糖尿病管理中的人工智慧市場 - 全球產業規模、佔有率、趨勢、機會和預測，按設備、按技術、按地區和競爭細分，2020-2030 年

2024 年全球糖尿病管理人工智慧市場價值為 136.2 億美元，預計到 2030 年將達到 190.3 億美元，預測期內複合年成長率為 8.69%。全球糖尿病管理人工智慧市場是指在糖尿病的管理和治療中使用人工智慧（AI）技術。根據IDF糖尿病地圖集（2021年），20-79歲的成年人中有10.5％患有糖尿病，其中近一半的人並不知道自己患有糖尿病。 IDF 預測，到 2045 年，每 8 個成年人中就有 1 個，約 7.83 億人將患有糖尿病，增幅為 46%

人工智慧在醫療保健領域的應用越來越廣泛，以提高包括糖尿病在內的各種疾病的診斷、監測和治療的準確性和效率。治療糖尿病涉及監測患者健康的各個方面，包括血糖水平、血壓和藥物依從性。人工智慧擅長整合來自不同來源的資料，為醫療保健專業人員提供患者健康狀況的全面視圖。這種綜合方法能夠更好地決策和協調護理，解決日益普遍的糖尿病人群中糖尿病管理的複雜性。

主要市場促進因素

糖尿病盛行率上升

主要市場挑戰

資料隱私和安全問題

主要市場趨勢

預測分析需求不斷成長

目錄

第 1 章：產品概述

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：顧客之聲

第5章：全球糖尿病管理人工智慧市場展望

• 市場規模和預測
  • 按價值
• 市場佔有率和預測
  • 依設備（診斷設備、血糖監測設備、胰島素輸送設備）
  • 按技術（基於案例的推理、智慧數據分析）
  • 按地區
  • 按公司分類（2024）
• 市場地圖

第6章：北美糖尿病管理人工智慧市場展望

• 市場規模和預測
• 市場佔有率和預測
• 北美：國家分析
  • 加拿大
  • 墨西哥

第7章：歐洲糖尿病管理人工智慧市場展望

• 市場規模和預測
• 市場佔有率和預測
• 歐洲：國家分析
  • 英國
  • 義大利
  • 法國
  • 西班牙

第 8 章：亞太地區糖尿病管理人工智慧市場展望

• 市場規模和預測
• 市場佔有率和預測
• 亞太地區：國家分析
  • 印度
  • 日本
  • 韓國
  • 澳洲

第9章：南美洲糖尿病管理人工智慧市場展望

• 市場規模和預測
• 市場佔有率和預測
• 南美洲：國家分析
  • 阿根廷
  • 哥倫比亞

第 10 章：中東和非洲糖尿病管理人工智慧市場展望

• 市場規模和預測
• 市場佔有率和預測
• MEA：國家分析
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國

第 11 章：市場動態

• 驅動程式
• 挑戰

第 12 章：市場趨勢與發展

• 合併與收購（如有）
• 產品發布（如果有）
• 最新動態

第 13 章：波特五力分析

• 產業競爭
• 新進入者的潛力
• 供應商的力量
• 顧客的力量
• 替代產品的威脅

第 14 章：競爭格局

• Vodafone Group PLC
• Apple Inc
• Google Inc
• International Business Machines Corporation (IBM)
• Glooko Inc
• Tidepool Inc

第 15 章：策略建議

第16章 關於出版商,免責事項

Global Artificial Intelligence in Diabetes Management Market was valued at USD 13.62 Billion in 2024 and is expected to reach USD 19.03 Billion by 2030 with a CAGR of 8.69% during the forecast period. The Global Artificial Intelligence in Diabetes Management Market refers to the use of artificial intelligence (AI) technologies in the management and treatment of diabetes. According to the IDF Diabetes Atlas (2021), 10.5% of adults aged 20-79 are affected by diabetes, with nearly half unaware of their condition. Projections by the IDF indicate that by 2045, 1 in 8 adults, or approximately 783 million people, will have diabetes-a 46% increase

AI has been increasingly employed in healthcare to enhance the accuracy and efficiency of diagnosis, monitoring, and treatment of various diseases, including diabetes. Managing diabetes involves monitoring various aspects of a patient's health, including glucose levels, blood pressure, and medication adherence. AI excels at integrating data from diverse sources, providing healthcare professionals with a comprehensive view of a patient's health. This integrated approach enables better decision-making and coordination of care, addressing the complexity of diabetes management in an increasingly prevalent population.

