Artificial Intelligence in Healthcare Market with Covid-19 Impact Analysis by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-Aware Computing, Computer Vision), End-Use Application, End User and Region - Global Forecast to 2026
AI in Healthcare Market
AI in Healthcare Market size is expected to grow at a CAGR of 44.9% during the forecast period.
AI in Healthcare Market and Top Companies
- NVIDIA Corporation (US) − NVIDIA develops GPUs and delivers value to its consumers through PC, mobile, and cloud architectures. From focus on PC graphics, the company now emphasizes on machine learning and various other technologies of AI. In 2019, the company launched NVIDIA DGX-2, the world’s first single-node computer to deliver two petaflops of deep learning performance. The Tegra Processor segment includes Tegra, DRIVE PX, Clara AGX, SHIELD, Jetson AGX. Clara AI, Clara Imaging, NVIDIA AGX, and DGX are the platforms that are used across multiple applications, including healthcare and life sciences.
- Intel Corporation (US) − Intel designs and manufactures key products and technologies that power the cloud and the smart, connected world. Intel delivers computer, networking, and communication platforms to a broad set of customers, including original equipment manufacturers (OEMs), original design manufacturers (ODMs), cloud and communications service providers, and industrial, communications, and automotive equipment manufacturers. Intel’s technology innovations are enabling the healthcare sector with data-driven insights and artificial intelligence by introducing various tools and solutions to simplify AI deployment in healthcare.
- IBM Corporation (US) − IBM is an established player and a globally renowned vendor for providing dedicated software, hardware, and related services. The cognitive software offerings of the company include software that address vertical and domain-specific solutions, increasingly infused with AI and enabled by IBM’s Watson technology. AI includes cognitive computing systems such as natural language processing, machine learning, algorithms that learn and adapt, and pattern recognition. For example, Watson for Drug Discovery from IBM Watson Health is a platform that mines text and data in medical literature to identify, assess, and rigorously formalize the relationships among genes, diseases, and drugs to help researchers uncover potential new therapies and find new uses for existing medicines.
- Google Inc. (US) − Google is one of the global technology leaders, and its primary areas of operations include advertising, search, operating systems and platforms, and enterprise and hardware products. The company operates its businesses through various subsidiaries, such as TensorFlow, Google X, DeepMind Technologies Limited, Calico, and GV in the healthcare market. Google Brain is the company’s in-house domain, which develops various AI technologies, such as ML algorithms and techniques, NLP, music and art generation, and robotics. CapitalG, GV, Google Fiber, Nest, Verily, Access, Waymo, Calico, and X are among the other major bets or segments of Google.
- Microsoft Corporation (US) – Microsoft is a global leader offering software products and diverse licensing suites. The company offers significantly diversified software products and is active in their development, licensing, and support. For the health & life sciences industry, it provides intelligent tools and solutions that empower clinical and operational efficiency and save costs. Azure Synapse Analytics, Azure Health GitHub Repo, Microsoft Healthcare bot, Azure API for FHIR, Azure AI and ML and Azure IoT for Healthcare are scalable platforms that bring conversational AI to healthcare. Yammer (US), Mojang AB (Sweden), StorSimple (US), and Xamarin (US) are among the subsidiaries of Microsoft.
- General Electric (US) – General Electric (GE) is a global digital industrial company with product and services ranging from aircraft engines, power generation, oil and gas production equipment, and medical imaging. GE Healthcare is a fully owned subsidiary of GE and provides healthcare diagnostic imaging and clinical systems, life sciences products and services, and digital solutions. GE Healthcare has expertise in medical imaging, digital solutions, patient monitoring and diagnostics, drug discovery, biopharmaceutical manufacturing technologies, and performance improvement solutions that are the building blocks of precision health.
