 연도 : 2032년 | 38억 8,000만 달러 |
| CAGR(%) | 15.06% |
로봇 간호 도우미 시장 도입
병원, 장기 요양 기관, 재활센터가 만성적인 인력 부족, 고령화, 그리고 중증도 높은 치료에 대한 대응에 직면한 가운데, 로봇 간호 도우미는 시범 프로젝트 단계에서 실용적인 도구로 전환되고 있습니다. 세계보건기구(WHO)는 2030년까지 전 세계적으로 의료 인력 부족 위험이 발생할 것이라고 경고하는 한편, 많은 고소득국의 의료 시스템에서는 간호사의 번아웃, 결원율, 그리고 병상 옆 지원에 대한 수요 증가에 대응하고 있습니다.
로봇 간호 분야의 혁신적인 변화
로봇 간호 도우미 분야는 단일 목적의 자동화에서 통합된 임상 워크플로우 지원으로 전환되고 있습니다. 병원에서는 전자의무기록, 간호사 호출 시스템, 약제 업무, 자산 추적, 엘리베이터, 출입 관리 시스템, 시설 물류와 연동되는 로봇을 우선적으로 도입하고 있습니다. 이는 교대당 소요 시간 단축, 수작업으로 인한 부상 감소, 업무 신뢰성 향상, 그리고 환자 처리 능력 향상을 통해 그 가치가 점점 더 높이 평가받고 있기 때문입니다.
인공지능(AI)이 미치는 누적 영향
인공지능(AI)은 자율 주행, 음성 대화, 예측적 작업 라우팅, 이상 감지, 컴퓨터 비전, 다국어를 통한 환자와의 대화, 적응형 워크플로우 조정을 가능하게 함으로써 로봇 간호 도우미의 영향을 더욱 증폭시키고 있습니다. AI를 통해 로봇은 고정된 경로에 얽매이지 않고, 혼잡한 복도, 엘리베이터 이용 현황, 병동의 우선순위 변동, 동적인 물자 수요 등 변화하는 병원 환경에 대응할 수 있게 됩니다.
지역별 주요 인사이트
북미는 선진적인 병원 인프라, 높은 인건비, 디지털 헬스 분야에 대한 적극적인 투자, 그리고 간호 인력 부족으로 인한 지속적인 압박으로 인해 로봇 간호 보조 솔루션의 조기 도입 지역으로 자리 잡고 있습니다. 미국에서는 급성기 의료 물류, 텔레프레즌스, 소독 지원, 환자 모니터링 분야에서 도입이 앞서고 있습니다. 한편, 캐나다에서는 광활한 영토와 고령화되는 인구를 배경으로 원격 진료 조정 및 병원 워크플로우 자동화와 같은 활용 사례가 추진되고 있습니다. 유럽에서는 공공 부문의 현대화, 엄격한 의료기기 규제, 노동력 고령화로 인한 압박 등의 요인이 호재로 작용하고 있으며, 유럽연합(EU)의 데이터 보호 및 AI 거버넌스 관련 요건이 안전하고 상호 운용 가능한 의료용 로봇의 조달 기준을 형성하고 있습니다.
주요 그룹 분석
싱가포르, 태국, 말레이시아, 인도네시아, 베트남, 필리핀의 병원 네트워크가 디지털 케어 체계를 확충하고 있어 아세안(ASEAN) 시장이 주목받고 있지만, 도입 상황은 보험 급여 제도, 인프라 성숙도, 임상 인력 배치 모델, 현지 기술 지원에 따라 달라집니다. GCC(걸프협력회의) 국가들은 사우디아라비아, 아랍에미리트, 카타르, 쿠웨이트, 바레인, 오만에서 진행 중인 국가적 의료 혁신 프로그램이 디지털 기술을 활용한 시설을 기반으로 한 스마트 병원, 자동화, 의료 관광, 그리고 고품질의 환자 경험을 중시하고 있어 높은 잠재력을 지닌 그룹으로 부상하고 있습니다.
