Artificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-aware Computing, Computer Vision), Application, End User and Geography (2021-2027)
[252 Pages Report] The artificial intelligence in healthcare market is estimated to generate USD 6.9 billion in 2021 and reach USD 67.4 billion by 2027; it is projected to grow at a CAGR of 46.2% during 2021 - 2027.
The primary factors driving the demand for artificial intelligence in healthcare sector includes the increasing concern towards cost optimization of healthcare and management of the data, increasing number of alliances between public and private organizations, and accelerated regional budget towards the healthcare sector. Moreover, the rising opportunities in geriatric population care with the help of AI tech and imaging & diagnostics to generate data for research development is expected to create market opportunities for artificial intelligence in healthcare market.
To know about the assumptions considered for the study, download the pdf brochure
Market Dynamics
Driver: Competitive Healthcare Cost and the need to optimize it
Globally, the cost of healthcare is on the rise due to factors such as the surging demand for healthcare services, increasing development of new expensive prescription drugs and medical technologies, rising prevalence of chronic diseases and operational inefficiencies, and growth rate of hospital readmissions and medical errors. In this case, healthcare providers need to optimize resource and asset allocation over time. This can include the allocation of medical devices, staffing of healthcare professionals, and other key aspects of operations. According to the Organization for Economic Cooperation and Development (OECD) estimates, 20% of the global healthcare spending is wasted across the world; the United States Institute of Medicine estimates this expenditure to be ~29%. According to the World Health Organization, global spending on healthcare was USD 8.3 trillion, or ~10% of the global GDP (~ USD 84.5 trillion) in 2020. The adoption of AI technologies may help in reducing healthcare spending as AI helps in reducing manual labor and avoiding inefficiencies caused by care delivery failures, overtreatment, and improper care delivery.
Recent advances in supercomputing and AI have enabled the delivery of more precise and personalized healthcare services to patients. There is relevant evidence supporting the adoption of AI frameworks for reducing healthcare costs while maintaining/improving the quality of care.
Therefore, the use of AI-based tools can result in potential savings for several end users in the healthcare industry, including patients, care providers, and healthcare payers. This would result in increasing their demand significantly in the coming years.
Restraint: Lack of skilled AI workforce and ambiguous regulatory guidelines for medical software
AI is a complex system, and for developing, managing, and implementing AI systems, companies require a workforce with certain skill sets. For instance, personnel dealing with AI systems should be aware of technologies such as cognitive computing, ML and machine intelligence, deep learning, and image recognition. In addition, the integration of AI solutions into existing systems is a challenging task requiring extensive data processing for replicating human brain behavior. Even a minor error can result in system failure or can adversely affect the desired result. Furthermore, the absence of professional standards and certifications in AI/ML technologies is restraining the growth of AI. Additionally, AI service providers are facing challenges regarding deploying/servicing their solutions at their customer sites. This is because of the lack of technology awareness and fewer AI experts.
Opportunity: Growing potential of AI-based tools for elderly care
With the growth in the geriatric population, the incidence of various age-related diseases is expected to increase across the world. To counter this and efficiently handle the growing burden on their respective healthcare systems, governments in several countries are increasingly focusing on adopting novel technologies. AI is one such technology that provides enhanced services, such as real-time patient data collection and monitoring for emergency care, as well as offers preventive healthcare recommendations. Moreover, AI-based tools can use health and wellness services, such as mobile applications, to monitor the movement and activities of patients. Also, the efficient implementation of in-home health monitoring and health information access, personalized health management, and the use of treatment devices (such as better hearing aids and visual assistive devices) and physical assistive devices (such as intelligent walkers) are possible with the implementation of AI-based tools. Thus, there is a growing interest in the use of
AI-based technologies to support the physical, emotional, social, and mental health of the elderly across the world.
Challenge: Lack of curated healthcare data
Data is a vital source to train and develop a complete and robust AI system. Earlier, datasets were mostly structured and entered manually. However, growing digital footprint and technology trends, such as IoT in healthcare, have resulted in the generation of a large volume of data from wearable devices, connected health monitors, EHRs, and various other remotely connected healthcare machines/devices. This data is largely unstructured and is in the form of text, voice, images, etc. The lack of an orderly internal structure limits developers to extract value. However, to train machine learning tools, developers require high-quality labeled data, along with skilled human trainers. Extracting and labeling unstructured data requires a large, skilled workforce and time. Moreover, patient information is extremely sensitive and is subject to stringent privacy norms. For instance, legislations such as the HIPAA (implemented in the US in 1996) and the HITECH Act (implemented in the US in 2003) require entities that are responsible for sensitive health information to implement certain measures to ensure its privacy and security; these entities are also required to inform patients of instances when the privacy and security of their information are compromised. This makes curated data hard to access due to privacy concerns, record identification concerns, and security requirements.
The hardware segment is projected to account for prominentshare in the artificial intelligence in healthcare market during the forecast period.
There is an increasing competition among established companies and startups in the market, leading to product launches and developments, including both hardware products and software solutions to run machine learning algorithms and various other software frameworks. NVIDIA (US), Intel (US), Micron (US), Xilinx (US), Google (US), General Vision (US), and Graphcore (UK) are among the companies that develop hardware for the AI in healthcare market. The increasing need for hardware platforms with high computing power to run various AI software is the key factor accelerating the growth of the AI in healthcare market for hardware devices.
The virtual assistant segment is expected to register the healthy CAGR.
Increasing need to disseminate precise medical information among vulnerable populations has resulted in growth of virtual assistants. AI-enabled health assistance and medical management tools use ML, big data, as well as NLP, and voice recognition to assist patients between doctor visits and provide a strong decision support system inpatient care.
To know about the assumptions considered for the study, download the pdf brochure
Asia Pacific is estimated to witness highest growth rate in the artificial intelligence in healthcare market during the stipulated period.
Increasing geriatric population in APAC countries is one of the major drivers for the growth of the AI in healthcare market. According to the UN’s World Population Aging 2019 report, 703 million persons were aged 65 years or over in the global population. The number is expected to reach 1.5 billion by 2050. The percentage of the population aged 65 years or above has almost doubled in Eastern and South-Eastern Asia, rising from 6% in 1990 to 11% in 2019. Among the high-income countries, Japan had the world’s highest old-age dependency ratio of 30% in 2019, which is expected to reach 81% by 2050.
Increasing adoption of AI technology across the continuum of care, especially in the US, and high healthcare spending combined with the onset of COVID-19 pandemic accelerating the adoption of AI in hospital and clinics across the region are the major factors driving the growth of the North American market.
