Artificial Intelligence in Healthcare Market

Artificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-aware Computing, Computer Vision), Application, End User and Geography - Global Forecast to 2027

Report Code: SE 5225 Oct, 2021, by marketsandmarkets.com

[252 Pages Report] The artificial intelligence in healthcare market is projected to grow from USD 6.9 billion in 2021 to USD 67.4 billion by 2027; it is expected to grow at a CAGR of 46.2%from 2021 to 2027.

The key factors fueling the growth of the market include market influx of large and complex healthcare datasets, growing need to reduce healthcare costs, improving computing power and declining hardware cost,  rising number of partnerships and collaborations among different domains in healthcare sector, and surging need for improvised healthcare services due to imbalance between health workforce and patients. Additionally, growing potential of AI-based tools for elderly care, increasing focus on developing human-aware AI systems, and rising potential of AI technology in genomics, drug discovery, and imaging & diagnostics to fight COVID-19 is expected to create a growth opportunity for the artificial intelligence in healthcare market.

Artificial Intelligence in Healthcare Market

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COVID-19 impact on the Artificial Intelligence in Healthcare Market

The AI in healthcare market has historically been showcasing significant growth owing to the rapid adoption of AI and ML solutions in the healthcare sector. The onset of the COVID-19 pandemic proved to be an opportunity to showcase the prowess and sophistication AI can bring to the healthcare sector. During the second wave of the pandemic hospitals and clinics across the world made use of AI-based virtual assistants, inpatient care bots, and AI-assisted surgery robots to handle the continuous influx of patients, which would have otherwise overwhelmed the entire hospital operation cycle. With several countries such as the US, Germany, France, China, India, Japan, and South Korea allocating funds to develop AI applications in the healthcare sector, the market is expected to boom over the course of 5 to 10 years.

The major drivers for the market are the increasingly large and complex datasets driving the need for AI, surging demand to reduce the increasing healthcare costs, improving computing power and declining hardware costs, a growing number of cross-industry partnerships and collaborations, and rising imbalance between health workforce and patients driving the need for improvised healthcare services. Another major driving factor for the market currently is the adoption of this technology by multiple pharmaceutical and biotechnology companies across the world to expedite the vaccine or drug development process for

COVID-19. The major restraint for the market is the reluctance among medical practitioners to adopt AI-based technologies and the lack of a skilled workforce. Critical challenges facing the AI in healthcare market include the lack of curated healthcare data, concerns related to data privacy, and the lack of interoperability among AI solutions. Underlying opportunities in the AI in healthcare market include the growing potential of AI-based tools for elderly care and increasing focus on developing human-aware AI systems. The emergence of the COVID-19 pandemic, which has significantly stressed the healthcare infrastructure across the world, is expected to force healthcare providers, payers, and pharmaceutical companies to adopt AI technology. The significant adoption of AI is expected in drug discovery, medical imaging, pathology, mental health, assistance robots, and precision medicine applications in the post-COVID-19 healthcare system.

Market Dynamics

Driver: Improving computing power and declining hardware cost

The increasing adoption of AI has been a new growth driver for semiconductor chipset manufacturers in recent years. GPU/CPU manufacturers, such as Nvidia, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in this field for the development of chipsets that are compatible with AI-based technologies and solutions. Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For instance, Google has built a new ASIC called tensor processing unit (TPU).

A compute-intensive chipset is one of the critical parameters for processing AI algorithms; the faster the chipset, the quicker it can process the data required to create an AI system. Currently, AI chipsets are mostly deployed in data centers/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time. Nvidia has a range of GPUs that offer GPU memory bandwidth based on the application. For instance, GeForce GTX Titan X offers a memory bandwidth of 336.5 GB/s and is mostly deployed in desktops, while Tesla V100 16 GB offers a memory bandwidth of 900 GB/s and is used in AI applications. Similarly, Nvidia’s Tesla V100 (32 GB) is used in high computing workloads. It delivers two times higher throughput compared with its previous generation and offers ~300 GB/s to unleash the highest application performance possible on a single server for approximately the same price (USD 8,799).

The cost of a few AI hardware products has significantly decreased in the past year, which further increases the adoption of AI in new applications, and thus drives the growth of the AI chipsets market.

Restraint: Reluctance among medical practitioners to adopt AI-based technologies

Extensive growth in digital health and mobile health technologies has enabled healthcare providers to assist patients through novel treatment approaches. AI technologies offer doctors tools that help them better diagnose and effectively treat patients. However, there is an observed reluctance among doctors about new technologies. For instance, there is a misconception among medical practitioners that AI will replace doctors in the coming years. The doctors and practitioners believe that skills such as empathy and persuasion are human skills, and thus, technologies cannot completely rule out the presence of a doctor. Additionally, there is a concern that patients may show an excessive inclination toward these technologies and may forgo necessary in-person treatments, which might also challenge long-term doctor-patient relationships.