주요 국가에 대한 인사이트
미국은 병원 자동화 예산, 인력 부족, 성숙한 디지털 인프라, 그리고 임상 정보 시스템의 광범위한 활용에 힘입어, 언급된 국가들 중 단기적으로 가장 큰 기회를 지니고 있습니다. 캐나다에서의 도입은 고령화, 원격의료 수요, 그리고 공공 자금을 통한 의료 시스템 생산성 향상에 대한 우선순위에 힘입어 추진되고 있습니다. 한편, 멕시코와 브라질은 민간 병원 체인, 도시 지역의 전문 의료 센터, 그리고 의료 관광 회랑을 통해 잠재력을 보여주고 있습니다. 영국은 의료 시스템의 생산성 향상과 인력 부족 완화에 주력하고 있으며, 독일, 프랑스, 이탈리아, 스페인에서는 고령화, 재활 수요, 병원 현대화, 그리고 커넥티드 의료 기술의 보다 안전한 조달을 뒷받침하는 유럽의 규제 조화가 추진력으로 작용하고 있습니다.
업계 리더를 위한 실천적 제안
업계 리더는 검체 이송, 약품 배달, 린넨 이동, 식사 배식, 소독 지원, 낙상 위험 모니터링, 텔레프레즌스를 통한 회진, 비품 보충 등 빈도가 높고 위험이 낮은 업무 흐름부터 착수해야 합니다. 이러한 이용 사례는 이동 시간을 단축하고, 업무의 신뢰성을 향상시키며, 피할 수 있는 직원의 노출을 제한하고, 간호사가 평가, 간호 계획 수립, 투약, 환자와의 소통에 집중할 수 있도록 함으로써 측정 가능한 업무적 가치를 창출할 수 있습니다.
조사 방법론
본 요약 보고서는 2차 조사, 업계 삼각 검증, 그리고 병원, 장기 요양, 재활, 재택 간호에 가까운 환경에서의 의료 자동화 동향 분석을 바탕으로 작성되었습니다. 정보 출처에는 세계보건기구(WHO), 각국의 노동 및 보건 기관, 의료기기 규제 당국, 데이터 보호 당국, 로봇 산업 단체, 동료 심사를 거친 문헌, 그리고 병원의 기술 도입 보고서 등에서 얻은 공개 데이터가 포함됩니다.
결론
로봇 간호 도우미는 인간 간호사를 대체하기보다는 의료 분야가 안고 있는 구조적 과제에 대한 실용적인 해결책으로 자리 잡고 있습니다. 그 가장 큰 가치는 반복적이고 육체적으로 혹독하며 물류 측면의 부담이 큰 업무를 자동화하는 한편, 환자 모니터링, 원격 존재감, 감염 예방 지원 및 임상 워크플로우 조정을 강화하는 데 있습니다.
자주 묻는 질문
목차
제1장 서문
제2장 조사 방법
제3장 주요 요약
제4장 시장 개요
제5장 시장 인사이트
제6장 AI의 누적 영향(2026년)
제7장 로봇 간호 도우미 시장 : 컴포넌트별
제8장 로봇 간호 도우미 시장 : 모빌리티별
제9장 로봇 간호 도우미 시장 : 자율 레벨별
제10장 로봇 간호 도우미 시장 : 용도별
제11장 로봇 간호 도우미 시장 : 최종 사용자별
제12장 로봇 간호 도우미 시장 : 지역별
제13장 로봇 간호 도우미 시장 : 그룹별
제14장 로봇 간호 도우미 시장 : 국가별
제15장 경쟁 구도
제16장 기업 개요
AJY 26.07.27The Robotic Nurse Assistant Market is projected to grow by USD 3.88 billion at a CAGR of 15.06% by 2032.
| KEY MARKET STATISTICS | |
|---|---|
| Base Year [2025] | USD 1.45 billion |
| Estimated Year [2026] | USD 1.68 billion |
| Forecast Year [2032] | USD 3.88 billion |
| CAGR (%) | 15.06% |
Robotic Nurse Assistant Market Introduction
Robotic nurse assistants are moving from pilot projects to operational tools as hospitals, long-term care providers, and rehabilitation centers confront persistent staffing pressure, aging populations, and higher acuity care. The World Health Organization has warned of a global health workforce shortfall risk by 2030, while many high-income health systems continue to manage nurse burnout, vacancy rates, and rising demand for bedside support.
The robotic nurse assistant landscape includes mobile service robots, autonomous transport systems, patient lifting and transfer robots, telepresence units, medication and supply delivery platforms, disinfection support robots, and AI-enabled monitoring assistants. Demand is strongest where robotic nurse assistant solutions can reduce non-clinical workload, improve response times, support infection prevention, lower manual handling risks, and extend clinical staff capacity without replacing licensed nursing judgment.