Key Market Drivers

Rising Diabetes Prevalence

Diabetes, often referred to as a global epidemic, has been steadily on the rise for several decades More than 90% of diabetes cases are type 2, driven by socio-economic, demographic, environmental, and genetic factors. Key contributors to the rise in type 2 diabetes include urbanization, an ageing population, reduced physical activity, and increasing rates of overweight and obesity. This alarming increase in diabetes prevalence presents a significant challenge to healthcare systems worldwide. However, it also presents a unique opportunity for the development and adoption of Artificial Intelligence (AI) in diabetes management. With the growing number of individuals at risk of developing diabetes, early diagnosis and risk prediction have become critical. AI-powered algorithms can analyze vast datasets, including medical records and genetic information, to identify individuals at high risk of diabetes. This proactive approach allows healthcare providers to intervene early, potentially preventing or delaying the onset of the disease. As a result, the demand for AI-driven diagnostic tools and risk assessment models is on the rise.

Managing diabetes is not a one-size-fits-all approach. Each individual's response to treatment varies, making personalized treatment plans essential. AI algorithms can analyze a patient's unique health data, including glucose levels, medication history, and lifestyle factors, to create personalized treatment plans. These plans optimize medication regimens, dietary recommendations, and exercise routines, leading to better glycemic control. As diabetes prevalence increases, the demand for tailored, AI-driven treatment plans is set to grow. Continuous Glucose Monitoring (CGM) devices, integrated with AI algorithms, are revolutionizing diabetes management. These devices provide real-time data on blood glucose levels, allowing individuals with diabetes and their healthcare providers to make informed decisions about insulin dosages, diet adjustments, and exercise routines. As more people seek efficient and accurate ways to manage their diabetes, the demand for CGM solutions powered by AI is expected to surge.

The rise of telemedicine and remote monitoring solutions is closely tied to the increasing prevalence of diabetes. AI-enhanced telemedicine platforms enable healthcare providers to remotely monitor patients with diabetes, reducing the need for frequent in-person visits. This not only improves patient convenience but also ensures timely interventions and support. As the diabetic population continues to grow, so does the demand for convenient and accessible care, driving the adoption of AI in telemedicine.

Key Market Challenges

Data Privacy and Security Concerns

Data privacy and security are among the most significant concerns when implementing AI in diabetes management. AI systems in healthcare rely heavily on patient-specific data, including personal medical records, genetic information, and lifestyle details. The collection and analysis of such data are crucial for AI algorithms to deliver accurate predictions and treatment recommendations. However, the highly sensitive nature of this information makes it a target for cyber threats and breaches, which could severely compromise patient confidentiality. To mitigate such risks, healthcare organizations must adhere to stringent data protection regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. or the General Data Protection Regulation (GDPR) in Europe, which impose strict rules on how patient data is collected, stored, and shared. Compliance with these regulations requires significant investment in cybersecurity infrastructure, legal expertise, and staff training. Moreover, patient consent and transparency about how their data is used become vital to maintain trust in AI systems. As healthcare organizations look to implement AI solutions, they must not only invest in secure data storage and transmission systems but also establish robust protocols for data access and usage, ensuring the ethical handling of sensitive health data. Balancing the need for AI to function effectively with stringent data protection requirements remains an ongoing challenge, one that requires continued attention to both technical and legal frameworks.