Key Market Players
The artificial intelligence in healthcare marketcompanieshave adopted organic and inorganic growth strategies, such as product launches, andmergers &acquisitions to strengthen their offerings in the market. The major playersin the artificial intelligence in healthcare marketare Intel (US), Koninklijke Philips (Netherlands), Microsoft (US), IBM (US), Siemens Healthineers (Germany), Nvidia (US), Google (US), General Electric Company (US), Medtronic (US), Micron Technology (US), Amazon Web Services (US), Johnson & Johnson (US), General Vision (US), CloudmedX (US), Oncora Medical (US), Enlitic (US), Lunit (South Korea), Qure.ai (India), Precision Health AI (US), Cota (US), FDNA Inc. (US), Recursion Pharmaceuticals (US), Atomwise (US), Welltok (US), Babylon Health (UK), MDLIVE (Evernorth Group) (US), Gauss Surgical (US), Qventus (US), Desktop Genetics (US), Cylance (US), Ginger.io (US), and Pillo (US).
The study includes an in-depth competitive analysis of these key players in the artificial intelligence in healthcare market with their company profiles, recent developments, and key market strategies.
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|
Report Metric |
Details |
|
Years considered |
2018–2027 |
|
Base year considered |
2020 |
|
Forecast period |
2021–2027 |
|
CAGR |
46.2% |
|
Segments covered |
Component, Technology, Application, Vertical |
|
Regions covered |
North America, APAC, Europe, and RoW |
|
Companies covered |
Intel (US), Koninklijke Philips (Netherlands), Microsoft (US), IBM (US), Siemens Healthineers (Germany), Nvidia (US), Google (US), General Electric Company (US), Medtronic (US), Micron Technology (US), Amazon Web Services (US), Johnson & Johnson (US), General Vision (US), CloudmedX (US), Oncora Medical (US), Enlitic (US), Lunit (South Korea), Qure.ai (India), Precision Health AI (US), Cota (US), FDNA Inc. (US), Recursion Pharmaceuticals (US), Atomwise (US), Welltok (US), Babylon Health (UK), MDLIVE (Evernorth Group) (US), Gauss Surgical (US), Qventus (US), Desktop Genetics (US), Cylance (US), Ginger.io (US), and Pillo (US) |
In this report, the overall AI in healthcare market has been segmented based on offering, technology, application, end user and region.
By Offering:
- Hardware
- Software
- Services
By Technology:
- Machine Learning
- Natural Language Processing
- Context-aware Computing
- Computer Vision
By Application:
- Patient Data & Risk Analysis
- Inpatient Care & Hospital Management
- Medical Imaging & Diagnostics
- Lifestyle Management & Monitoring
- Virtual Assistants
- Drug Discovery
- Research
- Healthcare Assistant Robots
- Precision Medicine
- Emergency Room & Surgery
- Wearables
- Mental Health
- Cybersecurity
By End User:
- Hospital & Healthcare Providers
- Patients
- Pharmaceuticals & Biotechnology Companies
- Healthcare Payers
- Others
By Region
- North America
- US
- Canada
- Mexico
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia Pacific (APAC)
- China
- Japan
- India
- South Korea
- Rest of APAC
- Rest of the World (RoW)
- Middle East & Africa
- South America
Recent Developments
- In April 2021,Intel announced to launch a 3rd Gen Intel Xeon Scalable processor that will deliver a balanced architecture with built-in artificial intelligence, crypto acceleration, and advanced security capabilities.
- In January 2021,Intel announced the launch of the 11th Gen Intel Core vPro and Intel Evo vPro processor for high-performance and hardware-based security.
- In January 2021,Intel introduced a new N-Series 10-nanometer Intel PentiumSilver and IntelCeleronprocessor for media and collaboration for education systems.
- In October 2020,Microsoft announced the launch of C3 AI CRM powered by Microsoft Dynamic 365 along with Adobe (US) & C3.ai (US). This is the first enterprise-class, AI-First customer relationship management solution built for industries using Adobe Experience Cloud and drive customer-facing operations with predictive business insight.
- In October 2019,Microsoft released Cromwell on Azure, an open-source project on GitHub from Microsoft Genomics that provides scientific workflow management for genetic analysis.
Frequently Asked Questions (FAQ):
Who are the top 5 players in the artificial intelligence in healthcare market?
The major vendors operating in the industry market include are Intel (US), Koninklijke Philips (Netherlands), Microsoft (US), IBM (US), Siemens Healthineers (Germany)
What are their major strategies to strengthen their market presence?
The major strategies adopted by these players are acquisitions, product launches, and developments, partnerships and collaborations.
Which major countries are considered in the European region?
The report includes an analysis of the UK, Germany, France, Italy, Spain, and rest of European countries.
Which major countries are considered in the APAC region?
The report includes an analysis of the China, Japan, India, South Korea and rest of APAC countries.
Does this report include the impact of COVID-19 on the artificial intelligence in healthcare market?
Yes, the report includes the impact of COVID-19 on the artificial intelligence in healthcare market. It illustrates the post- COVID-19 market scenario. .
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 25)
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 GEOGRAPHIC SCOPE
1.3.3 YEARS CONSIDERED
1.4 CURRENCY
1.5 VOLUME UNIT CONSIDERED
1.6 LIMITATIONS
1.7 STAKEHOLDERS
1.8 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 31)
2.1 RESEARCH DATA