- Siemens AG (Germany) – Siemens Healthineers caters to digital ecosystem and platforms population, health management, and imaging IT. It is among the largest technology suppliers to the healthcare industry and is a key player in diagnostic imaging and laboratory diagnostics. It company provides medical technology and software solutions as well as clinical consulting services, along with a complete set of training and service offering. Siemens Healthineers includes 6 business areas: Diagnostic Imaging, Laboratory Diagnostics, Advanced Therapies, Ultrasound, Point of Care Diagnostics, and Services. In its imaging business, the most important products are equipment for magnetic resonance, computed tomography, X-ray systems, molecular imaging and ultrasound.
- Medtronic plc (Germany) – Medtronic plc is involved in the development, manufacturing, and marketing of medical devices, therapies, and services for the treatment of cardiovascular diseases, spinal conditions, neurological disorders, and diabetes. The company’s primary customers include hospitals, clinics, third-party healthcare providers, distributors, and other institutions, including governmental healthcare programs and group purchasing organizations (GPOs).
AI in Healthcare Market and Top End-use Applications
- Patient Data and Risk Analysis − Patient risk algorithms consider several variables and express the results as the percentage risk of developing a major fatal or nonfatal disease in the coming years. Companies functioning in this segment cater to the needs of healthcare professionals, including healthcare providers and payers, by offering them solutions that can provide predictive insights into patient health using machine learning and natural language processing algorithms. The analytics are based on various factors, including medical history and demography.
- Inpatient Care & Hospital Management − AI and machine-learning methods have the potential to improve the quality and lower the cost of patient care. Clinical decision support systems (CDSS) are the most successful applications of AI in inpatient care and hospital management. Recent advances in machine learning and AI can help build predictive models and make real-time inferences from a large patient population for analyzing risks and predicting the length of hospital stay. Such developments are driving the growth of the AI in healthcare market for this application segment.
- MEDICAL IMAGING & DIAGNOSTICS − In healthcare, medical imaging generates a large volume of data, and this data is also the most challenging of all in terms of understanding and interpretation. Healthcare AI startups are raising venture capital and have been working in the field of imaging and diagnostics (especially pathology) and extracting insights using machine learning. Major startups in this field are CureMetrix (California, US), PathAI (Massachusetts, US), Subtle Medical (California, US), Zebra Medical Vision (Israel), Arterys (California, US), Imagen Technologies (New York, US), Viz.AI (California, US), RADLogics (Massachusetts, US), Bay Labs (California, US), Mindshare Medical (Washington, US), Enlitic (California, US), and Proscia (Pennsylvania, US).
- Drug discovery − AI algorithms ingest and analyze a vast amount of information and can identify potential drug candidates in a short time. Deep-learning systems can also be used for generating molecules with properties that are likely to be effective against specific diseases and without side effects. AI also plays an important role in drug discovery for chronic diseases, e.g., cancer. AI significantly reduces the time taken to bring a cancer-combatting drug to the market. Recently, AI has also been used to develop a drug against COVID-19. According to the National Science Foundation (US), researchers across the world are racing toward the development of a vaccine or drug, or a combination of both to treat COVID-19. The researchers are leveraging AI along with physics-based drug docking and molecular dynamics to identify molecules that might interact with the virus.
- Virtual Assistant − The use of AI-based personal assistants can have an incredible impact on monitoring and assisting patients in the absence of clinical personnel. Moreover, a virtual assistant can offer discharged patients more flexibility by capturing data through their voices. By using NLP, virtual assistants can better understand patient needs and conditions, and accordingly, assist in patient care.
AI in Healthcare Market and Top Technologies
- Machine Learning – Machine learning is being adopted in healthcare to deal with large volumes of data, where the time previously dedicated for poring over charts and spreadsheets is now being used to seek intelligent ways to automate data analysis. It is used to streamline administrative processes in hospitals, map and treat infectious diseases, and personalize medical treatments. Machine learning includes various technologies, such as deep learning, supervised learning, unsupervised learning, and reinforcement learning. Imaging and diagnostics, and drug discovery are among the applications that use deep learning.
