Global Artificial Intelligence In Diabetes Management Market is valued at USD 13.8 Bn in 2024 and is predicted to reach USD 32.4 Bn by the year 2034 at a 10.6% CAGR during the forecast period for 2025-2034.
Artificial intelligence in diabetes management describes medical devices that observe, analyze, and treat diabetes remotely. The biological condition known as diabetes raises sugar levels in the circulatory system. The digital treatment of diabetes includes utilising software-driven systems, apps, and gadgets, including injection pens, detectors, closed-loop techniques, insulin updates, and smart glucose meters. This absorbed strategy addresses the difficulty of managing diabetes in a population that is becoming more and more common by facilitating better choice-making and treatment coordination.
The demand for diabetes management platforms is growing significantly due to technological improvements. The industry for diabetes management platforms is expanding as a result of the growing emphasis on individualized therapy. The requirement for platforms that provide individualized solutions is being driven by patients' growing need for therapies that are specifically designed to meet their demands.
However, one of the biggest issues when using AI to control diabetes is data security and privacy. In medical treatment, artificial intelligence relies significantly on tailored patient data, such as lifestyle characteristics, genealogy data, and private health records. For AI systems to produce precise forecasts and treatment suggestions, this type of data must be gathered and analyzed. However, because this data is so sensitive, it is vulnerable to cyberattacks and breaches, which might seriously jeopardize patient privacy. Additionally, worries regarding patient data privacy and a lack of knowledge about digital diabetes treatment are impeding market expansion in underdeveloped nations.
The artificial intelligence in diabetes management market is segmented based on device and technique. Based on the device, the market is segmented into diagnostic devices, glucose monitoring devices, and insulin delivery devices. By technique, the market is segmented into case-based reasoning and intelligent data analysis.
Insulin delivery devices are expected to hold a major global market share in 2021 in the artificial intelligence in diabetes management market. These devices provide accurate insulin administration, lowering the chance of bradycardia and enhancing glucose regulation in general. More patients choose robots over manual techniques as insulin delivery system technology improves and becomes highly accessible and efficient. The use of intelligent insulin delivery systems is being further accelerated by their progress, which integrates with online resources for remote oversight and analysis.
The intelligent data analysis segment is growing in the artificial intelligence market for diabetes management because diabetic individuals create so much data—including lifestyle information, medical records, and glucose levels—that it takes sophisticated analytical methods to glean helpful insights. Additionally, as AI algorithms and machine learning methods progress, data analysis becomes more precise and effective, propelling intelligent data analysis systems in diabetes management.
The North American artificial intelligence in the diabetes management market is expected to register the highest market share in revenue shortly. This can be attributed to the region's established leading companies and the rising incidence of diabetes. Another factor contributing to the region's dominance in this sector is the existence of reputable artificial intelligence firms. Due to the combination of these variables, North America is leading the worldwide demand for artificial intelligence in diabetes management. In addition, the Asia Pacific is expected to grow rapidly in the artificial intelligence in diabetes management market owing to an increasing proportion of people with the disease, rising medical expenses, and more knowledge about how to treat the disease. Furthermore, government programs that encourage diabetes control, increased financial resources, and improved medical facilities contribute to the market's acceleration.
The key players in the artificial intelligence in diabetes management market have shifted their focus toward technological advancement and higher demand for them. They are initiating significant strategies, such as mergers and joint ventures of major and domestic players, to expand their selection of products and raise their global market footprint. Some of the major key players in the artificial intelligence in diabetes management market are Vodafone Group PLC, Apple Inc, Google Inc, International Business Machines Corporation (IBM), Glooko Inc, and Tidepool Inc.
| Report Attribute | Specifications |
| Market Size Value In 2024 | USD 13.8 Billion |
| Revenue Forecast In 2034 | USD 32.4 Billion |
| Growth Rate CAGR | CAGR of 10.6% from 2025 to 2034 |
| Quantitative Units | Representation of revenue in US$ Mn,and CAGR from 2025 to 2034 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Device, Technique |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | Vodafone Group PLC, Apple Inc, Google Inc, International Business Machines Corporation (IBM), Glooko Inc, and Tidepool Inc. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Segmentation of Artificial Intelligence in Diabetes Management Market -
By Device
By Technique
By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.