FIGURE 1 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN
2.1.1 SECONDARY AND PRIMARY RESEARCH
2.1.2 SECONDARY DATA
2.1.2.1 List of major secondary sources
2.1.2.2 Secondary sources
2.1.3 PRIMARY DATA
2.1.3.1 Primary interviews with experts
2.1.3.2 Key industry insights
2.1.3.3 Breakdown of primaries
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Estimating market size by bottom-up approach (demand side)
FIGURE 2 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
FIGURE 3 MARKET SIZE ESTIMATION METHODOLOGY: APPROACH 2 BOTTOM-UP (SUPPLY SIDE)—ILLUSTRATION OF REVENUE ESTIMATION OF COMPANIES FROM SALES OF AI IN HEALTHCARE OFFERING
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY: APPROACH 3—BOTTOM-UP (DEMAND SIDE) ESTIMATION OF SIZE OF AI IN HEALTHCARE MARKET, BY END USER
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Estimating market size by top-down approach (supply side)
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: APPROACH 1 (SUPPLY SIDE)—REVENUE GENERATED FROM AI IN HEALTHCARE OFFERINGS
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
FIGURE 7 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
FIGURE 8 ASSUMPTIONS FOR RESEARCH STUDY
2.5 RISK ASSESSMENT
TABLE 1 LIMITATIONS AND ASSOCIATED RISKS
2.6 LIMITATIONS
3 EXECUTIVE SUMMARY (Page No. - 44)
3.1 GROWTH RATE ASSUMPTIONS/GROWTH FORECAST
TABLE 2 GLOBAL AI IN HEALTHCARE MARKET, 2018–2020 (USD MILLION)
TABLE 3 GLOBAL MARKET, 2021–2027 (USD MILLION)
FIGURE 9 EFFECT OF COVID-19 ON MARKET
3.2 POST-COVID-19 SCENARIO
TABLE 4 POST-COVID-19 SCENARIO: MARKET, 2021–2027 (USD MILLION)
3.3 OPTIMISTIC SCENARIO (POST-COVID-19)
TABLE 5 OPTIMISTIC SCENARIO (POST-COVID-19): MARKET, 2021–2027 (USD MILLION)
3.4 PESSIMISTIC SCENARIO (POST-COVID-19)
TABLE 6 PESSIMISTIC SCENARIO (POST-COVID-19): MARKET, 2021–2027 (USD BILLION)
FIGURE 10 SOFTWARE SEGMENT TO LEAD MARKET, IN TERMS OF SIZE, FROM 2018 TO 2027
FIGURE 11 NORTH AMERICA TO HOLD LARGEST SHARE OF MARKET IN 2021
4 PREMIUM INSIGHTS (Page No. - 49)
4.1 ATTRACTIVE GROWTH OPPORTUNITIES IN AI IN HEALTHCARE MARKET
FIGURE 12 AVAILABILITY OF BIG DATA IN HEALTHCARE AND INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES ARE MAJOR FACTORS DRIVING MARKET GROWTH DURING 2021–2027
4.2 MARKET, BY TECHNOLOGY
FIGURE 13 MACHINE LEARNING TECHNOLOGY TO ACCOUNT FOR LARGEST SIZE OF MARKET FROM 2021 TO 2027
4.3 MARKET, BY TECHNOLOGY AND REGION
FIGURE 14 MACHINE LEARNING TECHNOLOGY AND NORTH AMERICA TO BE LARGEST SHAREHOLDERS OF MARKET IN IN 2021
4.4 MARKET, BY COUNTRY
FIGURE 15 MARKET IN CHINA AND MEXICO TO GROW AT HIGHEST CAGR FROM 2021 TO 2027
5 MARKET OVERVIEW (Page No. - 51)
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
FIGURE 16 AI IN HEALTHCARE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
5.2.1 DRIVERS
5.2.1.1 Influx of large and complex healthcare datasets
5.2.1.2 Growing need to reduce healthcare costs
5.2.1.3 Improving computing power and declining hardware cost
5.2.1.4 Rising number of partnerships and collaborations among different domains in healthcare sector
5.2.1.5 Surging need for improvised healthcare services due to imbalance between health workforce and patients
5.2.2 RESTRAINTS
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies
5.2.2.2 Lack of skilled AI workforce and ambiguous regulatory guidelines for medical software
5.2.3 OPPORTUNITIES
5.2.3.1 Growing potential of AI-based tools for elderly care
5.2.3.2 Increasing focus on developing human-aware AI systems
5.2.3.3 Rising potential of AI technology in genomics, drug discovery, and imaging & diagnostics to fight COVID-19
5.2.4 CHALLENGES
5.2.4.1 Lack of curated healthcare data
5.2.4.2 Concerns regarding data privacy
FIGURE 17 TYPES OF HEALTHCARE BREACHES REPORTED TO US DEPARTMENT OF HEALTH AND HUMAN SERVICES (2019 TO 2021)
5.2.4.3 Lack of interoperability between AI solutions offered by different vendors
5.3 VALUE CHAIN ANALYSIS
FIGURE 18 AI IN HEALTHCARE MARKET VALUE CHAIN IN 2020
5.4 PORTER’S FIVE FORCES ANALYSIS
TABLE 7 MARKET: PORTER’S FIVE FORCES ANALYSIS
5.5 ECOSYSTEM ANALYSIS
TABLE 8 ECOSYSTEM: MARKET
5.6 REVENUE SHIFT AND NEW REVENUE POCKETS FOR MARKET
FIGURE 19 YC–YCC SHIFT: MARKET
5.7 CASE STUDY ANALYSIS
5.7.1 USE CASE – BIOBEAT (ISRAEL)
5.7.2 USE CASE – CLEVELAND CLINIC AND MICROSOFT
5.7.3 USE CASE – GOVERNMENT OF INDIA, MICROSOFT, AND ACCENTURE
5.8 TECHNOLOGY ANALYSIS
5.8.1 CLOUD COMPUTING
5.8.2 CLOUD GPU
5.9 PRICING ANALYSIS
FIGURE 20 ASP OF PROCESSOR COMPONENTS, MARKET, 2018–2027 (USD)
TABLE 9 ASP RANGE OF PROCESSOR COMPONENTS IN MARKET, 2018–2027
TABLE 10 ASP RANGE OF SERVER SOFTWARE IN MARKET
5.10 TRADE ANALYSIS
TABLE 11 EXPORTS DATA, BY COUNTRY, 2016–2020
FIGURE 21 EXPORTS DATA FOR HS CODE 854231 FOR TOP COUNTRIES IN AI IN HEALTHCARE MARKET, 2016–2020 (THOUSAND UNITS)
TABLE 12 IMPORTS DATA, BY COUNTRY, 2016–2020
FIGURE 22 IMPORTS DATA FOR HS CODE 854231 FOR TOP COUNTRIES IN MARKET, 2016–2020 (THOUSAND UNITS)
5.11 PATENT ANALYSIS
FIGURE 23 PATENT ANALYSIS: MARKET
TABLE 13 LIST OF PATENTS
5.12 REGULATORY LANDSCAPE
TABLE 14 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS ARE EXPORTED BY US, 2020
TABLE 15 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS ARE EXPORTED BY CHINA, 2020
TABLE 16 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS EXPORTED BY GERMANY, 2020
5.12.1 REGULATIONS
5.12.1.1 Export-import regulations
5.12.2 RESTRICTION OF HAZARDOUS SUBSTANCES (ROHS) AND WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT (WEEE)
5.12.3 REGISTRATION, EVALUATION, AUTHORIZATION, AND RESTRICTION OF CHEMICALS (REACH)
