당뇨병 관리용 인공지능(AI) 시장 : 디바이스 유형, 기술, 구성 요소, 도입 형태, 최종 사용자별 - 세계 시장 예측(2026-2032년)

The Artificial Intelligence in Diabetes Management Market is projected to grow by USD 9.04 billion at a CAGR of 31.66% by 2032.

Artificial intelligence in diabetes management is moving from pilot projects to operational care infrastructure as health systems confront rising prevalence, clinician shortages, and the need for continuous, personalized support. The International Diabetes Federation estimated that 537 million adults lived with diabetes in 2021, with the total projected to reach 643 million by 2030 and 783 million by 2045, creating sustained demand for AI-enabled glucose monitoring, predictive analytics, insulin titration support, and remote patient management.

For providers and payers, the strongest value proposition is measurable improvement in risk stratification, adherence, early intervention, and workflow efficiency. AI tools that integrate electronic health records, continuous glucose monitoring data, claims, laboratory results, and behavioral signals can support earlier detection of hypoglycemia risk, diabetes complications, and care gaps while improving population health management.

Transformative Shifts Reshaping Diabetes Care

The landscape is being reshaped by connected devices, cloud-based analytics, regulatory modernization, and wider adoption of continuous glucose monitoring. The American Diabetes Association's Standards of Care increasingly recognize the role of diabetes technology, including continuous glucose monitoring and automated insulin delivery, while regulators are refining expectations for software as a medical device, clinical decision support, cybersecurity, and adaptive algorithms.

A major shift is the transition from episodic clinic-based diabetes care to continuous, data-driven care. AI-powered decision support can identify patients who need medication review, nutrition coaching, retinal screening, nephropathy testing, cardiovascular risk assessment, or behavioral support, enabling organizations to move from reactive treatment to proactive chronic disease management.

Cumulative Impact of Artificial Intelligence

The cumulative impact of artificial intelligence is strongest when algorithms are embedded across the full diabetes journey: screening, diagnosis support, therapy optimization, complication detection, adherence management, and outcomes monitoring. Evidence-backed use cases include diabetic retinopathy screening, predictive hypoglycemia alerts, insulin dosing assistance, automated triage of high-risk patients, and risk prediction for kidney, cardiovascular, and foot complications.

At scale, AI can support reductions in avoidable emergency visits, improve time-in-range for insulin users, and strengthen value-based care programs by identifying actionable risk earlier. However, impact depends on validated datasets, transparent performance monitoring, cybersecurity, clinician oversight, regulatory compliance, and equitable access for underserved populations that carry a disproportionate diabetes burden.

Key Regional Insights

Asia-Pacific is central to AI in diabetes management because China and India account for very large diabetes populations, while Japan, Australia, and South Korea have mature digital health ecosystems, connected device adoption, and advanced clinical infrastructure. Across the region, rising type 2 diabetes prevalence, urbanization, smartphone penetration, and government interest in digital health are supporting the use of AI-enabled glucose analytics, remote patient monitoring, and complication screening.

North America remains a leading commercialization environment through advanced reimbursement structures, high continuous glucose monitoring adoption, strong clinical research networks, telehealth normalization, and regulatory pathways for software-enabled diabetes technologies. Europe benefits from national diabetes programs, interoperable health data initiatives, the European Health Data Space agenda, and the EU AI Act's risk-based governance approach, creating strong demand for trustworthy, clinically validated, and privacy-compliant AI solutions.

Latin America is expanding remote monitoring through private provider networks, public health digitization, and growing attention to chronic disease management in countries with rising obesity and diabetes burdens. The Middle East is prioritizing diabetes prevention and digital care in national health strategies, particularly across Gulf economies where type 2 diabetes prevalence is a major public health concern. Africa remains underpenetrated but offers long-term opportunity through mobile-first screening, community health programs, low-cost AI triage, and diabetic retinopathy detection models suited to resource-constrained settings.