Currently, several healthcare professionals have doubts about the capabilities of AI solutions in terms of accurately diagnosing patient conditions. Considering this, it is challenging to convince providers that AI-based solutions are cost-effective, efficient, and safe solutions that offer convenience to doctors as well as better care for patients.

However, healthcare providers are increasingly accepting the potential benefits of AI-based solutions and the spectrum of applications they serve. Hence, there is a possibility that in the coming years, doctors will show more inclination toward AI-based technologies for healthcare.

Opportunity: Increasing focus on developing human-aware AI systems

The actual projections aimed during the emergence of AI technologies were to make these technologies human-aware, i.e., developing models with the characteristics of human thinking. However, creating interactive and scalable machines remains a challenge for the developers of AI machines. Additionally, increasing human interference with AI techniques has introduced new research challenges, i.e., interpretation and presentation challenges such as interaction issues with automating parts and intelligent control of crowdsourcing parts. Interpretation challenges include challenges faced by AI machines in understanding human input, such as knowledge and specific directives. Presentation challenges include issues related to delivering the AI system’s output and feedback. The complexity of output can lead to different interpretations of feedback. Hence, the output must be presented exactly as it was intended, to eliminate any ambiguity. This can be a challenge if the target user is not highly skilled in understanding the technology Thus, the development of human-aware AI systems remains the foremost opportunity for AI developers.

Challenge: Concerns regarding data privacy

AI has several useful applications in healthcare. However, the adoption of AI in the industry is restricted owing to data privacy concerns. Patient health data is protected under federal laws in several countries, and any breach or failure to maintain its integrity can result in legal and financial penalties. As AI used for patient care requires access to multiple health datasets, AI-based tools need to adhere to all the data security protocols implemented by governments and regulatory authorities. This is a difficult task as most AI platforms are consolidated and require extensive computing power owing to which patient data, or parts of it, can be required to reside in a vendor’s data center. The vendor data centers are not secure enough to avoid data breaches as the data is accessible to an array of employees and containing breaches becomes much difficult. If the patients’ data is leaked from these data centers, even inadvertently, it can lead to huge lawsuits and settlement claims by aggrieved parties. This is a major challenge in the market. The figure below shows the percentage of healthcare breaches reported to the US Department of Health and Human Services involving 500 or more individuals.

The software segment is projected to account for the largest share of the artificial intelligence in healthcare market during the forecast period.

Many companies are developing software solutions for various healthcare applications; this is the key factor complementing the growth of the software segment. Strong demand among software developers (especially in medical centers and universities) and widening applications of AI in the healthcare sector are among the prime factors complementing the growth of the AI platform within the software segment. Google AI Platform, TensorFlow, Microsoft Azure, Premonition, Watson Studio, Lumiata, and Infrrd are some of the top AI platforms.

Artificial Intelligence in Healthcare Market by Region

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North America region is expected to hold the largest share of the artificial intelligence in healthcare market during the forecast period.

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 market players have implemented various types of organic as well as inorganic growth strategies, such as new product launches,  and acquisitions to strengthen their offerings in the market. The major players in the artificial intelligence in healthcare market are  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.

Report Scope:

Report Metric

Details

Years considered

2018–2027

Base year considered

2020

Forecast period

2021–2027

Forecast units

Volume (million units), Value (USD million/billion)

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 artificial intelligence 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 August 2021, Philips launched two new HealthSuite solutions. HealthSuite solutions allow health systems to integrate informatics applications that can be combined and scaled up or down according to emerging needs. Philips HealthSuite solutions help health systems deliver on the quadruple aim through a connected, protected, future-ready, and cost-predictive single cloud infrastructure and Software-as-a-Service (SaaS) model.
  • In May 2021, IBM launched two-nanometer chip technology. It will increase chip performance, increase efficiency, and will help in AI and cloud applications.
  • In April 2021, IBM launched advanced storage solutions designed to simplify data accessibility and availability for cloud and AI applications.
  • In April 2021, Nvidia announced the launch of A30 &A10 GPUs for enterprise servers.
  • In April 2021, Nvidia launched Morpheus to enable cybersecurity providers to develop AI solutions that can instantly detect cyber breaches.