5.12.4 GENERAL DATA PROTECTION REGULATION (GDPR)
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING (Page No. - 80)
6.1 INTRODUCTION
FIGURE 24 AI IN HEALTHCARE MARKET, BY OFFERING
FIGURE 25 SOFTWARE TO HOLD LARGEST SIZE OF MARKET DURING FORECAST PERIOD
TABLE 17 MARKET, BY OFFERING, 2018–2020 (USD MILLION)
TABLE 18 MARKET, BY OFFERING, 2021–2027 (USD MILLION)
6.2 HARDWARE
6.2.1 PROCESSORS, MEMORY DEVICES, AND NETWORK SYSTEMS ARE INALIENABLE HARDWARE COMPONENTS OF AI IN HEALTHCARE ECOSYSTEM
TABLE 19 MARKET, BY HARDWARE, 2018–2020 (USD MILLION)
TABLE 20 MARKET, BY HARDWARE, 2021–2027 (USD MILLION)
TABLE 21 MARKET FOR HARDWARE, BY REGION, 2018–2020 (USD MILLION)
TABLE 22 MARKET FOR HARDWARE, BY REGION, 2021–2027 (USD MILLION)
6.2.2 PROCESSOR
6.2.2.1 Intel (US), Nvidia (US), and Xilinx (US) are key providers of hardware components for AI applications
TABLE 23 MARKET, BY PROCESSOR TYPE, 2018–2020 (MILLION UNITS)
TABLE 24 MARKET, BY PROCESSOR TYPE, 2021–2027 (MILLION UNITS)
TABLE 25 MARKET, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 26 MARKET, BY PROCESSOR TYPE, 2021–2027 (USD MILLION)
6.2.2.2 MPU/CPU
6.2.2.2.1 Use case: Dynalife and Altaml colon polyp project
6.2.2.3 GPU
6.2.2.3.1 Use case: University of Sydney, brain and mind center (SNAC) and Nvidia
6.2.2.4 FPGA
6.2.2.4.1 Use case: Xilinx and spline. AI
6.2.2.5 ASIC
6.2.2.5.1 Neureality: NR1-p Soc’s
6.2.3 MEMORY
6.2.3.1 High-bandwidth memory is being developed and deployed for AI applications, independent of its computing architecture
6.2.3.2 Case study: INTEL, DELL, and university of florida
6.2.4 NETWORK
6.2.4.1 Nvidia (US) and Intel (US) are key providers of network interconnect adapters for AI applications
6.2.4.2 Recent development:
TABLE 27 AI IN HEALTHCARE MARKET, BY NETWORK, 2018–2020 (MILLION UNITS)
TABLE 28 MARKET, BY NETWORK, 2021–2027 (MILLION UNITS)
TABLE 29 MARKET, BY NETWORK, 2018–2020 (USD MILLION)
TABLE 30 MARKET, BY NETWORK, 2021–2027 (USD MILLION)
6.3 SOFTWARE
6.3.1 SOFTWARE SEGMENT HOLDS LARGEST SHARE IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET
TABLE 31 MARKET, BY SOFTWARE TYPE, 2018–2020 (USD MILLION)
TABLE 32 MARKET, BY SOFTWARE TYPE, 2021–2027 (USD MILLION)
TABLE 33 MARKET FOR SOFTWARE, BY REGION, 2018–2020 (USD MILLION)
TABLE 34 MARKET FOR SOFTWARE, BY REGION, 2021–2027 (USD MILLION)
6.3.2 AI SOLUTION
6.3.2.1 Case study: VA Hospital (TEXAS) and DeepMind health
TABLE 35 MARKET FOR AI SOLUTIONS, BY DEPLOYMENT MODE, 2018–2020 (USD MILLION)
TABLE 36 MARKET FOR AI SOLUTIONS, BY DEPLOYMENT MODE, 2021–2027 (USD MILLION)
6.3.2.2 On premises
6.3.2.2.1 Data-sensitive enterprises prefer advanced on-premises NLP and ML tools for use in AI solutions
6.3.2.2.2 Recent development:
6.3.2.3 Cloud
6.3.2.3.1 Cloud provides additional flexibility for business operations and real-time deployment ease to companies that are implementing real-time analytics
6.3.2.3.2 Case study: Google and Portal Telemedicina
6.3.3 AI PLATFORM
TABLE 37 MARKET FOR SOFTWARE, BY AI PLATFORM, 2018–2020 (USD MILLION)
TABLE 38 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR SOFTWARE, BY AI PLATFORM, 2021–2027 (USD MILLION)
6.3.3.1 Machine learning framework
6.3.3.2 Application program interface
6.3.3.2.1 Case study: AWS and Caremonitor
6.4 SERVICES
6.4.1 BIG TECHNOLOGY COMPANIES SUCH AS MICROSOFT (US), AND GOOGLE (US) ARE PROVIDING CLOUD SERVICES FOR AI IN HEALTHCARE APPLICATIONS
TABLE 39 MARKET, BY SERVICE TYPE, 2018–2020 (USD MILLION)
TABLE 40 MARKET, BY SERVICE TYPE, 2021–2027 (USD MILLION)
TABLE 41 MARKET FOR SERVICES, BY REGION, 2018–2020 (USD MILLION)
TABLE 42 AI IN HEALTHCARE MARKET FOR SERVICES, BY REGION, 2021–2027 (USD MILLION)
6.4.2 DEPLOYMENT & INTEGRATION
6.4.3 SUPPORT & MAINTENANCE
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY (Page No. - 97)
7.1 INTRODUCTION
FIGURE 26 MARKET, BY TECHNOLOGY
FIGURE 27 MACHINE LEARNING TECHNOLOGY TO HOLD LARGEST SIZE OF MARKET DURING FORECAST PERIOD
TABLE 43 MARKET, BY TECHNOLOGY, 2018–2020 (USD MILLION)
TABLE 44 MARKET, BY TECHNOLOGY, 2021–2027 (USD MILLION)
7.2 MACHINE LEARNING
7.2.1 CASE STUDY: MAYO CLINIC AND GOOGLE
TABLE 45 MARKET FOR MACHINE LEARNING, BY TYPE, 2018–2020 (USD MILLION)
TABLE 46 MARKET FOR MACHINE LEARNING, BY TYPE, 2021–2027 (USD MILLION)
7.2.2 DEEP LEARNING
7.2.2.1 Deep learning enables machines to build hierarchical representations
7.2.2.2 Case study: Johns Hopkins university and Google
7.2.3 SUPERVISED LEARNING
7.2.3.1 Classification and regression are major segments of supervised learning
7.2.4 REINFORCEMENT LEARNING
7.2.4.1 Reinforcement learning allows systems and software to determine ideal behavior for maximizing performance of systems
7.2.5 UNSUPERVISED LEARNING
7.2.5.1 Unsupervised learning includes clustering methods consisting of algorithms with unlabeled training data
7.2.6 OTHERS
7.3 NATURAL LANGUAGE PROCESSING
7.3.1 NLP IS WIDELY USED BY CLINICAL AND RESEARCH COMMUNITIES IN HEALTHCARE
7.3.2 CASE STUDY: ROCHE AND JOHN SNOW LABS
7.3.3 CASE STUDY: DEEP 6 AI AND JOHN SNOW LABS
TABLE 47 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR NATURAL LANGUAGE PROCESSING, BY TYPE, 2018–2020 (USD MILLION)
TABLE 48 MARKET FOR NATURAL LANGUAGE PROCESSING, BY TYPE, 2021–2027 (USD MILLION)
7.4 CONTEXT-AWARE COMPUTING
7.4.1 DEVELOPMENT OF MORE SOPHISTICATED HARD AND SOFT SENSORS HAS ACCELERATED GROWTH OF CONTEXT-AWARE COMPUTING
7.4.2 RECENT DEVELOPMENT: PEGASYSTEMS
TABLE 49 MARKET FOR CONTEXT-AWARE COMPUTING, BY TYPE, 2018–2020 (USD MILLION)
TABLE 50 MARKET FOR CONTEXT-AWARE COMPUTING, BY TYPE, 2021–2027 (USD MILLION)