Key Group Insights

ASEAN markets are progressing through mobile health platforms, urban diabetes clinics, expanding insurance coverage, and public-private digital health collaborations, although reimbursement maturity and specialist access vary widely across member states. GCC countries are investing heavily in AI-enabled healthcare as part of national transformation plans, with diabetes management a priority because of high obesity rates, sedentary lifestyle risk factors, and the growing burden of type 2 diabetes.

The European Union is emphasizing trustworthy AI, interoperability, privacy protection, clinical evidence, and post-market monitoring, making compliance a core requirement for AI diabetes management solutions. BRICS countries combine large patient populations with expanding digital infrastructure, creating scale opportunities for AI-enabled screening, remote monitoring, and population health management, while also requiring localized datasets, affordability, and integration with public health systems.

G7 markets provide strong evidence-generation, regulatory clarity, clinical adoption, and reimbursement pathways for AI in diabetes care, particularly where continuous glucose monitoring, electronic health records, and digital therapeutics are already embedded in care models. NATO countries increasingly view health data security, cyber resilience, and supply-chain assurance as strategic requirements, which raises expectations for secure AI deployment, protected patient data exchange, and resilient connected diabetes technologies.

Key Country Insights

The United States is a major innovation and reimbursement market, supported by CDC-reported diabetes prevalence of 38.4 million people and strong adoption of continuous glucose monitoring, telehealth, remote patient monitoring, and regulator-cleared software tools. Canada emphasizes equitable chronic care access, public health coordination, and virtual care adoption, while Mexico is expanding diabetes management through public programs and private provider networks amid a high national burden of type 2 diabetes.

Brazil is advancing digital health and diabetes care through a combination of public health initiatives, private healthcare networks, and growing use of telemedicine. The United Kingdom is strengthening digital diabetes prevention, remote monitoring, and technology-enabled care within a national health system model, while Germany benefits from structured disease management programs, digital health reimbursement mechanisms, and strong medical technology adoption. France is advancing remote monitoring reimbursement and chronic care digitization, Italy and Spain are using regional health systems to expand diabetes technology access, and Russia remains influenced by healthcare localization, procurement requirements, and infrastructure differences across regions.

China offers major scale for AI in diabetes management through large patient volumes, hospital digitization, and rapid adoption of mobile health tools, while India's opportunity is driven by one of the world's largest diabetes populations, expanding smartphone use, and growing demand for low-cost screening and coaching. Japan combines aging-population needs with advanced device integration and clinical infrastructure, Australia supports evidence-led digital health adoption through national digital health capabilities, and South Korea leads in connected health infrastructure, broadband penetration, and technology-enabled chronic disease management.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize clinically validated AI models, measurable outcomes, and workflow integration over standalone digital features. The most defensible strategies combine continuous glucose monitoring data, electronic health record integration, behavioral coaching, medication intelligence, insulin dosing support, and complication screening into interoperable diabetes management platforms.

Organizations should invest in regulatory readiness, real-world evidence, cybersecurity, bias testing, model monitoring, and explainable AI. Commercial teams should align product claims with payer and provider priorities such as fewer acute events, improved HbA1c control, better time-in-range, higher screening adherence, and lower total cost of care. Partnerships with providers, pharmacies, employers, public health agencies, and device manufacturers can accelerate adoption while improving access and continuity of care.

Research Methodology

This executive summary is informed by secondary research from recognized sources including the International Diabetes Federation, World Health Organization, U.S. CDC, American Diabetes Association, U.S. FDA guidance, European regulatory frameworks, peer-reviewed clinical literature, national digital health strategies, and public health policy documents.

The methodology emphasizes triangulation across epidemiology, regulatory developments, technology adoption, reimbursement signals, clinical evidence, and healthcare digitization. Insights were assessed for relevance to AI in diabetes management, including continuous glucose monitoring analytics, automated insulin delivery support, clinical decision support, diabetic retinopathy screening, remote patient monitoring, predictive risk stratification, and AI-enabled population health management.