Transformative Shifts in Robotic Nursing
The robotic nurse assistant landscape is shifting from single-purpose automation toward integrated clinical workflow support. Hospitals are prioritizing robots that connect with electronic health records, nurse call systems, pharmacy workflows, asset tracking, elevators, access control systems, and facility logistics because value is increasingly measured by time saved per shift, fewer manual handling injuries, improved task reliability, and better patient throughput.
Another transformative shift is the movement from hardware procurement to robotics-as-a-service and managed service models. Subscription-based deployment can lower upfront capital pressure and allow providers to scale units across departments. At the same time, cybersecurity, interoperability, infection control, battery performance, fleet management, and human factors design have become central purchasing criteria as robotic systems operate closer to patients, protected health information, and regulated care environments.
Cumulative Impact of Artificial Intelligence
Artificial intelligence is compounding the impact of robotic nurse assistants by enabling autonomous navigation, voice interaction, predictive task routing, anomaly detection, computer vision, multilingual patient engagement, and adaptive workflow coordination. AI allows robots to move beyond fixed routes and respond to changing hospital environments, including crowded hallways, elevator use, shifting ward priorities, and dynamic supply demands.
The cumulative impact is operational rather than purely futuristic: AI can prioritize transport tasks, support remote patient observation, flag fall-risk behavior, improve uptime through predictive maintenance, and generate auditable workflow data for clinical and facilities teams. However, adoption depends on validated performance, explainable decision support, privacy safeguards, cybersecurity resilience, and clear clinical governance. In regulated healthcare settings, AI-enabled robotic nurse assistants must support nurses, document actions reliably, and maintain human oversight for all care-critical decisions.
Key Regional Insights
North America remains an early adopter of robotic nurse assistant solutions due to advanced hospital infrastructure, high labor costs, strong digital health investment, and sustained pressure from nursing workforce shortages. The United States leads deployments in acute care logistics, telepresence, disinfection support, and patient monitoring, while Canada's large geography and aging population support use cases in remote care coordination and hospital workflow automation. Europe benefits from public-sector modernization, strong medical device regulation, and workforce aging pressures, with the European Union's data protection and AI governance expectations shaping procurement standards for safe, interoperable healthcare robotics.
Asia-Pacific is a major innovation and adoption hub, led by Japan's super-aging demographics, South Korea's robotics capability and connectivity infrastructure, China's scale in automation and hospital modernization, India's expanding healthcare capacity, and Australia's need to serve geographically dispersed populations. Latin America is advancing selectively through private hospital networks and higher-acuity urban care centers in Brazil and Mexico, where automation is evaluated around productivity and premium care delivery. The Middle East, especially GCC health systems, is investing in smart hospitals, digital front doors, and automation-enabled patient experience, while Africa remains earlier-stage, with practical adoption focused on telepresence, logistics, training support, and workforce-extension models where connectivity, maintenance capacity, and procurement funding permit.
Key Group Insights
ASEAN markets are gaining attention as hospital networks in Singapore, Thailand, Malaysia, Indonesia, Vietnam, and the Philippines expand digital care capacity, although adoption varies by reimbursement, infrastructure maturity, clinical staffing models, and local technical support. The GCC is a high-potential group because national health transformation programs across Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Bahrain, and Oman emphasize smart hospitals, automation, medical tourism, and premium patient experience supported by digitally enabled facilities.
The European Union is influential because its medical device, data privacy, cybersecurity, and AI rules set global expectations for safe deployment of robotic nurse assistant technologies. BRICS countries combine large patient populations with industrial policy ambitions, particularly in China and India, while Brazil and South Africa highlight the role of private hospital systems and access-focused care models. G7 markets account for many advanced deployments because of capital availability, aging demographics, mature hospital IT, and regulatory clarity. NATO members' dual-use robotics expertise also indirectly supports innovation in autonomous navigation, operational resilience, cybersecurity, remote operations, and secure communications that can transfer into healthcare robotics environments.
Key Country Insights
The United States represents the strongest near-term opportunity among the listed countries, supported by hospital automation budgets, workforce shortages, mature digital infrastructure, and broad use of clinical information systems. Canada's adoption is supported by aging demographics, remote care requirements, and publicly funded health system productivity priorities, while Mexico and Brazil show potential through private hospital chains, urban specialty care centers, and medical tourism corridors. The United Kingdom is focused on health system productivity and workforce relief, and Germany, France, Italy, and Spain are driven by aging populations, rehabilitation needs, hospital modernization, and European regulatory harmonization that supports safer procurement of connected medical technologies.