7.5 COMPUTER VISION
7.5.1 COMPUTER VISION TECHNOLOGY HAS SIGNIFICANT APPLICATIONS IN SURGERY AND THERAPY
7.5.2 RECENT DEVELOPMENT: NORTHWESTERN MEMORIAL HOSPITAL AND NVIDIA
8 MARKET, BY APPLICATION (Page No. - 107)
8.1 INTRODUCTION
FIGURE 28 MARKET, BY APPLICATION
FIGURE 29 MEDICAL IMAGING & DIAGNOSTICS TO ACCOUNT FOR LARGEST SIZE OF MARKET IN 2027
TABLE 51 MARKET, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 52 AI IN HEALTHCARE MARKET, BY APPLICATION, 2021–2027 (USD MILLION)
8.2 PATIENT DATA & RISK ANALYSIS
8.2.1 GROWTH IN HEALTHCARE DATASETS AND PATIENTS’ HISTORIES HAS LED TO ADOPTION OF PATIENT DATA & RISK ANALYSIS SOLUTIONS
8.2.2 USE CASE: UNIVERSITY OF NOTTINGHAM
8.2.3 USE CASE: CLEVELAND CLINIC AND MICROSOFT
TABLE 53 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY REGION, 2018–2020 (USD MILLION)
TABLE 54 MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY REGION, 2021–2027 (USD MILLION)
TABLE 55 MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY END USER, 2018–2020 (USD MILLION)
TABLE 56 MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY END USER, 2021–2027 (USD MILLION)
8.3 INPATIENT CARE & HOSPITAL MANAGEMENT
8.3.1 EXCESSIVE OPERATIONAL COSTS TO GENERATE DEMAND FOR AI-BASED INPATIENT CARE AND HOSPITAL MANAGEMENT SOLUTIONS
8.3.2 USE CASE: QURE.AI AND ROYAL BOLTON HOSPITAL
TABLE 57 MARKET FOR INPATIENT CARE & HOSPITAL MANAGEMENT, BY REGION, 2018–2020 (USD MILLION)
TABLE 58 MARKET FOR INPATIENT CARE & HOSPITAL MANAGEMENT, BY REGION, 2021–2027 (USD MILLION)
TABLE 59 MARKET FOR INPATIENT CARE AND HOSPITAL MANAGEMENT, BY END USER, 2018–2020 (USD MILLION)
TABLE 60 MARKET FOR INPATIENT CARE AND HOSPITAL MANAGEMENT, BY END USER, 2021–2027 (USD MILLION)
8.4 MEDICAL IMAGING & DIAGNOSTICS
8.4.1 LEVERAGING CAPABILITIES OF HIGH-END GPU IN GENERATING HIGHLY ACCURATE IMAGING DATA HAS LED TO GROWTH OF AI-BASED MEDICAL IMAGING & DIAGNOSTICS SOLUTIONS
8.4.2 USE CASE: ULTRONICS, ZEBRA MEDICAL VISION, AI2, AND FUJIFILM SONOSITE
TABLE 61 MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY REGION, 2018–2020 (USD MILLION)
TABLE 62 MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY REGION, 2021–2027 (USD MILLION)
TABLE 63 MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY END USER, 2018–2020 (USD MILLION)
TABLE 64 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY END USER, 2021–2027 (USD MILLION)
8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING
8.5.1 GROWTH IN AI-DRIVEN REMOTE PATIENT MONITORING SOLUTIONS SUCH AS MEDICAL WEARABLES HAS HELPED DOCTORS REDUCE BURDEN ON HOSPITALS DURING COVID-19 PANDEMIC
8.5.2 RECENT DEVELOPMENTS:
TABLE 65 AI IN HEALTHCARE MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY REGION, 2018–2020 (USD MILLION)
TABLE 66 MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY REGION, 2021–2027 (USD MILLION)
TABLE 67 MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY END USER, 2018–2020 (USD MILLION)
TABLE 68 MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY END USER, 2021–2027 (USD MILLION)
8.6 VIRTUAL ASSISTANTS
8.6.1 INCREASING NEED TO DISSEMINATE PRECISE MEDICAL INFORMATION AMONG VULNERABLE POPULATIONS HAS RESULTED IN GROWTH OF VIRTUAL ASSISTANTS
8.6.2 KEY DEVELOPMENTS:
8.6.3 CASE STUDY: GOVERNMENT OF INDIA
TABLE 69 MARKET FOR VIRTUAL ASSISTANT, BY REGION, 2018–2020 (USD MILLION)
TABLE 70 MARKET FOR VIRTUAL ASSISTANT, BY REGION, 2021–2027 (USD MILLION)
TABLE 71 MARKET FOR VIRTUAL ASSISTANT, BY END USER, 2018–2020 (USD MILLION)
TABLE 72 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANT, BY END USER, 2021–2027 (USD MILLION)
8.7 DRUG DISCOVERY
8.7.1 AI IS EXPECTED TO REDUCE TIME AND COST INVOLVED IN DRUG DISCOVERY
8.7.2 RECENT DEVELOPMENTS:
TABLE 73 MARKET FOR DRUG DISCOVERY, BY REGION, 2018–2020 (USD MILLION)
TABLE 74 MARKET FOR DRUG DISCOVERY, BY REGION, 2021–2027 (USD MILLION)
TABLE 75 MARKET FOR DRUG DISCOVERY, BY END USER, 2018–2020 (USD MILLION)
TABLE 76 MARKET FOR DRUG DISCOVERY, BY END USER, 2021–2027 (USD MILLION)
8.8 RESEARCH
8.8.1 USE OF AI ALGORITHMS BY BIOINFORMATICS RESEARCHERS FOR DATABASE CLASSIFICATION AND MINING DRIVES ADOPTION OF AI IN RESEARCH
8.8.2 USE CASES: NUMEDII, 4QUANT, AND DESKTOP GENETICS
TABLE 77 MARKET FOR RESEARCH, BY REGION, 2018–2020 (USD MILLION)
TABLE 78 MARKET FOR RESEARCH, BY REGION, 2021–2027 (USD MILLION)
TABLE 79 MARKET FOR RESEARCH, BY END USER, 2018–2020 (USD MILLION)
TABLE 80 AI IN HEALTHCARE MARKET FOR RESEARCH, BY END USER, 2021–2027 (USD MILLION)
8.9 HEALTHCARE ASSISTANCE ROBOTS
8.9.1 HEALTHCARE ASSISTANT ROBOTS HAVE SIGNIFICANTLY HELPED IN REDUCING NEED FOR ROUND THE CLOCK MANUAL NURSING CARE
8.9.2 USE CASES: MAYO CLINIC
TABLE 81 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY REGION, 2018–2020 (USD MILLION)
TABLE 82 MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY REGION, 2021–2027 (USD MILLION)
TABLE 83 MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY END USER, 2018–2020 (USD MILLION)
TABLE 84 MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY END USER, 2021–2027 (USD MILLION)
8.10 PRECISION MEDICINE
8.10.1 AI IS EXPECTED TO FULFIL DEMAND FOR PERSONALIZED TREATMENT PLANS FOR PATIENTS ADMINISTERED WITH PRECISION MEDICINE
8.10.2 RECENT DEVELOPMENTS:
TABLE 85 MARKET FOR PRECISION MEDICINE, BY REGION, 2018–2020 (USD MILLION)
TABLE 86 MARKET FOR PRECISION MEDICINE, BY REGION, 2021–2027 (USD MILLION)
TABLE 87 MARKET FOR PRECISION MEDICINE, BY END USER, 2018–2020 (USD MILLION)
TABLE 88 MARKET FOR PRECISION MEDICINE, BY END USER, 2021–2027 (USD MILLION)
8.11 EMERGENCY ROOM & SURGERY