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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 AI IN HEALTHCARE MARKET, 2021–2027 (USD MILLION)
          FIGURE 9 EFFECT OF COVID-19 ON AI IN HEALTHCARE MARKET
    3.2 POST-COVID-19 SCENARIO
          TABLE 4 POST-COVID-19 SCENARIO: AI IN HEALTHCARE MARKET, 2021–2027 (USD MILLION)
    3.3 OPTIMISTIC SCENARIO (POST-COVID-19)
          TABLE 5 OPTIMISTIC SCENARIO (POST-COVID-19): AI IN HEALTHCARE MARKET, 2021–2027 (USD MILLION)
    3.4 PESSIMISTIC SCENARIO (POST-COVID-19)
          TABLE 6 PESSIMISTIC SCENARIO (POST-COVID-19): AI IN HEALTHCARE MARKET, 2021–2027 (USD BILLION)
          FIGURE 10 SOFTWARE SEGMENT TO LEAD AI IN HEALTHCARE MARKET, IN TERMS OF SIZE, FROM 2018 TO 2027
          FIGURE 11 NORTH AMERICA TO HOLD LARGEST SHARE OF AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY TECHNOLOGY
          FIGURE 13 MACHINE LEARNING TECHNOLOGY TO ACCOUNT FOR LARGEST SIZE OF AI IN HEALTHCARE MARKET FROM 2021 TO 2027
    4.3 AI IN HEALTHCARE MARKET, BY TECHNOLOGY AND REGION
          FIGURE 14 MACHINE LEARNING TECHNOLOGY AND NORTH AMERICA TO BE LARGEST SHAREHOLDERS OF AI IN HEALTHCARE MARKET IN IN 2021
    4.4 AI IN HEALTHCARE MARKET, BY COUNTRY
          FIGURE 15 AI IN HEALTHCARE 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 58
           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 AI IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS
    5.5 ECOSYSTEM ANALYSIS
          TABLE 8 ECOSYSTEM: AI IN HEALTHCARE MARKET
    5.6 REVENUE SHIFT AND NEW REVENUE POCKETS FOR AI IN HEALTHCARE MARKET
          FIGURE 19 YC–YCC SHIFT: AI IN HEALTHCARE 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, AI IN HEALTHCARE MARKET, 2018–2027 (USD)
          TABLE 9 ASP RANGE OF PROCESSOR COMPONENTS IN AI IN HEALTHCARE MARKET, 2018–2027
          TABLE 10 ASP RANGE OF SERVER SOFTWARE IN AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, 2016–2020 (THOUSAND UNITS)
    5.11 PATENT ANALYSIS
            FIGURE 23 PATENT ANALYSIS: AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
          TABLE 17 AI IN HEALTHCARE MARKET, BY OFFERING, 2018–2020 (USD MILLION)
          TABLE 18 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY HARDWARE, 2018–2020 (USD MILLION)
                     TABLE 20 AI IN HEALTHCARE MARKET, BY HARDWARE, 2021–2027 (USD MILLION)
                     TABLE 21 AI IN HEALTHCARE MARKET FOR HARDWARE, BY REGION, 2018–2020 (USD MILLION)
                     TABLE 22 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY PROCESSOR TYPE, 2018–2020 (MILLION UNITS)
                                TABLE 24 AI IN HEALTHCARE MARKET, BY PROCESSOR TYPE, 2021–2027 (MILLION UNITS)
                                TABLE 25 AI IN HEALTHCARE MARKET, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
                                TABLE 26 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY NETWORK, 2021–2027 (MILLION UNITS)
                                TABLE 29 AI IN HEALTHCARE MARKET, BY NETWORK, 2018–2020 (USD MILLION)
                                TABLE 30 AI IN HEALTHCARE MARKET, BY NETWORK, 2021–2027 (USD MILLION)
    6.3 SOFTWARE
           6.3.1 SOFTWARE SEGMENT HOLDS LARGEST SHARE IN AI IN HEALTHCARE MARKET
                     TABLE 31 AI IN HEALTHCARE MARKET, BY SOFTWARE TYPE, 2018–2020 (USD MILLION)
                     TABLE 32 AI IN HEALTHCARE MARKET, BY SOFTWARE TYPE, 2021–2027 (USD MILLION)
                     TABLE 33 AI IN HEALTHCARE MARKET FOR SOFTWARE, BY REGION, 2018–2020 (USD MILLION)
                     TABLE 34 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT MODE, 2018–2020 (USD MILLION)
                                TABLE 36 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR SOFTWARE, BY AI PLATFORM, 2018–2020 (USD MILLION)
                     TABLE 38 AI 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 AI IN HEALTHCARE MARKET, BY SERVICE TYPE, 2018–2020 (USD MILLION)
                     TABLE 40 AI IN HEALTHCARE MARKET, BY SERVICE TYPE, 2021–2027 (USD MILLION)
                     TABLE 41 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY TECHNOLOGY
           FIGURE 27 MACHINE LEARNING TECHNOLOGY TO HOLD LARGEST SIZE OF AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
          TABLE 43 AI IN HEALTHCARE MARKET, BY TECHNOLOGY, 2018–2020 (USD MILLION)
          TABLE 44 AI IN HEALTHCARE MARKET, BY TECHNOLOGY, 2021–2027 (USD MILLION)
    7.2 MACHINE LEARNING
           7.2.1 CASE STUDY: MAYO CLINIC AND GOOGLE
                    TABLE 45 AI IN HEALTHCARE MARKET FOR MACHINE LEARNING, BY TYPE, 2018–2020 (USD MILLION)
                    TABLE 46 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR NATURAL LANGUAGE PROCESSING, BY TYPE, 2018–2020 (USD MILLION)
                    TABLE 48 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR CONTEXT-AWARE COMPUTING, BY TYPE, 2018–2020 (USD MILLION)
                    TABLE 50 AI IN HEALTHCARE 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 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION (Page No. - 107)
    8.1 INTRODUCTION
          FIGURE 28 AI IN HEALTHCARE MARKET, BY APPLICATION
          FIGURE 29 MEDICAL IMAGING & DIAGNOSTICS TO ACCOUNT FOR LARGEST SIZE OF AI IN HEALTHCARE MARKET IN 2027
