PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1757979
PUBLISHER: Global Industry Analysts, Inc. | PRODUCT CODE: 1757979
Global Artificial Intelligence (AI) in Diabetes Management Market to Reach US$21.4 Billion by 2030
The global market for Artificial Intelligence (AI) in Diabetes Management estimated at US$13.2 Billion in the year 2024, is expected to reach US$21.4 Billion by 2030, growing at a CAGR of 8.3% over the analysis period 2024-2030. Diagnostic Devices, one of the segments analyzed in the report, is expected to record a 9.3% CAGR and reach US$14.0 Billion by the end of the analysis period. Growth in the Glucose Monitoring Devices segment is estimated at 7.0% CAGR over the analysis period.
The U.S. Market is Estimated at US$3.6 Billion While China is Forecast to Grow at 13.1% CAGR
The Artificial Intelligence (AI) in Diabetes Management market in the U.S. is estimated at US$3.6 Billion in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$4.7 Billion by the year 2030 trailing a CAGR of 13.1% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 4.2% and 8.0% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 5.6% CAGR.
Global Artificial Intelligence (AI) in Diabetes Management Market - Key Trends & Drivers Summarized
Is AI Revolutionizing Personalized Care in Diabetes Management?
Artificial Intelligence (AI) is redefining the landscape of diabetes management by enabling unprecedented levels of personalization, precision, and proactive care. Traditionally, diabetes treatment has been characterized by reactive and generalized approaches, relying heavily on periodic glucose measurements, standard medication schedules, and patient self-monitoring. However, with the advent of AI-powered systems, diabetes care is transitioning into a dynamic, real-time, and individualized model. Machine learning algorithms can now analyze massive streams of data from continuous glucose monitors (CGMs), insulin pumps, fitness trackers, and electronic health records to identify patterns and predict fluctuations in blood glucose levels before they happen. These insights empower patients to take preventive actions, such as adjusting their diet or activity level, even before symptoms arise. AI-enabled platforms also support real-time insulin dosing recommendations and closed-loop systems, often referred to as “artificial pancreas” technologies, which automatically administer insulin based on predictive analytics. Moreover, these systems provide tailored coaching and lifestyle guidance through mobile apps, enhancing adherence and long-term outcomes. Natural language processing is further being used to extract insights from unstructured clinical notes, while AI chatbots offer 24/7 support and reminders. Collectively, these innovations are reducing the cognitive burden on patients, minimizing complications, and making diabetes management more responsive and sustainable. As AI continues to learn from diverse patient populations and evolving medical data, it is setting a new standard for intelligent, patient-centric diabetes care.
How Are Digital Health Ecosystems and Remote Monitoring Expanding AI’s Role?
The expanding digital health ecosystem is providing fertile ground for AI to play a central role in comprehensive diabetes management, especially through remote monitoring and telemedicine solutions. The COVID-19 pandemic accelerated the adoption of digital health tools, and diabetes care was among the areas that experienced the most significant transformation. With increased reliance on virtual consultations, AI-enabled platforms became critical for monitoring patient conditions outside of clinical settings. Remote glucose monitoring devices paired with AI algorithms are now used to alert healthcare providers and patients to dangerous trends, such as hyperglycemia or hypoglycemia, in real time. These systems help clinicians prioritize interventions and reduce emergency hospital visits. AI also supports dynamic risk stratification, enabling physicians to allocate resources and customize care plans based on individual risk profiles. Integration with wearable devices and smartphones allows for the collection of contextual data-like physical activity, sleep, and food intake-which AI models analyze to give holistic insights into patient health. Such comprehensive tracking is particularly beneficial in managing Type 1 and Type 2 diabetes in patients with coexisting conditions. Moreover, AI is enabling large-scale population health management by aggregating anonymized data to identify regional trends, medication effectiveness, and gaps in care delivery. In rural or underserved areas, where access to endocrinologists may be limited, AI-driven virtual care platforms are bridging the gap, providing timely and consistent care. As digital health ecosystems become more sophisticated, AI is poised to become the central intelligence layer connecting devices, providers, and patients in an integrated care continuum.
Is AI Changing the Paradigm of Early Detection and Predictive Intervention?
AI is proving to be a game-changer in the early detection and prediction of diabetes-related complications, fundamentally shifting the focus of care from treatment to prevention. One of the most powerful applications of AI in diabetes management lies in its predictive capabilities. Using vast datasets from electronic health records, genetic profiles, imaging results, and biometric data, machine learning models can identify early warning signs of diabetes onset, long before symptoms become apparent. For individuals at risk of developing Type 2 diabetes, AI tools can generate risk scores and recommend targeted lifestyle changes, thus preventing disease progression. In diagnosed patients, AI is being used to anticipate complications such as diabetic retinopathy, nephropathy, neuropathy, and cardiovascular disease by detecting subtle patterns that are invisible to the human eye. AI-enhanced retinal imaging systems, for example, are being deployed in primary care clinics to screen for diabetic eye disease without the need for specialist intervention. Similarly, AI models are helping clinicians determine the likelihood of hospitalization or insulin resistance, enabling timely intervention. AI is also revolutionizing clinical trial design by identifying suitable patient cohorts, predicting treatment adherence, and monitoring outcomes with greater precision. As a result, pharmaceutical companies and healthcare systems are increasingly investing in AI tools to enhance research, reduce costs, and personalize care pathways. This proactive approach not only improves clinical outcomes but also reduces long-term healthcare expenditures. In essence, AI is pushing diabetes management upstream-toward earlier identification, predictive action, and ultimately, disease prevention.
What Is Driving the Rapid Global Adoption of AI in Diabetes Management?
The growth in the artificial intelligence (AI) in diabetes management market is driven by several factors rooted in technology advancement, healthcare transformation, and evolving patient expectations. The increasing global prevalence of diabetes-estimated to affect over half a billion people-has created an urgent need for scalable, cost-effective solutions that go beyond traditional clinical models. AI’s ability to deliver real-time, data-driven decision support at scale makes it ideally suited to address this challenge. Technological advancements in sensors, wearables, and mobile platforms have created an interconnected ecosystem where data can be collected continuously and unobtrusively, providing the raw material for AI analysis. On the consumer side, there is rising demand for digital health tools that offer personalized insights, reduce the burden of self-care, and improve quality of life. From a provider standpoint, AI solutions help mitigate clinician burnout by automating routine tasks, generating actionable alerts, and enhancing diagnostic accuracy. The integration of AI into cloud platforms, electronic medical records, and telemedicine systems further supports interoperability and scalability. Regulatory agencies and payers are beginning to recognize the value of AI in chronic disease management, leading to increased reimbursement coverage and policy support. Additionally, strategic investments and partnerships among medtech companies, pharmaceutical firms, and AI startups are accelerating product development and market expansion. In emerging economies, mobile-first AI applications are helping extend care to remote populations, democratizing access to quality diabetes care. Together, these factors are creating a robust and rapidly growing global market where AI stands at the center of a new, intelligent paradigm for managing diabetes.
SCOPE OF STUDY:
The report analyzes the Artificial Intelligence (AI) in Diabetes Management market in terms of units by the following Segments, and Geographic Regions/Countries:
Segments:
Device (Diagnostic Devices, Glucose Monitoring Devices, Insulin Delivery Devices); Technique (Case-based Reasoning Technique, Intelligent Data Analysis Technique)
Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.
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