8.11.1 LIMITED AVAILABILITY OF SKILLED WORKFORCE IN EMERGENCY ROOMS AND DEMAND TO SUPPORT CLINICIANS WITH SURGICAL DATA TO DRIVE ADOPTION OF AI IN EMERGENCY ROOM AND SURGERY
8.11.2 USE CASE: THE HOSPITAL FOR SICK CHILDREN (TORONTO, CANADA)
TABLE 89 MARKET FOR EMERGENCY ROOM & SURGERY, BY REGION, 2018–2020 (USD MILLION)
TABLE 90 MARKET FOR EMERGENCY ROOM & SURGERY, BY REGION, 2021–2027 (USD MILLION)
TABLE 91 MARKET FOR EMERGENCY ROOM & SURGERY, BY END USER, 2018–2020 (USD MILLION)
TABLE 92 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY END USER, 2021–2027 (USD MILLION)
8.12 WEARABLES
8.12.1 REAL-TIME PATIENT MONITORING AND GENERATION OF DATA OF VITAL SIGNS DIAGNOSTICS TO BOOST DEMAND FOR AI IN WEARABLES
8.12.2 USE CASE: KENSCI AND MICROSOFT
TABLE 93 MARKET FOR WEARABLES, BY REGION, 2018–2020 (USD MILLION)
TABLE 94 MARKET FOR WEARABLES, BY REGION, 2021–2027 (USD MILLION)
TABLE 95 MARKET FOR WEARABLES, BY END USER, 2018–2020 (USD MILLION)
TABLE 96 MARKET FOR WEARABLES, BY END USER, 2021–2027 (USD MILLION)
8.13 MENTAL HEALTH
8.13.1 USE OF AI IN DIAGNOSING MENTAL DISTRESS AND NEUROLOGICAL ABNORMALITIES HAS LED TO GROWING ADOPTION OF AI IN MENTAL HEALTH
8.13.2 USE CASE: COGNOA
TABLE 97 MARKET FOR MENTAL HEALTH, BY REGION, 2018–2020 (USD MILLION)
TABLE 98 MARKET FOR MENTAL HEALTH, BY REGION, 2021–2027 (USD MILLION)
TABLE 99 MARKET FOR MENTAL HEALTH, BY END USER, 2018–2020 (USD MILLION)
TABLE 100 MARKET FOR MENTAL HEALTH, BY END USER, 2021–2027 (USD MILLION)
8.14 CYBERSECURITY
8.14.1 AI IN HEALTHCARE CYBERSECURITY IS BECOMING CRITICAL FOR PROTECTING ONSITE SYSTEMS
TABLE 101 AI IN HEALTHCARE MARKET FOR CYBERSECURITY, BY REGION, 2018–2020 (USD MILLION)
TABLE 102 MARKET FOR CYBERSECURITY, BY REGION, 2021–2027 (USD MILLION)
TABLE 103 MARKET FOR CYBERSECURITY, BY END USER, 2018–2020 (USD MILLION)
TABLE 104 MARKET FOR CYBERSECURITY, BY END USER, 2021–2027 (USD MILLION)
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER (Page No. - 134)
9.1 INTRODUCTION
FIGURE 30 MARKET, BY END USER
FIGURE 31 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE DURING FORECAST PERIOD
TABLE 105 MARKET, BY END USER, 2018–2020 (USD MILLION)
TABLE 106 MARKET, BY END USER, 2021–2027 (USD MILLION)
9.2 HOSPITALS AND HEALTHCARE PROVIDERS
9.2.1 AI CAN BE UTILIZED TO PREDICT AND PREVENT READMISSIONS AND IMPROVE OPERATIONS OF HOSPITALS AND CARE PROVIDERS
9.2.2 RECENT DEVELOPMENTS:
TABLE 107 MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 108 MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY APPLICATION, 2021–2027 (USD MILLION)
TABLE 109 MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY REGION, 2018–2020 (USD MILLION)
TABLE 110 MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY REGION, 2021–2027 (USD MILLION)
9.3 PATIENTS
9.3.1 INCREASING POPULARITY OF SMARTPHONE APPLICATIONS AND WEARABLES TO DRIVE ADOPTION OF AI AMONG PATIENTS
9.3.2 USE CASE: KENSCI AND MICROSOFT
9.3.3 USE CASE: BIOBEAT
TABLE 111 MARKET FOR PATIENTS, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 112 MARKET FOR PATIENTS, BY APPLICATION, 2021–2027 (USD MILLION)
TABLE 113 MARKET FOR PATIENTS, BY REGION, 2018–2020 (USD MILLION)
TABLE 114 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR PATIENTS, BY REGION, 2021–2027 (USD MILLION)
9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES
9.4.1 APPLICATIONS SUCH AS DRUG DISCOVERY, PRECISION MEDICINE, AND RESEARCH ARE EXPECTED TO DRIVE USE OF AI BY PHARMACEUTICALS AND BIOTECHNOLOGY COMPANIES
9.4.2 RECENT DEVELOPMENTS:
TABLE 115 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 116 MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY APPLICATION, 2021–2027 (USD MILLION)
TABLE 117 MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY REGION, 2018–2020 (USD MILLION)
TABLE 118 MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY REGION, 2021–2027 (USD MILLION)
9.5 HEALTHCARE PAYERS
9.5.1 HEALTHCARE PAYERS USE AI TOOLS MAINLY FOR MANAGING RISKS, IDENTIFYING CLAIM TRENDS, AND MAXIMIZING PAYMENT ACCURACY
9.5.2 RECENT DEVELOPMENTS:
TABLE 119 MARKET FOR HEALTHCARE PAYERS, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 120 MARKET FOR HEALTHCARE PAYERS, BY APPLICATION, 2021–2027 (USD MILLION)
TABLE 121 MARKET FOR HEALTHCARE PAYERS, BY REGION, 2018–2020 (USD MILLION)
TABLE 122 MARKET FOR HEALTHCARE PAYERS, BY REGION, 2021–2027 (USD MILLION)
9.6 OTHERS
TABLE 123 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR OTHERS, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 124 MARKET FOR OTHERS, BY APPLICATION, 2021–2027 (USD MILLION)
TABLE 125 MARKET FOR OTHERS, BY REGION, 2018–2020 (USD MILLION)
TABLE 126 MARKET FOR OTHERS, BY REGION, 2021–2027 (USD MILLION)
10 MARKET, GEOGRAPHIC ANALYSIS (Page No. - 148)
10.1 INTRODUCTION
FIGURE 32 CHINA AND US ARE EMERGING AS NEW HOTSPOTS FOR MARKET
FIGURE 33 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
TABLE 127 MARKET, BY REGION, 2018–2020 (USD MILLION)
TABLE 128 MARKET, BY REGION, 2021–2027 (USD MILLION)
10.2 NORTH AMERICA
FIGURE 34 NORTH AMERICA: SNAPSHOT OF AI IN HEALTHCARE MARKET
TABLE 129 MARKET IN NORTH AMERICA, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 130 MARKET IN NORTH AMERICA, BY COUNTRY, 2021–2027 (USD MILLION)
TABLE 131 MARKET IN NORTH AMERICA, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 132 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN NORTH AMERICA, BY APPLICATION, 2021–2027 (USD MILLION)
10.2.1 US
10.2.1.1 High healthcare spending combined with increasing demand for AI in medical sector due to COVID-19 pandemic to complement growth of AI market in US