- Computer Vision − In healthcare, computer vision has shown significant application in surgery and therapy of a few diseases. Robotic surgery application uses computer vision to identify distances or specific body part. Computer vision systems offer precise diagnoses, thus minimizing false positives. The technology can potentially wipe out the requirement for redundant surgical procedures and expensive therapies. Computer vision algorithms that are trained using a huge amount of training data can detect the slightest presence of a condition which human doctors may miss because of their sensory limitations.
- Natural Language Processing − NLP is widely used by the clinical and research community in healthcare to develop and manage semi-structured and unstructured textual documents, such as electronics health reports, pathology reports, and clinical notes. The algorithm extracts the health problems from narrative text clinical documents and proposes for inclusion in a patient’s electronic problem list to interpret accurately. The demand for NLP has grown, with healthcare institutions using it to structure their clinical data and interpret more accurately. Moreover, the growing use of the Internet and connected devices, along with the huge volume of patients’ data, drives the growth of this market.
[ 255 Pages Reports] The global AI in healthcare market size is expected to grow from USD 4.9 billion in 2020 and reach USD 45.2 billion by 2026; it is projected to grow at a CAGR of 44.9% during the forecast period. The major factors driving the market growth are the increasing volume of healthcare data and growing complexities of datasets driving the need for AI, the intensifying need to reduce towering healthcare costs, improving computing power and declining hardware costs, 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 factor fueling the market growth currently is the adoption of this technology by multiple pharmaceutical and biotechnology companies across the world to expedite vaccine or drug development processes for COVID-19.
Software segment to hold largest share in AI in healthcare market during forecast period
Many companies are developing software solutions for various healthcare applications; this is the key factor complementing the growth of the software segment. Growing adoption of AI-driven healthcare informatics solutions and healthcare operational support by hospitals and other healthcare service providers is expected to boost the services segment in the later part of the forecast period.
Machine learning in AI healthcare market projected to grow at highest CAGR during forecast period.
Growing adoption of deep learning in various healthcare applications, especially in the areas of medical imaging, disease diagnostics, and drug discovery, and the use of different sensors and devices to track a patient’s health status in real time are supplementing the growth of the market.
Medical imaging & diagnostics segment in AI healthcare market projected to grow at highest CAGR during forecast period
The growth of the medical imaging & diagnostics segment can be attributed to factors such as the presence of a large volume of imaging data, advantages offered by AI systems to radiologists in diagnosis and treatment management, and the influx of a large number of startups in this segment.
North America accounted for largest share of overall AI in healthcare market in 2019
North America is a key AI in healthcare market, as it is home to some of the largest multinational corporations, such as IBM (US), Microsoft (US), Google (US), NVIDIA (US), Intel (US), GE Healthcare (US), and Johnson & Johnson (US). The US held the largest share of the AI in healthcare market in North America in 2019. The US has the largest number of registered hospitals in the region; however, it is expected to experience a shortfall of healthcare staff in the coming years. AI can help in patient data management, patient care, and hospital management applications, which can help to alleviate such concerns. In addition, aging population, shrinking healthcare workforce in North American countries increasing demand for improved diagnosis and treatment services, rise in demand for digital health systems, and significant presence of major AI technology and product developers in the region are other factors favoring the growth of the market.
Key Market Players
Major players in the AI in healthcare market are NVIDIA Corporation (NVIDIA) (US), Intel Corporation (Intel) (US), International Business Machines Corporation (IBM) (US), Google Inc. (Google) (US), Microsoft Corporation (Microsoft) (US), Amazon Web Services (an Amazon.com, Inc. subsidiary) (AWS) (US), General Vision, Inc. (US), General Electric (GE) Healthcare (US), Siemens Healthineers (Germany), Medtronic plc (US), Johnson & Johnson Services, Inc. (Johnson & Johnson) (US), and Koninklijke Philips N.V. (Netherlands).