          TABLE 51 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 54 AI IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 55 AI IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 56 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR INPATIENT CARE & HOSPITAL MANAGEMENT, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 58 AI IN HEALTHCARE MARKET FOR INPATIENT CARE & HOSPITAL MANAGEMENT, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 59 AI IN HEALTHCARE MARKET FOR INPATIENT CARE AND HOSPITAL MANAGEMENT, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 60 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 62 AI IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 63 AI IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 64 AI 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 AI IN HEALTHCARE MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 67 AI IN HEALTHCARE MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 68 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANT, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 70 AI IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANT, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 71 AI IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANT, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 72 AI 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 AI IN HEALTHCARE MARKET FOR DRUG DISCOVERY, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 74 AI IN HEALTHCARE MARKET FOR DRUG DISCOVERY, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 75 AI IN HEALTHCARE MARKET FOR DRUG DISCOVERY, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 76 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR RESEARCH, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 78 AI IN HEALTHCARE MARKET FOR RESEARCH, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 79 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 82 AI IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 83 AI IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 84 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR PRECISION MEDICINE, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 86 AI IN HEALTHCARE MARKET FOR PRECISION MEDICINE, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 87 AI IN HEALTHCARE MARKET FOR PRECISION MEDICINE, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 88 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 90 AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 91 AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 92 AI 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 AI IN HEALTHCARE MARKET FOR WEARABLES, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 94 AI IN HEALTHCARE MARKET FOR WEARABLES, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 95 AI IN HEALTHCARE MARKET FOR WEARABLES, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 96 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR MENTAL HEALTH, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 98 AI IN HEALTHCARE MARKET FOR MENTAL HEALTH, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 99 AI IN HEALTHCARE MARKET FOR MENTAL HEALTH, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 100 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR CYBERSECURITY, BY REGION, 2021–2027 (USD MILLION)
                    TABLE 103 AI IN HEALTHCARE MARKET FOR CYBERSECURITY, BY END USER, 2018–2020 (USD MILLION)
                    TABLE 104 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET, BY END USER
          FIGURE 31 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE DURING FORECAST PERIOD
          TABLE 105 AI IN HEALTHCARE MARKET, BY END USER, 2018–2020 (USD MILLION)
          TABLE 106 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY APPLICATION, 2018–2020 (USD MILLION)
                    TABLE 108 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY APPLICATION, 2021–2027 (USD MILLION)
                    TABLE 109 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 110 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR PATIENTS, BY APPLICATION, 2018–2020 (USD MILLION)
                    TABLE 112 AI IN HEALTHCARE MARKET FOR PATIENTS, BY APPLICATION, 2021–2027 (USD MILLION)
                    TABLE 113 AI IN HEALTHCARE MARKET FOR PATIENTS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 114 AI 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 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY APPLICATION, 2021–2027 (USD MILLION)
                    TABLE 117 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 118 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY APPLICATION, 2018–2020 (USD MILLION)
                    TABLE 120 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY APPLICATION, 2021–2027 (USD MILLION)
                    TABLE 121 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY REGION, 2018–2020 (USD MILLION)
                    TABLE 122 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY REGION, 2021–2027 (USD MILLION)
    9.6 OTHERS
          TABLE 123 AI IN HEALTHCARE MARKET FOR OTHERS, BY APPLICATION, 2018–2020 (USD MILLION)
          TABLE 124 AI IN HEALTHCARE MARKET FOR OTHERS, BY APPLICATION, 2021–2027 (USD MILLION)
          TABLE 125 AI IN HEALTHCARE MARKET FOR OTHERS, BY REGION, 2018–2020 (USD MILLION)
          TABLE 126 AI IN HEALTHCARE MARKET FOR OTHERS, BY REGION, 2021–2027 (USD MILLION)