Russia's market is constrained by procurement complexity, budget pressures, and sanctions-related technology access, although domestic automation needs remain across large hospital networks. China is scaling healthcare robotics through manufacturing strength, automation policy support, hospital modernization, and rapid digital health adoption. India is driven by hospital bed expansion, staffing gaps, and growing private healthcare investment, while Japan remains a pioneer because of age-related care demand, long-term care pressure, and deep robotics experience. Australia needs automation across geographically dispersed care systems and remote service models, and South Korea combines robotics manufacturing, advanced connectivity, smart hospital adoption, and strong engineering capacity to support robotic nurse assistant deployment.
Actionable Recommendations for Industry Leaders
Industry leaders should begin with high-frequency, lower-risk workflows such as specimen transport, medication delivery, linen movement, meal delivery, disinfection support, fall-risk monitoring, telepresence rounding, and supply replenishment. These use cases can produce measurable operational value because they reduce walking time, improve task reliability, limit avoidable staff exposure, and allow nurses to focus on assessment, care planning, medication administration, and patient communication.
Executives should require evidence-based pilots with baseline and post-deployment metrics, including nurse time saved, task completion rates, response time, safety incidents, patient satisfaction, staff acceptance, uptime, cybersecurity performance, integration performance, and total cost of ownership. Vendors should prioritize interoperability, service support, clinical change management, human-centered design, and co-design with nurses. Buyers should also establish governance for AI validation, privacy, escalation protocols, infection prevention, maintenance responsibility, and staff training before scaling robotic nurse assistant platforms across facilities.
Research Methodology
This executive summary is based on secondary research, industry triangulation, and analysis of healthcare automation trends across hospitals, long-term care, rehabilitation, and home-adjacent care settings. Inputs include publicly available data from organizations such as the World Health Organization, national labor and health agencies, medical device regulators, data protection authorities, robotics industry bodies, peer-reviewed literature, and hospital technology adoption reports.
The methodology prioritizes verified indicators over speculative forecasts: workforce shortages, population aging, hospital capacity, digital health maturity, regulatory readiness, procurement models, infrastructure preparedness, and use-case economics. Regional, group, and country insights were evaluated through comparative assessment of healthcare infrastructure, robotics capabilities, reimbursement environments, data governance, clinical workflow readiness, cybersecurity requirements, and readiness for AI-enabled healthcare robotics integration.
Conclusion
Robotic nurse assistants are becoming a practical response to structural healthcare challenges rather than a replacement for human nurses. Their strongest value lies in automating repetitive, physically demanding, and logistics-heavy tasks while strengthening patient monitoring, telepresence, infection prevention support, and clinical workflow coordination.
The next phase of adoption will favor solutions that are safe, interoperable, AI-enabled, cybersecure, and measurable in operational outcomes. Providers that align robotics deployment with nurse experience, patient safety, workforce resilience, and digital governance will be best positioned to capture productivity gains. Technology providers that prove reliability, integration, service quality, and clinical trust will shape the competitive future of the robotic nurse assistant market.
Table of Contents
1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Definition
- 1.3. Market Segmentation & Coverage
- 1.4. Years Considered for the Study
- 1.5. Currency Considered for the Study