IBM is ranked first in the AI in healthcare market. The company has been continually engaged in the development and launch of AI healthcare systems for the past few years. The company has 12 research laboratories dedicated to its AI and deep-learning initiatives. It has more than 1,400 patents in the field of AI. The launch of IBM Watson, a machine learning tool, has further strengthened its market position in the AI in healthcare market. It aims to achieve its AI goals through both organic and inorganic developments. For instance, recently, Mayo Clinic (US) declared results from the use of the IBM Watson for Clinical Trial Matching and announced the extension of the usage of this system in Mayo Clinic’s oncology practices. The system is being used in Mayo’s breast, lung, and gastrointestinal cancer clinical trials. The organization is also aiming to extend and expand the training and use of this system for additional cancer types. The acquisition of Truven Health Analytics (Colorado, US) and Merge Healthcare, Inc. (Illinois, US) has also fortified its AI capabilities in the healthcare industry.
Report Scope
|
Report Metric |
Detail |
|
Market size availability years |
2017–2026 |
|
Base year |
2019 |
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Forecast period |
2020–2026 |
|
Forecast units |
Value (USD million/billion) |
|
Covered segments |
By Offering, By Technology, By End-use Application, By End user |
|
Covered regions |
North America, Europe, APAC, and RoW |
|
Covered companies |
NVIDIA Corporation (NVIDIA) (US), Intel Corporation (Intel) (US), IBM Corporation (IBM) (US), Google LLC (Google) (US), Microsoft Corporation (Microsoft) (US), General Electric X-ray Corporation (GE Healthcare) (US), Siemens Healthineers AG (Siemens Healthineers) (Germany), Medtronic Plc (Medtronic) (Ireland), Micron Technologies (Micron)(US), Amazon Web Services, Inc (AWS) (US), Johnson&Johnson (Johnson&Johnson) (US), Koninklijke Philips N.V. koninklijke Philips (Netherlands), General Vision Services (GVS) (General Vision) (US), Cloudmex Inc. (Cloudmex) (US), Oncora Medical, Inc (Oncora Medical) (US), Anju Life Sciences Software (US), CareSkore, Inc (Careskore) (US), Linguamatics (UK), Enlitic, Inc.(Enlitic) (US), Lunit Inc. (Lunit) (South Korea), CureMetrix Inc. (CureMetrix) (US), Qure.ai Technologies Private Limited (Qure.ai) (India), Context Vision Operations (ContextVision) (Europe), Caption Health(US), Butterfly Network Inc. (Butterfly Networks) (US), Imagia Cybernetics Inc. (Imagia Cybernetics) (Canada), Precision Health Intelligence, LLC. (Precision Health AI) (US), Cota Healthcare (Cota) (US), FDNA, Inc. (FDNA) (US), Recursion Pharmaceuticals, Inc. (Recursion Pharmaceuticals) (US), Atomwise, Inc. (Atomwise) (US), Deep Genomics Inc. (DeepGenomics) (Canada), Cloud Pharmaceuticals (US), Welltok, Inc. (Welltok) (US), Vitagene, Inc. (Vitagene) (US), Lucina Health, Inc. (LucinaHealth) (US), Next IT Corp. (NextIt) (US), Babylon Health (Babylon) (UK), MDLIVE Inc. (MDLive) (US), Magnea (Sweden), Physiq, Inc. (Physiq) (US), CyrcadiaHealth (US), Caresyntax Inc. (CareSyntax) (Germany), Gauss Surgical, Inc. (Gauss Surgical) (US), Perceive3D (Portugal), MaxQ AI. (Maxq.ai) (Israel), Qventus, Inc. (Qventus) (US), WorkFusion, Inc. (WorkFusion) (US), IcarbonX IntellegenceTechnology Co Ltd (iCarbonix) (China), Desktop Genetics Ltd. (Desktop Genetics) (US), Darktrace Limited (DarkTrace) (US), Cylance Inc. (Cylance) (US), LexisNexisRiskSolutions(US), Securonix, Inc. (Securonix) (US), Ginger.io, Inc. (Ginger.IO) (US), X2AI(US), BioBeats Group Ltd (BioBeats) (UK), Pillo, Inc. (Pillo) (US), Catalia Health Inc. (Catalia Health) (US). |