10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, GEOGRAPHIC ANALYSIS (Page No. - 148)
     10.1 INTRODUCTION
             FIGURE 32 CHINA AND US ARE EMERGING AS NEW HOTSPOTS FOR AI IN HEALTHCARE MARKET
             FIGURE 33 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
             TABLE 127 AI IN HEALTHCARE MARKET, BY REGION, 2018–2020 (USD MILLION)
             TABLE 128 AI IN HEALTHCARE MARKET, BY REGION, 2021–2027 (USD MILLION)
     10.2 NORTH AMERICA
             FIGURE 34 NORTH AMERICA: SNAPSHOT OF AI IN HEALTHCARE MARKET
             TABLE 129 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY COUNTRY, 2018–2020 (USD MILLION)
             TABLE 130 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY COUNTRY, 2021–2027 (USD MILLION)
             TABLE 131 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY APPLICATION, 2018–2020 (USD MILLION)
             TABLE 132 AI 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 AI IN HEALTHCARE MARKET
             TABLE 133 AI IN HEALTHCARE MARKET IN EUROPE, BY COUNTRY, 2018–2020 (USD MILLION)
             TABLE 134 AI IN HEALTHCARE MARKET IN EUROPE, BY COUNTRY, 2021–2027 (USD MILLION)
             TABLE 135 AI IN HEALTHCARE MARKET IN EUROPE, BY APPLICATION, 2018–2020 (USD MILLION)
             TABLE 136 AI IN HEALTHCARE MARKET IN EUROPE, BY APPLICATION, 2021–2027 (USD MILLION)
             10.3.1 GERMANY
                        10.3.1.1 Government initiatives to expedite AI development supporting AI in healthcare 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 AI in healthcare 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 AI in healthcare market growth
                        10.3.5.2 Recent developments:
             10.3.6 REST OF EUROPE
     10.4 APAC
             FIGURE 36 APAC: SNAPSHOT OF AI IN HEALTHCARE MARKET
             TABLE 137 AI IN HEALTHCARE MARKET IN APAC, BY COUNTRY, 2018–2020 (USD MILLION)
             TABLE 138 AI IN HEALTHCARE MARKET IN APAC, BY COUNTRY, 2021–2027 (USD MILLION)
             TABLE 139 AI IN HEALTHCARE MARKET IN APAC, BY APPLICATION, 2018–2020 (USD MILLION)
             TABLE 140 AI IN HEALTHCARE 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 AI in healthcare 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 AI in healthcare 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 AI in healthcare 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 AI IN HEALTHCARE MARKET IN ROW, BY REGION, 2018–2020 (USD MILLION)
             TABLE 142 AI IN HEALTHCARE MARKET IN ROW, BY REGION, 2021–2027 (USD MILLION)
             TABLE 143 AI IN HEALTHCARE MARKET IN ROW, BY APPLICATION, 2018–2020 (USD MILLION)
             TABLE 144 AI IN HEALTHCARE 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 AI in healthcare 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 AI IN HEALTHCARE 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 AI IN HEALTHCARE 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 AI IN HEALTHCARE 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 AI IN HEALTHCARE MARKET: PRODUCT LAUNCHES AND DEVELOPMENTS, MARCH 2019 TO OCTOBER 2021
             TABLE 151 AI IN HEALTHCARE 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.

Artificial Intelligence in Healthcare Market Size, and Share

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)
COVID-19

Get in-depth analysis of the COVID-19 impact on the Artificial Intelligence in Healthcare Market

Benchmarking the rapid strategy shifts of the Top 100 companies in the Artificial Intelligence in Healthcare Market

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Report Code
SE 5225
Published ON
Oct, 2021
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