Global Artificial Intelligence in Diabetes Management by Device types (Glucose Monitoring Devices, Diagnostic Devices and Insulin Delivery Devices); By Artificial Intelligence Techniques (Intelligent Data Analysis and Case Based Reasoning); By Regions (North America, Europe, APAC and LAMEA); Drivers, Opportunities, Restraints, Trends, and Forecast to 2023
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Diabetes is a major pervasive chronic ailment that extensively impacts the global population. It is a global epidemic and the most expensive disease. As per the Center for Disease Control and Prevention, the US alone incurs more than $245 billion a year in lost wages. Millions of people are afflicted with diabetes worldwide, and this number is rising at a rapid pace creating lucrative opportunities for companies and entrepreneurs. According to WHO statistics, nearly 422 million people globally have diabetes, and the number is accelerating at a burgeoning pace. One in 11 people must manage this chronic condition on a regular basis. The data-intensive nature of diabetes care and management makes it an ideal fit for applying artificial intelligence and machine learning to improve outcomes and find better solutions. Diabetes often has a debilitating effect on individuals resulting in strokes, heart attack, blindness, amputation, and kidney failure. Diabetes mellitus is a clinical condition in which an individual has elevated blood glucose levels. Insulin-dependent (type 1) diabetes is managed by monitoring the blood glucose level with finger prick blood tests performed several times throughout the day and adjusting insulin levels based on such readings. This invasive method is painful, inconvenient, time-consuming, and not good enough for managing a dynamic and complex disease condition such as diabetes. Several non-invasive methods of blood glucose measurement and insulin delivery are under development. Glucose measurement devices are now capable of continuously monitoring blood glucose levels throughout the day.
Digitization has made big data technologies more relevant in the healthcare context. The proliferation of smart devices has accelerated the transition to digitized healthcare. Smart apps have made self-management of diabetes by the patients affordable. Sensors are capable of transmitting data to the smart devices and diabetics can monitor their blood glucose levels on these devices. Pharmacogenetics and machine learning will help systems embedded with AI to manage diabetes better than humans within the next 20 years. AI algorithms bring about state-of-the-art glucose prediction methods that help in tackling diabetes effectively. They can mimic human cognitive functions. AI is leveraging the avalanche of healthcare data. AI is improving patient outcomes for diabetes patients. Data analytics is expected to make a disruptive impact in diabetes management. Machine learning algorithms enable better diagnosis and monitoring resulting in improved patient-centric treatment. Insulin trackers in the form of wearables, wristwatches, waistbands, and even pens can help in monitoring glucose levels in the blood. Portable and custom-made decision support systems for insulin dosing accumulate data from multiple sources such as manual inputs and body worn sensors. It is predicted that diabetes will extensively be managed by smart machines and AI algorithms within 20 years.
According to Infoholic Research, the “Global Artificial Intelligence in Diabetes Management” market is expected to grow at a CAGR of 50.7% during the forecast period 2017–2023. The market is driven by factors such as rising elderly population, increasing obesity, favorable demographics, urbanization, rising healthcare costs, and increasing digital affinity. Patient’s privacy and security concerns, lack of curated datasets, and less digital savvy patients are few growth deterrents in the market. The market experiences few growth opportunities such as product innovations, democratization of knowledge, and rising prevalence of type 1 and type 2 diabetes.
Segmentation by Device Types
The market has been segmented and analyzed by the following device types: glucose monitoring devices, diagnostic devices, insulin delivery devices, and other devices.
Segmentation by Artificial Intelligence Techniques
The market has been segmented and analyzed by two artificial intelligence techniques namely case-based reasoning and intelligent data analysis.
Segmentation by Regions
The market has been segmented and analyzed by the following regions: North America, Europe, APAC, and LAMEA.
Segmentation by Countries
Each region has been further segmented and analyzed by countries:
North America – US and Canada
Europe – UK, Germany, Italy, and France
APAC – China, Japan, and India
LAMEA – GCC Countries, Mexico, and Africa
The study covers and analyzes the “Global Artificial Intelligence in Diabetes Management” market. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
- The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
- It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategies, and views.