Segmentation for AI in Healthcare Market
In this report, the AI in Healthcare Market has been segmented into the following categories:
By Offering
- Hardware
- Processor
- MPU
- GPU
- FPGA
- ASIC
- Memory
- Network
- Processor
- Software
- AI Solutions
- On-Premises
- Cloud
- AI Platform
- Machine Learning Framework
- Application Program Interface
- Services
- Deployment & Integration
- Support & Maintenance
- AI Solutions
By Technology
- Machine Learning
- Deep Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Others
- Natural Language Processing
- Context-Aware Computing
- Computer Vision
By End-Use Application
- Patient Data & Risk Analysis
- Inpatient Care & Hospital Management
- Medical Imaging & Diagnostics
- Lifestyle Management & Patient Monitoring
- Virtual Assistants
- Drug Discovery
- Research
- Healthcare Assistance Robots
- Precision Medicine
- Emergency Room & Surgery
- Wearables
- Mental Health
- Cybersecurity
By End User
- Hospitals & Healthcare Providers
- Patients
- Pharmaceuticals & Biotechnology Companies
- Healthcare Payers
- Others (ACOS and MCOS)
By Region
- North America
- US
- Canada
- Mexico
- Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
- Asia Pacific
- China
- Japan
- South Korea
- India
- Rest of Asia Pacific
- Rest of the World (ROW)
- South America
- Middle East & Africa
Recent Developments
- In March 2020, IBM Watson Health and EBSCO Information Services partnered to provide access to evidence-based drug and disease information that can help support clinicians and individuals as they cope with infectious diseases, including COVID-19.
- In July 2019, IBM acquired RedHat. Red Hat’s open hybrid cloud technologies are now paired with IBM’s innovation and industry expertise. IBM and Red Hat will accelerate innovation by offering a next-generation hybrid multi-cloud platform. Based on open source technologies, such as Linux and Kubernetes, the platform will allow businesses to securely deploy, run, and manage data and applications on premises and on private and multiple public clouds.
- In March 2018, Mayo Clinic (US) and IBM Watson Health announced an agreement to use IBM’s cognitive computing system for clinical trial matching. The use of this system associates with more patients enrolled in Mayo’s breast cancer clinical trials. The organizations aim to extend and expand the training and use of the system. Currently, the system is trained to support clinical trial matching for breast, lung, and gastrointestinal cancers.
Critical Questions the Report Answers:
- Where are all these developments expected to take AI in healthcare market in the mid- and long-term?
- What are the prevalent trends in the AI healthcare market?
- What are the key strategies adopted by leading companies in the AI healthcare market?
- Which country is likely to emerge as the largest market?
- What is the impact of COVID-19 on the AI in healthcare market?