Conclusion

Artificial intelligence is becoming a core enabler of next-generation diabetes management as healthcare systems seek earlier intervention, personalized therapy, and scalable chronic disease support. Momentum is reinforced by rising diabetes prevalence, expanding continuous glucose monitoring adoption, maturing digital health infrastructure, stronger regulatory guidance, and growing acceptance of software-enabled medical care.

The strongest performers will be organizations that prove clinical value, protect patient data, meet regulatory expectations, integrate into care workflows, and deliver equitable outcomes across diverse populations. AI will not replace clinicians, but it will increasingly augment diabetes care teams with predictive insights, automation, continuous patient engagement, and more targeted chronic disease interventions.

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. Artificial Intelligence in Diabetes Management Market, by Device Type

• 7.1. Blood Glucose Meter
  • 7.1.1. Non Invasive Bg Meter
  • 7.1.2. Smbg
• 7.2. Closed Loop System
  • 7.2.1. Fully Closed Loop
  • 7.2.2. Hybrid Closed Loop
• 7.3. Continuous Glucose Monitor
  • 7.3.1. Intermittently Scanned Cgm
  • 7.3.2. Real Time Cgm
• 7.4. Insulin Pump
  • 7.4.1. Patch Pump
  • 7.4.2. Tubed Pump

8. Artificial Intelligence in Diabetes Management Market, by Technology

• 8.1. Decision Support Systems
  • 8.1.1. Alert Generation
  • 8.1.2. Dose Recommendation
• 8.2. Machine Learning
• 8.3. Mobile Applications
• 8.4. Predictive Analytics
  • 8.4.1. Glucose Trend Prediction
  • 8.4.2. Risk Prediction

9. Artificial Intelligence in Diabetes Management Market, by Component

• 9.1. Hardware
  • 9.1.1. Pumps
  • 9.1.2. Sensors
  • 9.1.3. Wearable Devices
• 9.2. Software
  • 9.2.1. Algorithms
  • 9.2.2. Data Management
  • 9.2.3. User Interface

10. Artificial Intelligence in Diabetes Management Market, by Deployment Mode

• 10.1. Cloud Based
  • 10.1.1. Hybrid Cloud
  • 10.1.2. Public Cloud
• 10.2. On Premise
  • 10.2.1. Edge Computing
  • 10.2.2. Server Based

11. Artificial Intelligence in Diabetes Management Market, by End User

• 11.1. Clinic
  • 11.1.1. Diabetes Center
  • 11.1.2. General Clinic
• 11.2. Home Care
  • 11.2.1. Remote Monitoring
  • 11.2.2. Self Monitoring
• 11.3. Hospital
  • 11.3.1. Inpatient
  • 11.3.2. Outpatient
• 11.4. Research Institute
  • 11.4.1. Academic
  • 11.4.2. Private

12. Artificial Intelligence in Diabetes Management 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. Artificial Intelligence in Diabetes Management Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Artificial Intelligence in Diabetes Management 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. Abbott Laboratories
• 16.2. Apple Inc.
• 16.3. Bigfoot Biomedical, Inc.
• 16.4. Dexcom, Inc.
• 16.5. Diabeloop SA
• 16.6. Eyenuk, Inc.
• 16.7. F. Hoffmann-La Roche Ltd
• 16.8. Glooko Inc.
• 16.9. Google LLC by Alphabet Inc.
• 16.10. Insulet Corporation
• 16.11. International Business Machines Corporation
• 16.12. Livongo Health, Inc.
• 16.13. Medtronic plc
• 16.14. Omada Health, Inc.
• 16.15. Tandem Diabetes Care, Inc.
• 16.16. Teladoc Health, Inc.
• 16.17. Tidepool Inc.
• 16.18. Virta Health Corp.
• 16.19. Wellthy Therapeutics Pvt. Ltd.