- The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
- In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.
1.1 Industry Overview
1.2 Industry Trends
1.2.1 Trends in Diabetes Management
2 Report Outline
2.1 Report Scope
2.2 Report Summary
2.3 Research Methodology
2.4 Report Assumptions
3 Market Snapshot
3.1 Total Addressable Market (TAM)
3.2 Related Markets
4 Market Outlook
4.2 Regulatory Bodies & Standards
4.3 Political and Legal Implications in Diabetes Care
4.4 Economic Implications in Diabetes Care
4.5 Societal and Environmental Implications in Diabetes Care
4.6 Porter 5 (Five) Forces
4.7 Market Segmentation
4.8 Market Dynamics
18.104.22.168 Favorable Demographics
22.214.171.124 Obesity Pandemic
126.96.36.199 Rising Healthcare Costs
188.8.131.52 Rising Digital Affinity
184.108.40.206 Less Digital Savvy Patients
220.127.116.11 Patient Privacy and Data Security Concerns
18.104.22.168 Reimbursement and Cost Coverage Issues
22.214.171.124 Lack of Curated Data sets
126.96.36.199 Products Innovations
188.8.131.52 Rising Prevalence of Type 1 and Type 2 Diabetes
184.108.40.206 Growing usage of mobile technology in diabetes management
220.127.116.11 Democratization of Knowledge
4.8.4 DRO – Impact Analysis
5 Trends, Roadmap and Projects
5.1 Market Trends & Impact
5.2 Technology Roadmap
6 Device Types: Market Size & Analysis
6.1.1 Market Size and Analysis
6.2 Glucose Monitoring Devices
6.3 Diagnostic devices
6.4 Insulin Delivery Devices
7 Artificial intelligence Techniques: Market Size & Analysis
7.1.1 Intelligent Data Analysis
7.1.2 Case Based Reasoning
8 Geographic Segmentation: Market Size & Analysis
9 Global Generalist
9.1.1 IBM Corporation
18.104.22.168 Business units
22.214.171.124 Geographic revenue
126.96.36.199 IBM in Diabetes Management
188.8.131.52 Business focus
184.108.40.206 SWOT analysis
220.127.116.11 Business strategies
9.1.2 Google Inc.
18.104.22.168 Business units
22.214.171.124 Geographic revenue
126.96.36.199 Google in Diabetes Management
188.8.131.52 Business focus
184.108.40.206 SWOT analysis
220.127.116.11 Business strategies
9.1.3 Apple Inc.
18.104.22.168 Business units
22.214.171.124 Geographic revenue
126.96.36.199 Apple in Diabetes Management
188.8.131.52 Business focus
184.108.40.206 SWOT analysis
220.127.116.11 Business strategies
9.1.4 Vodafone Group Plc.
18.104.22.168 Business units
22.214.171.124 Geographic revenue
126.96.36.199 Vodafone in Diabetes Management
188.8.131.52 Business focus
184.108.40.206 SWOT analysis
220.127.116.11 Business strategies
10 Companies to Watch for
10.1.2 Diabnext Offerings
10.2 Glooko Inc.
10.2.3 Glukoo Offerings
10.2.4 Strategic Collaborations
10.3.3 Tidepool Offerings
11 Competitive Landscape
11.1 Competitor Comparison Analysis
12 Expert’s Views
Infoholic Research works on a holistic 360° approach in order to deliver high quality, validated and reliable information in our market reports. The Market estimation and forecasting involves following steps:
- Data Collation (Primary & Secondary)
- In-house Estimation (Based on proprietary data bases and Models)
- Market Triangulation
Market related information is congregated from both primary and secondary sources.
Involved participants from all global stakeholders such as Solution providers, service providers, Industry associations, thought leaders etc. across levels such as CXOs, VPs and managers. Plus, our in-house industry experts having decades of industry experience contribute their consulting and advisory services.
Include public sources such as regulatory frameworks, government IT spending, government demographic indicators, industry association statistics, and company publications along with paid sources such as Factiva, OneSource, Bloomberg among others.