Key Market Trends

Rising demand for Predictive Analytics

The prevalence of diabetes is on the rise worldwide, creating an urgent need for more effective and efficient ways to manage this chronic condition. Predictive Analytics, when combined with Artificial Intelligence (AI), is emerging as a powerful tool in the field of diabetes management. Predictive Analytics utilizes AI algorithms to analyze extensive datasets, including patient health records, genetic information, and lifestyle factors. By identifying patterns and correlations, these algorithms can predict an individual's risk of developing diabetes or prediabetes. Early detection and risk assessment are crucial in combating the rising prevalence of diabetes, as they enable healthcare providers to intervene proactively and provide personalized preventive measures. In April 2023, Insulet Corporation announced FDA clearance for its latest innovation, the Omnipod GO, an insulin delivery device designed for individuals with type 2 diabetes aged 18 and older. This device offers a more convenient alternative to traditional injection methods for those who typically require daily long-acting insulin injections.

One of the critical challenges in diabetes management is tailoring treatment plans to individual patients. Predictive Analytics enhances the personalization of these plans by taking into account an individual's specific health metrics, medication history, dietary preferences, and activity levels. This precision in treatment recommendations improves patient compliance and ultimately contributes to better glycemic control. Diabetes is associated with various complications, including neuropathy, retinopathy, and cardiovascular diseases. Predictive Analytics can analyze patient data to predict the likelihood of these complications developing. By identifying high-risk patients, healthcare providers can implement preventive measures, offer specialized care, and closely monitor those at risk, potentially reducing the incidence and severity of complications.

Managing diabetes often involves adjusting medication regimens. Predictive Analytics can analyze a patient's glucose trends and medication response over time. This data-driven approach enables healthcare providers to optimize medication dosages and types for each patient, reducing the risk of hypoglycemia and hyperglycemia episodes.The rise of tele health and remote monitoring is transforming diabetes care, and Predictive Analytics plays a pivotal role. These systems continuously collect patient data, including glucose levels, activity, and vital signs. AI-driven predictive models can analyze this real-time data to detect deviations from the norm, prompting timely interventions by healthcare providers. Remote monitoring offers convenience for patients and can help reduce the strain on healthcare systems. On a broader scale, Predictive Analytics can be used to identify trends and patterns in diabetes prevalence within specific populations. Public health organizations and policymakers can leverage this information to allocate resources, design targeted interventions, and implement preventive strategies. This population-level approach can contribute to reducing the overall burden of diabetes. In the realm of diabetes research, Predictive Analytics is invaluable. It can analyze vast datasets from clinical trials to identify potential biomarkers, treatment responses, and patient subgroups. This information accelerates the development of new therapies and interventions for diabetes management.

Key Market Players

• Vodafone Group PLC
• Apple Inc
• Google Inc
• International Business Machines Corporation (IBM)
• Glooko Inc
• Tidepool Inc

Report Scope:

In this report, the Global Artificial Intelligence in Diabetes Management Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Artificial Intelligence in Diabetes Management Market, By Device:

• Diagnostic Devices
• Glucose Monitoring Devices
• Insulin Delivery Devices

Artificial Intelligence in Diabetes Management Market, By Technique:

• Case-Based Reasoning
• Intelligent Data Analysis

Artificial Intelligence in Diabetes Management Market, By Region:

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • Germany
  • United Kingdom
  • France
  • Italy
  • Spain
• Asia-Pacific
  • China
  • Japan
  • India
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Artificial Intelligence in Diabetes Management Market.

