The global artificial intelligence in healthcare market size is expected to reach around US$ 187.95 billion by 2030 from US$ 11.06 billion in 2021 and is expected to grow at an impressive double-digit rate of 37% from 2022 to 2030.
The study includes drivers and restraints of this market. The study provides an analysis of the global artificial intelligence in healthcare market for the period 2017-2030, wherein 2022 to 2030 is the forecast period and 2021 is considered as the base year.
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Report Scope of the Artificial Intelligence in Healthcare Market
| Report Coverage | Details |
| Market Size by 2030 | USD 187.95 Billion |
| Growth Rate from 2022 to 2030 | CAGR of 37% |
| North America Market Share in 2021 | 58.1% |
| Software Solutions Segment Market Share in 2021 | 39.5% |
| Base Year | 2021 |
| Forecast Period | 2022 to 2030 |
Report Highlights
- On the basis of component, the software is the leading and the fastest-growing segment in the global AI in healthcare market. Software is a major component of AI systems and its increased adoption among the healthcare providers, payers, and patients has led to the dominance of this segment.
- By application, the virtual assistant segment is expected to exhibit the highest CAGR during the forecast period. Elimination of human errors, time saving, and accurate results are the major features of the AI-based virtual assistants that is expected to foster the growth of this segment in the upcoming years.
Market Dynamics
Driver
Declining costs of hardware and improved computation
The manufacturers of GPUS and CPUs such as Nvidia, Intel, Huawei, and Samsung have heavily invested in the development of AI compatible chipsets. Furthermore, they have also developed field-programmable gate arrays and application-specific integrated circuits that are compatible with the artificial intelligence technology. These AI compatible chipsets can easily integrate with the AI and the computation functions can be enhanced. The technological advancements in the field of hardware manufacturing has resulted in the decrease in the costs. Therefore, the declining costs of hardware and development of AI-enable chipsets are expected to be the major drivers of the AI in healthcare market during the forecast period.
Restraint
Reluctance in the adoption of AI technology
The misconception among the healthcare professionals/doctors that the AI will replace doctors in future makes them reluctance to adopt the AI-based tools in treatment of patients. The rising adoption of digital health technologies among the consumers has forced the healthcare professionals to adopt the digital technologies and the misconceptions among the doctors may act as a restraint.
Opportunity
Development of human-aware artificial intelligence system
The major purpose of developing AI was to develop a model that can match human thinking. The developers of AI are focusing on the development on human aware artificial intelligence systems. The rising investments towards the improvement of interaction, presentation, and interpretation of the AI systems is expected to offer lucrative growth aspects to the market players as well as it would facilitate more automated and enhanced services in the healthcare sector.
Challenge
Data privacy and cybersecurity
The rising technological advancements have led to the emergence of various threats like cybersecurity and data privacy. The increasing number of cyberattacks may result in financial losses to the healthcare units and may also put the life of the patient in danger. Therefore, the data privacy and cybersecurity are the major issues or challenges faced by the market players.
Research Methodology
A unique research methodology has been utilized to conduct comprehensive research on the growth of the global artificial intelligence in healthcare market and arrive at conclusions on the future growth prospects of the market. This research methodology is a combination of primary and secondary research, which helps analysts warrant the accuracy and reliability of the draw conclusions. Secondary sources referred to by analysts during the production of the global market report include statistics from company annual reports, SEC filings, company websites, World Bank database, investor presentations, regulatory databases, government publications, and industry white papers. Analysts have also interviewed senior managers, product portfolio managers, CEOs, VPs, and market intelligence managers, who contributed to the production of our study on the market as a primary source.
These primary and secondary sources provided exclusive information during interviews, which serves as a validation from mattress topper industry leaders. Access to an extensive internal repository and external proprietary databases allows this report to address specific details and questions about the global artificial intelligence in healthcare market with accuracy. The study also uses the top-down approach to assess the numbers for each segment and the bottom-up approach to counter-validate them. This has helped to estimates the future prospects of the global market more reliable and accurate.
Why should you invest in this report?
If you are aiming to enter the global artificial intelligence in healthcare market, this report is a comprehensive guide that provides crystal clear insights into this niche market. All the major application areas for artificial intelligence in healthcare are covered in this report and information is given on the important regions of the world where this market is likely to boom during the forecast period of 2022-2030, so that you can plan your strategies to enter this market accordingly.