- 1.6. Language Considered for the Study
- 1.7. Key Stakeholders
2. Research Methodology
- 2.1. Introduction
- 2.2. Research Design
- 2.2.1. Primary Research
- 2.2.2. Secondary Research
- 2.3. Research Framework
- 2.3.1. Qualitative Analysis
- 2.3.2. Quantitative Analysis
- 2.4. Market Size Estimation
- 2.4.1. Top-Down Approach
- 2.4.2. Bottom-Up Approach
- 2.5. Data Triangulation
- 2.6. Research Outcomes
- 2.7. Research Assumptions
- 2.8. Research Limitations
3. Executive Summary
- 3.1. Introduction
- 3.2. CXO Perspective
- 3.3. Market Size & Growth Trends
- 3.4. Market Share Analysis, 2025
- 3.5. FPNV Positioning Matrix, 2025
- 3.6. New Revenue Opportunities
- 3.7. Next-Generation Business Models
- 3.8. Industry Roadmap
4. Market Overview
- 4.1. Introduction
- 4.2. Industry Ecosystem & Value Chain Analysis
- 4.2.1. Supply-Side Analysis
- 4.2.2. Demand-Side Analysis
- 4.2.3. Stakeholder Analysis
- 4.3. Market Dynamics
- 4.3.1. Key Drivers
- 4.3.2. Key Restraints
- 4.3.3. Key Opportunities
- 4.3.4. Key Challenges
- 4.4. Porter's Five Forces Analysis
- 4.5. PESTLE Analysis
- 4.6. Market Outlook
- 4.6.1. Near-Term Market Outlook (0-2 Years)
- 4.6.2. Medium-Term Market Outlook (3-5 Years)
- 4.6.3. Long-Term Market Outlook (5-10 Years)
- 4.7. Go-to-Market Strategy
5. Market Insights
- 5.1. Consumer Insights & End-User Perspective
- 5.2. Consumer Experience Benchmarking
- 5.3. Opportunity Mapping
- 5.4. Distribution Channel Analysis
- 5.5. Pricing Trend Analysis
- 5.6. Regulatory Compliance & Standards Framework
- 5.7. ESG & Sustainability Analysis
- 5.8. Disruption & Risk Scenarios
- 5.9. Return on Investment & Cost-Benefit Analysis
6. Cumulative Impact of Artificial Intelligence 2026
7. Robotic Nurse Assistant Market, by Component
- 7.1. Hardware
- 7.1.1. Actuators
- 7.1.2. Sensors
- 7.1.3. Wearable Robotics
- 7.1.4. Stationary Robotic Workstations
- 7.2. Services
- 7.2.1. Installation
- 7.2.2. Maintenance
- 7.2.3. Training
- 7.3. Software
- 7.3.1. Clinical Workflow Integration Software
- 7.3.2. AI-based Decision Support Systems
- 7.3.3. Fleet Management Software
- 7.3.4. Remote Monitoring & Teleoperation Software
8. Robotic Nurse Assistant Market, by Mobility
- 8.1. Stationary
- 8.2. Tracked
- 8.3. Wheeled
9. Robotic Nurse Assistant Market, by Autonomy Level
- 9.1. Fully Autonomous
- 9.2. Semi-Autonomous
10. Robotic Nurse Assistant Market, by Application
- 10.1. Medication Management
- 10.1.1. Adherence Monitoring
- 10.1.2. Dispensing
- 10.2. Logistics
- 10.2.1. Supply Transport
- 10.2.2. Waste Disposal
- 10.3. Patient Monitoring
- 10.3.1. Fall Detection
- 10.3.2. Vital Signs Monitoring
- 10.4. Rehabilitation
- 10.4.1. Cognitive Therapy Assistance
- 10.4.2. Physical Therapy Assistance
- 10.5. Telepresence
- 10.5.1. Remote Consultation
- 10.5.2. Remote Surgery Assistance
11. Robotic Nurse Assistant Market, by End User
- 11.1. Clinics
- 11.2. Home Healthcare
- 11.3. Hospitals
- 11.4. Long-Term Care Facilities
12. Robotic Nurse Assistant Market, by Region
- 12.1. Asia-Pacific
- 12.2. North America
- 12.3. Latin America
- 12.4. Europe
- 12.5. Middle East
- 12.6. Africa
13. Robotic Nurse Assistant Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
14. Robotic Nurse Assistant Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
15. Competitive Landscape
- 15.1. Market Concentration Analysis, 2025
- 15.1.1. Concentration Ratio (CR)
- 15.1.2. Herfindahl Hirschman Index (HHI)
- 15.2. Recent Developments & Impact Analysis, 2025
- 15.3. Product Portfolio Analysis, 2025
- 15.4. Benchmarking Analysis, 2025
16. Company Profiles
- 16.1. ABB Ltd.
- 16.2. Aethon Inc.
- 16.3. CYBERDYNE Inc.
- 16.4. Diligent Robotics
- 16.5. Ekso Bionics Holdings, Inc.
- 16.6. F&P Robotics AG
- 16.7. Fraunhofer-Gesellschaft
- 16.8. Honda Motor Co., Ltd.
- 16.9. Hstar Technologies Corp.
- 16.10. Intuition Robotics Ltd.
- 16.11. KUKA AG
- 16.12. LG Electronics Inc.
- 16.13. PAL Robotics S.L.
- 16.14. Panasonic Holdings Corporation
- 16.15. Relay Robotics, Inc.
- 16.16. Richtech Robotics Inc.
- 16.17. Samsung Electronics Co., Ltd.
- 16.18. SoftBank Robotics Group Corp.
- 16.19. Toyota Motor Corporation
- 16.20. UBTECH Robotics Corp.