Available Customizations:

Global Artificial Intelligence in Diabetes Management market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validations
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Artificial Intelligence in Diabetes Management Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Device (Diagnostic Devices, Glucose Monitoring Devices, Insulin Delivery Devices)
  • 5.2.2. By Technique (Case-Based Reasoning, Intelligent Data Analysis)
  • 5.2.3. By Region
  • 5.2.4. By Company (2024)
• 5.3. Market Map

6. North America Artificial Intelligence in Diabetes Management Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Device
  • 6.2.2. By Technique
  • 6.2.3. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Artificial Intelligence in Diabetes Management Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Device
      • 6.3.1.2.2. By Technique
  • 6.3.2. Canada Artificial Intelligence in Diabetes Management Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Device
      • 6.3.2.2.2. By Technique
  • 6.3.3. Mexico Artificial Intelligence in Diabetes Management Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Device
      • 6.3.3.2.2. By Technique

7. Europe Artificial Intelligence in Diabetes Management Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Device
  • 7.2.2. By Technique
  • 7.2.3. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Artificial Intelligence in Diabetes Management Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Device
      • 7.3.1.2.2. By Technique
  • 7.3.2. United Kingdom Artificial Intelligence in Diabetes Management Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Device
      • 7.3.2.2.2. By Technique
  • 7.3.3. Italy Artificial Intelligence in Diabetes Management Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Device
      • 7.3.3.2.2. By Technique
  • 7.3.4. France Artificial Intelligence in Diabetes Management Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Device
      • 7.3.4.2.2. By Technique
  • 7.3.5. Spain Artificial Intelligence in Diabetes Management Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Device
      • 7.3.5.2.2. By Technique

8. Asia-Pacific Artificial Intelligence in Diabetes Management Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Device
  • 8.2.2. By Technique
  • 8.2.3. By Country
• 8.3. Asia-Pacific: Country Analysis
  • 8.3.1. China Artificial Intelligence in Diabetes Management Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Device
      • 8.3.1.2.2. By Technique
  • 8.3.2. India Artificial Intelligence in Diabetes Management Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Device
      • 8.3.2.2.2. By Technique
  • 8.3.3. Japan Artificial Intelligence in Diabetes Management Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Device
      • 8.3.3.2.2. By Technique
  • 8.3.4. South Korea Artificial Intelligence in Diabetes Management Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Device
      • 8.3.4.2.2. By Technique
  • 8.3.5. Australia Artificial Intelligence in Diabetes Management Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Device
      • 8.3.5.2.2. By Technique

9. South America Artificial Intelligence in Diabetes Management Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Device
  • 9.2.2. By Technique
  • 9.2.3. By Country
• 9.3. South America: Country Analysis
  • 9.3.1. Brazil Artificial Intelligence in Diabetes Management Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Device
      • 9.3.1.2.2. By Technique
  • 9.3.2. Argentina Artificial Intelligence in Diabetes Management Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Device
      • 9.3.2.2.2. By Technique
  • 9.3.3. Colombia Artificial Intelligence in Diabetes Management Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Device
      • 9.3.3.2.2. By Technique

10. Middle East and Africa Artificial Intelligence in Diabetes Management Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Device
  • 10.2.2. By Technique
  • 10.2.3. By Country
• 10.3. MEA: Country Analysis
  • 10.3.1. South Africa Artificial Intelligence in Diabetes Management Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Device
      • 10.3.1.2.2. By Technique
  • 10.3.2. Saudi Arabia Artificial Intelligence in Diabetes Management Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Device
      • 10.3.2.2.2. By Technique
  • 10.3.3. UAE Artificial Intelligence in Diabetes Management Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Device
      • 10.3.3.2.2. By Technique

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Porter's Five Forces Analysis

• 13.1. Competition in the Industry
• 13.2. Potential of New Entrants
• 13.3. Power of Suppliers
• 13.4. Power of Customers
• 13.5. Threat of Substitute Products

14. Competitive Landscape

• 14.1. Vodafone Group PLC
  • 14.1.1. Business Overview
  • 14.1.2. Company Snapshot
  • 14.1.3. Products & Services
  • 14.1.4. Financials (As Reported)
  • 14.1.5. Recent Developments
  • 14.1.6. Key Personnel Details
  • 14.1.7. SWOT Analysis
• 14.2. Apple Inc
• 14.3. Google Inc
• 14.4. International Business Machines Corporation (IBM)
• 14.5. Glooko Inc
• 14.6. Tidepool Inc

15. Strategic Recommendations

16. About Us & Disclaimer