10.2.1.2 Recent developments:
10.2.2 CANADA
10.2.2.1 Continuous research on NLP and ML across research institutions and universities in Canada to propel AI in healthcare market
10.2.2.2 Recent developments:
10.2.3 MEXICO
10.2.3.1 AI-enabled devices for healthcare sector have been gaining traction in Mexico
10.2.3.2 Recent developments:
10.3 EUROPE
FIGURE 35 EUROPE: SNAPSHOT OF MARKET
TABLE 133 MARKET IN EUROPE, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 134 MARKET IN EUROPE, BY COUNTRY, 2021–2027 (USD MILLION)
TABLE 135 MARKET IN EUROPE, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 136 MARKET IN EUROPE, BY APPLICATION, 2021–2027 (USD MILLION)
10.3.1 GERMANY
10.3.1.1 Government initiatives to expedite AI development supporting market growth in Germany
10.3.1.2 Recent developments:
10.3.2 UK
10.3.2.1 Adoption of AI in drug discovery space is fueling growth of AI in healthcare market in UK
10.3.2.2 Recent developments:
10.3.3 FRANCE
10.3.3.1 Government endeavors to develop healthcare IT in France is likely to support market
10.3.3.2 Recent developments:
10.3.4 ITALY
10.3.4.1 Development of electronic health records and aging population are driving market growth in Italy
10.3.4.2 Recent developments:
10.3.5 SPAIN
10.3.5.1 Growing awareness of AI in Spain is favoring market growth
10.3.5.2 Recent developments:
10.3.6 REST OF EUROPE
10.4 APAC
FIGURE 36 APAC: SNAPSHOT OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET
TABLE 137 MARKET IN APAC, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 138 MARKET IN APAC, BY COUNTRY, 2021–2027 (USD MILLION)
TABLE 139 MARKET IN APAC, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 140 MARKET IN APAC, BY APPLICATION, 2021–2027 (USD MILLION)
10.4.1 CHINA
10.4.1.1 Concrete government measures to accelerate AI development are fueling market growth in China
10.4.1.2 Recent developments:
10.4.2 JAPAN
10.4.2.1 AI applications to expedite drug discovery is motivating growth of AI in healthcare market in Japan
10.4.3 SOUTH KOREA
10.4.3.1 Quality healthcare services and rapid expansion of medical insurance coverage are motivating growth of market in South Korea
10.4.3.2 Recent developments:
10.4.4 INDIA
10.4.4.1 Developing IT infrastructure and AI-friendly government initiatives supporting growth of market in India
10.4.4.2 Recent developments:
10.4.5 REST OF ASIA PACIFIC
10.4.5.1 Recent developments:
10.5 ROW
FIGURE 37 ROW: SNAPSHOT OF AI IN HEALTHCARE MARKET
TABLE 141 MARKET IN ROW, BY REGION, 2018–2020 (USD MILLION)
TABLE 142 MARKET IN ROW, BY REGION, 2021–2027 (USD MILLION)
TABLE 143 MARKET IN ROW, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 144 MARKET IN ROW, BY APPLICATION, 2021–2027 (USD MILLION)
10.5.1 SOUTH AMERICA
10.5.1.1 Heavy investments in healthcare IT are driving growth of market in South America
10.5.2 MIDDLE EAST AND AFRICA
10.5.2.1 Growing healthcare expenditure in Middle East and North Africa is fostering growth of market
10.5.2.2 Recent developments:
11 COMPETITIVE LANDSCAPE (Page No. - 176)
11.1 KEY PLAYER STRATEGIES/RIGHT TO WIN
11.2 OVERVIEW
11.3 TOP 5 COMPANY REVENUE ANALYSIS
FIGURE 38 3 YEARS REVENUE ANALYSIS OF TOP 5 PLAYERS IN AI IN HEALTHCARE MARKET
11.4 MARKET SHARE ANALYSIS (2020)
TABLE 145 MARKET: MARKET SHARE ANALYSIS
11.5 COMPANY EVALUATION QUADRANT, 2020
11.5.1 STAR
11.5.2 PERVASIVE
11.5.3 EMERGING LEADER
11.5.4 PARTICIPANT
FIGURE 39 MARKET (GLOBAL) COMPANY EVALUATION QUADRANT, 2020
11.6 SMALL AND MEDIUM ENTERPRISES (SME) EVALUATION QUADRANT, 2020
11.6.1 PROGRESSIVE COMPANY
11.6.2 RESPONSIVE COMPANY
11.6.3 DYNAMIC COMPANY
11.6.4 STARTING BLOCK
FIGURE 40 AI IN HEALTHCARE MARKET (GLOBAL), SME EVALUATION QUADRANT, 2020
TABLE 146 MARKET: COMPANY FOOTPRINT
TABLE 147 COMPANY END USER FOOTPRINT
TABLE 148 COMPANY APPLICATION FOOTPRINT
TABLE 149 COMPANY REGION FOOTPRINT
11.7 COMPETITIVE SCENARIO
TABLE 150 MARKET: PRODUCT LAUNCHES AND DEVELOPMENTS, MARCH 2019 TO OCTOBER 2021
TABLE 151 MARKET: DEALS, MARCH 2019−OCTOBER 2021
12 COMPANY PROFILES (Page No. - 189)
(Business overview, Products/solutions/services offered, Recent developments & MnM View)*
12.1 KEY PLAYERS
12.1.1 INTEL
TABLE 152 INTEL: BUSINESS OVERVIEW
FIGURE 41 INTEL: COMPANY SNAPSHOT
12.1.2 KONINKLIJKE PHILIPS
TABLE 153 KONINKLIJKE PHILIPS: BUSINESS OVERVIEW
FIGURE 42 KONINKLIJKE PHILIPS: COMPANY SNAPSHOT
12.1.3 MICROSOFT
TABLE 154 MICROSOFT: BUSINESS OVERVIEW
FIGURE 43 MICROSOFT: COMPANY SNAPSHOT
12.1.4 IBM
TABLE 155 IBM: BUSINESS OVERVIEW
FIGURE 44 IBM: COMPANY SNAPSHOT
12.1.5 SIEMENS HEALTHINEERS
TABLE 156 SIEMENS HEALTHINEERS: BUSINESS OVERVIEW
FIGURE 45 SIEMENS HEALTHINEERS: COMPANY SNAPSHOT
12.1.6 NVIDIA
TABLE 157 NVIDIA: BUSINESS OVERVIEW
FIGURE 46 NVIDIA: COMPANY SNAPSHOT
12.1.7 GOOGLE
TABLE 158 GOOGLE: BUSINESS OVERVIEW
FIGURE 47 GOOGLE: COMPANY SNAPSHOT
12.1.8 GENERAL ELECTRIC (GE) COMPANY
TABLE 159 GENERAL ELECTRIC: BUSINESS OVERVIEW
FIGURE 48 GENERAL ELECTRIC: COMPANY SNAPSHOT
12.1.9 MEDTRONIC
TABLE 160 MEDTRONIC: BUSINESS OVERVIEW
FIGURE 49 MEDTRONIC: COMPANY SNAPSHOT
12.1.10 MICRON TECHNOLOGY
TABLE 161 MICRON TECHNOLOGY: BUSINESS OVERVIEW
FIGURE 50 MICRON TECHNOLOGY: COMPANY SNAPSHOT
12.1.11 AMAZON WEB SERVICES (AWS)
TABLE 162 AMAZON WEB SERVICES (AWS): BUSINESS OVERVIEW
FIGURE 51 AMAZON WEB SERVICES: COMPANY SNAPSHOT
12.1.12 JOHNSON & JOHNSON
TABLE 163 JOHNSON & JOHNSON: BUSINESS OVERVIEW
FIGURE 52 JOHNSON & JOHNSON: COMPANY SNAPSHOT
*Details on Business overview, Products/solutions/services offered, Recent developments & MnM View might not be captured in case of unlisted companies.