Besides, through this report, you can have a complete grasp of the level of competition you will be facing in this hugely competitive market and if you are an established player in this market already, this report will help you gauge the strategies that your competitors have adopted to stay as market leaders in this market. For new entrants to this market, the voluminous data provided in this report is invaluable.
Some of the prominent players in the global artificial intelligence in healthcare market include:
- Intel
- Koninklijke Philips
- Microsoft
- IBM
- Siemens Healthineers
- Nvidia
- General Electric Company
- Medtronic
- Micron Technology
- Amazon Web Services
- Johnson & Johnson
- General Vision
- CloudmedX
- Oncora Medical
- Enlitic
- Lunit
Market Segmentation:
By Component
- Software
- Hardware
- Services
By Application
- Virtual Assistants
- Diagnosis
- Robot Assisted Surgery
- Clinical Trials
- Wearables
- Administrative Workflow Assistants
- Cybersecurity
- Dosage Error Reduction
- Fraud Detection
- Connected Machines
By Technology
- Machine Learning
- Natural Language Processing
- Context-aware Computing
- Computer Vision
By End User
- Hospital & Healthcare Providers
- Patients
- Pharmaceuticals & Biotechnology Companies
- Healthcare Payers
Regional Analysis:
The geographical analysis of the global artificial intelligence in healthcare market has been done for North America, Europe, Asia-Pacific, and the Rest of the World.
The North American Market is again segmented into the US, Canada, and Mexico. Coming to the European Market, it can be segmented further into the UK, Germany, France, Italy, Spain, and the rest. Coming to the Asia-Pacific, the global artificial intelligence in healthcare Market is segmented into China, India, Japan, and Rest of Asia Pacific. Among others, the market is segmented into the Middle East and Africa, (GCC, North Africa, South Africa and Rest of the Middle East & Africa).
Key Questions Answered by the Report:
- What will be the size of the global artificial intelligence in healthcare market in 2030?
- What is the expected CAGR for the artificial intelligence in healthcare market between 2021 and 2030?
- Which are the top players active in this global market?
- What are the key drivers of this global market?
- How will the market situation change in the coming years?
- Which region held the highest market share in this global market?
- What are the common business tactics adopted by players?
- What is the growth outlook of the global artificial intelligence in healthcare market?
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Artificial Intelligence (AI) in Healthcare Market
5.1. COVID-19 Landscape: Artificial Intelligence (AI) in Healthcare Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Artificial Intelligence (AI) in Healthcare Market, By Component
8.1. Artificial Intelligence (AI) in Healthcare Market, by Component Type, 2022-2030
8.1.1. Software
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Hardware
8.1.2.1. Market Revenue and Forecast (2017-2030)
8.1.3. Services
8.1.3.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Artificial Intelligence (AI) in Healthcare Market, By Application
9.1. Artificial Intelligence (AI) in Healthcare Market, by Application, 2022-2030
9.1.1. Virtual Assistants
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Diagnosis
9.1.2.1. Market Revenue and Forecast (2017-2030)
9.1.3. Robot Assisted Surgery
9.1.3.1. Market Revenue and Forecast (2017-2030)
9.1.4. Clinical Trials
9.1.4.1. Market Revenue and Forecast (2017-2030)
9.1.5. Wearables
9.1.5.1. Market Revenue and Forecast (2017-2030)
9.1.6. Administrative Workflow Assistants
9.1.6.1. Market Revenue and Forecast (2017-2030)
9.1.7. Cybersecurity
9.1.7.1. Market Revenue and Forecast (2017-2030)
9.1.8. Dosage Error Reduction
9.1.8.1. Market Revenue and Forecast (2017-2030)
9.1.9. Fraud Detection
9.1.9.1. Market Revenue and Forecast (2017-2030)
9.1.10. Connected Machines
9.1.10.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Artificial Intelligence (AI) in Healthcare Market, By Technology Type
10.1. Artificial Intelligence (AI) in Healthcare Market, by Technology Type, 2022-2030
10.1.1. Machine Learning
10.1.1.1. Market Revenue and Forecast (2017-2030)
10.1.2. Natural Language Processing
10.1.2.1. Market Revenue and Forecast (2017-2030)
10.1.3. Context-aware Computing
10.1.3.1. Market Revenue and Forecast (2017-2030)
10.1.4. Computer Vision
10.1.4.1. Market Revenue and Forecast (2017-2030)
Chapter 11. Global Artificial Intelligence (AI) in Healthcare Market, By End User Type
11.1. Artificial Intelligence (AI) in Healthcare Market, by End User Type, 2022-2030
11.1.1. Hospital & Healthcare Providers
11.1.1.1. Market Revenue and Forecast (2017-2030)
11.1.2. Patients
11.1.2.1. Market Revenue and Forecast (2017-2030)
11.1.3. Pharmaceuticals & Biotechnology Companies
11.1.3.1. Market Revenue and Forecast (2017-2030)
11.1.4. Healthcare Payers
Chapter 12. Global Artificial Intelligence (AI) in Healthcare Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.2. Market Revenue and Forecast, by Application (2017-2030)