12.2 STARTUP ECOSYSTEM
12.2.1 GENERAL VISION
12.2.2 CLOUDMEDX
12.2.3 ONCORA MEDICAL
12.2.4 ENLITIC
12.2.5 LUNIT
12.2.6 QURE.AI
12.2.7 PRECISION HEALTH AI
12.2.8 COTA
12.2.9 FDNA
12.2.10 RECURSION PHARMACEUTICALS
12.2.11 ATOMWISE
12.2.12 WELLTOK
12.2.13 BABYLON HEALTH
12.2.14 MDLIVE (EVERNORTH GROUP)
12.2.15 GAUSS SURGICAL
12.2.16 QVENTUS
12.2.17 DESKTOP GENETICS
12.2.18 CYLANCE
12.2.19 GINGER.IO
12.2.20 PILLO
13 APPENDIX (Page No. - 244)
13.1 INSIGHTS OF INDUSTRY EXPERTS
13.2 DISCUSSION GUIDE
13.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
13.4 AVAILABLE CUSTOMIZATIONS
13.5 RELATED REPORTS
13.6 AUTHOR DETAILS
The study involves four major activities for estimating the size of the artificial intelligence in healthcare market. Exhaustive secondary research has been conducted to collect information related to the market. The next step has been the validation of these findings, assumptions, and sizing with the industry experts across the value chain through primary research. Top-down and bottom-up approaches have been used to estimate and validate the size of the artificial intelligence in healthcare market and other dependent submarkets. The leading players in the market have been identified through secondary research, and their market share in the key regions has been determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top players and extensive interviews with the industry experts such as chief executive officers, vice presidents, directors, and marketing executives for the key insights.
Secondary Research
In the secondary research process, various secondary sources have been referred to for identifying and collecting information important for this study. Secondary sources such as the Association for the Advancement of Artificial Intelligence (AAAI), European Association for Artificial Intelligence (EurAI), AI Association of Patent and Trademark Attorneys (AIPAT), Data Science Association, International Association for Artificial Intelligence and Law (IAAIL), German Research Center for Artificial Intelligence (DFKI), Swedish Artificial Intelligence Society, Chinese Association for Artificial Intelligence, Artificial Intelligence Association of India, Pattern Recognition and Machine Intelligence Association (PREMIA-Singapore), and The Israeli Association for Artificial Intelligence have been used to identify and collect information for an extensive technical and commercial study of the artificial intelligence in healthcare market.
Primary Research
Extensive primary research was conducted after understanding and analyzing the AI in healthcare market through secondary research. Several primary interviews were conducted with key opinion leaders from both the demand- and supply-side vendors across four major regions—North America, Europe, APAC, and RoW. RoW comprises the Middle East, Africa, and South America. Approximately 25% of the primary interviews were conducted with the demand-side vendors and 75% with the supply-side vendors. This primary data was mainly collected through telephonic interviews/web conferences, which consist of 80% of total primary interviews, as well as questionnaires and e-mails.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
The artificial intelligence in healthcare market consists of various technologies such as machine learning, natural language processing, context-aware computing, and computer vision. Artificial Intelligence has major applications in healthcare sector such as patient data & risk analysis, inpatient care & hospital management, medical imaging & diagnostics, lifestyle management & monitoring, virtual assistants, drug discovery, research, healthcare assistant robots, precision medicine, emergency room & surgery, wearables, mental health, and cybersecurity.
Top-down and bottom-up approaches have been used to estimate and validate the size of the artificial intelligence in healthcare market and other dependent submarkets. The leading players in the market have been identified through secondary research, and their market share in the key regions has been determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top players and extensive interviews with the industry experts such as chief executive officers, vice presidents, directors, and marketing executives for the key insights.
Data Triangulation
After arriving at the overall market size from the market size estimation process explained earlier, the total market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments, data triangulation and market breakdown procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. Along with this, the AI in healthcare market has been validated using both top-down and bottom-up approaches.
Study Objectives:
- To describe and forecast the artificial intelligence (AI) in healthcare market, in terms of value,
by offering, technology, application, and end user. - To describe and forecast the AI in healthcare market, in terms of value, for four main regions—
North America, Europe, Asia Pacific (APAC), and the Rest of the World (RoW) - To provide detailed information regarding the major factors influencing the market growth (drivers, restraints, opportunities, and challenges)
- To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contributions to the total market
- To study the complete value chain of the AI in healthcare market
- To provide a detailed impact of the COVID-19 pandemic on the AI in healthcare market, its segments, and the market players
- To analyze opportunities in the market for stakeholders by identifying high-growth segments of the AI in healthcare ecosystem
- To profile key players and comprehensively analyze their market position in terms of ranking and core competencies2, and provide a detailed competitive landscape of the market
- To analyze the competitive developments, such as joint ventures, collaborations, agreements, contracts, partnerships, mergers & acquisitions, new product developments, and research and development (R&D), in the AI in healthcare market
Available Customizations:
With the given market data, MarketsandMarkets offers customizations according to the specific requirements of companies. The following customization options are available for the report.
Company information:
- Detailed analysis and profiling of additional market players (up to 5)
This FREE sample includes market data points, ranging from trend analyses to market estimates & forecasts. See for yourself.
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Growth opportunities and latent adjacency in Artificial Intelligence in Healthcare Market
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