12.1.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.1.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.1.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.1.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.6.2. Market Revenue and Forecast, by Application (2017-2030)
12.1.6.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.1.6.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.2. Market Revenue and Forecast, by Application (2017-2030)
12.2.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.2.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.2.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.2.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.6.2. Market Revenue and Forecast, by Application (2017-2030)
12.2.6.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.2.6.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.7.2. Market Revenue and Forecast, by Application (2017-2030)
12.2.7.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.2.7.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.8.2. Market Revenue and Forecast, by Application (2017-2030)
12.2.8.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.2.8.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.2. Market Revenue and Forecast, by Application (2017-2030)
12.3.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.3.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.3.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.3.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.6.2. Market Revenue and Forecast, by Application (2017-2030)
12.3.6.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.3.6.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.7.2. Market Revenue and Forecast, by Application (2017-2030)
12.3.7.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.3.7.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.8.2. Market Revenue and Forecast, by Application (2017-2030)
12.3.8.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.3.8.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.2. Market Revenue and Forecast, by Application (2017-2030)
12.4.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.4.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.4.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.4.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.6.2. Market Revenue and Forecast, by Application (2017-2030)
12.4.6.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.4.6.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.7.2. Market Revenue and Forecast, by Application (2017-2030)
12.4.7.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.4.7.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.8.2. Market Revenue and Forecast, by Application (2017-2030)
12.4.8.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.4.8.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.5.2. Market Revenue and Forecast, by Application (2017-2030)
12.5.5.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.5.5.4. Market Revenue and Forecast, by End User Type (2017-2030)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.6.2. Market Revenue and Forecast, by Application (2017-2030)
12.5.6.3. Market Revenue and Forecast, by Technology Type (2017-2030)
12.5.6.4. Market Revenue and Forecast, by End User Type (2017-2030)
Chapter 13. Company Profiles
13.1. Intel
13.1.1. Company Overview
13.1.2. Product Offerings
13.1.3. Financial Performance
13.1.4. Recent Initiatives
13.2. Koninklijke Philips
13.2.1. Company Overview
13.2.2. Product Offerings
13.2.3. Financial Performance
13.2.4. Recent Initiatives
13.3. Microsoft
13.3.1. Company Overview
13.3.2. Product Offerings
13.3.3. Financial Performance
13.3.4. Recent Initiatives
13.4. IBM
13.4.1. Company Overview
13.4.2. Product Offerings
13.4.3. Financial Performance
13.4.4. Recent Initiatives
13.5. Siemens Healthineers
13.5.1. Company Overview
13.5.2. Product Offerings
13.5.3. Financial Performance
13.5.4. Recent Initiatives
13.6. Nvidia
13.6.1. Company Overview
13.6.2. Product Offerings
13.6.3. Financial Performance
13.6.4. Recent Initiatives
13.7. Google
13.7.1. Company Overview
13.7.2. Product Offerings
13.7.3. Financial Performance
13.7.4. Recent Initiatives
13.8. General Electric Company
13.8.1. Company Overview
13.8.2. Product Offerings
13.8.3. Financial Performance
13.8.4. Recent Initiatives
13.9. Medtronic
13.9.1. Company Overview
13.9.2. Product Offerings
13.9.3. Financial Performance
13.9.4. Recent Initiatives
13.10. Micron Technology
13.10.1. Company Overview
13.10.2. Product Offerings
13.10.3. Financial Performance
13.10.4. Recent Initiatives
13.11. Amazon Web Services
13.11.1. Company Overview
13.11.2. Product Offerings
13.11.3. Financial Performance
13.11.4. Recent Initiatives
Chapter 14. Research Methodology
14.1. Primary Research
14.2. Secondary Research
14.3. Assumptions
Chapter 15. Appendix
15.1. About Us
15.2. Glossary of Terms
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