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 22)
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 YEARS CONSIDERED
1.4 CURRENCY
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY (Page No. - 26)
2.1 RESEARCH DATA
2.1.1 SECONDARY AND PRIMARY RESEARCH
2.1.1.1 Key industry insights
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 Breakdown of primaries
2.1.3.3 Key data from primary sources
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Estimating market size by bottom-up approach (demand side)
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Estimating market size by top-down approach (supply side)
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
3 EXECUTIVE SUMMARY (Page No. - 39)
3.1 COVID-19 IMPACT ANALYSIS: AI IN HEALTHCARE MARKET
3.1.1 PRE-COVID-19 SCENARIO
3.1.2 REALISTIC SCENARIO
3.1.3 OPTIMISTIC SCENARIO
3.1.4 PESSIMISTIC SCENARIO
4 PREMIUM INSIGHTS (Page No. - 47)
4.1 ATTRACTIVE OPPORTUNITIES IN AI IN HEALTHCARE MARKET
4.2 AI IN HEALTHCARE MARKET, BY OFFERING
4.3 AI IN HEALTHCARE MARKET, BY TECHNOLOGY
4.4 EUROPE: AI IN HEALTHCARE MARKET, BY END USER AND COUNTRY
4.5 AI IN HEALTHCARE MARKET, BY COUNTRY
5 MARKET OVERVIEW (Page No. - 50)
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
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 Growing number of cross-industry partnerships and collaborations
5.2.1.5 Rising 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 Growing 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
5.2.4.3 Lack of interoperability between AI solutions offered by different vendors
5.3 VALUE CHAIN ANALYSIS
5.4 CASE STUDIES
5.4.1 MAYO CLINIC’S CENTER FOR INDIVIDUALIZED MEDICINE COLLABORATED WITH TEMPUS TO PERSONALIZE CANCER TREATMENT
5.4.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE
5.4.3 NVIDIA AND MASSACHUSETTS GENERAL HOSPITAL PARTNERED TO USE ARTIFICIAL INTELLIGENCE FOR ADVANCED RADIOLOGY, PATHOLOGY, AND GENOMICS
5.4.4 MICROSOFT PARTNERED WITH WEIL CORNELL MEDICINE TO DEVELOP AI-POWERED CHATBOT
5.4.5 PARTNERS HEALTHCARE AND GE HEALTHCARE ENTERED INTO 10-YEAR COLLABORATION FOR INTEGRATING AI ACROSS CONTINUUM OF CARE
5.4.6 ULTRONICS, ZEBRA MEDICAL VISION, AI2 INCUBATOR, AND FUJIFILM SONOSITE ARE USING AI PLATFORM FOR ENHANCING MEDICAL IMAGING ANALYSIS
5.4.7 NUMEDII, 4QUANT, AND DESKTOP GENETICS TO USE AI FOR RESEARCH AND DEVELOPMENT
5.4.8 NUANCE LAUNCHED DRAGON MEDICAL VIRTUAL ASSISTANT
5.4.9 GE HEALTHCARE LAUNCHED COMMAND CENTER FOR EMERGENCY ROOMS AND SURGERIES
5.4.10 AISERVE OFFERS AI WEARABLE FOR BLIND AND PARTIALLY SIGHTED
5.5 IMPACT OF COVID-19 ON AI IN HEALTHCARE MARKET
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING (Page No. - 68)
6.1 INTRODUCTION
6.2 HARDWARE
6.2.1 PROCESSOR
6.2.1.1 MPU
6.2.1.2 GPU
6.2.1.3 FPGA
6.2.1.4 ASIC
6.2.2 MEMORY
6.2.2.1 High-bandwidth memory is being developed and deployed for AI applications, independent of its computing architecture
6.2.3 NETWORK
6.2.3.1 NVIDIA (US) and Intel (US) are key providers of network interconnect adapters for AI applications
6.3 SOFTWARE
6.3.1 AI SOLUTIONS
6.3.1.1 On-premises
6.3.1.1.1 Data-sensitive enterprises prefer advanced on-premises NLP and ML tools for use in AI solutions
6.3.1.2 Cloud
6.3.1.2.1 Cloud provides additional flexibility for business operations and real-time deployment ease to companies that are implementing real-time analytics
6.3.2 AI PLATFORM
6.3.2.1 Machine learning framework
6.3.2.1.1 Major tech companies such as Google, IBM, and Microsoft are developing and offering ML frameworks
6.3.2.2 Application program interface (API)
6.3.2.2.1 APIs are used during programming of graphical user interface (GUI) components
6.4 SERVICES
6.4.1 DEPLOYMENT & INTEGRATION
6.4.1.1 Need for deployment and integration services for AI hardware and software solutions is supplementing growth of this segment
6.4.2 SUPPORT & MAINTENANCE
6.4.2.1 Maintenance services are required to keep the performance of systems at an acceptable standard
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY (Page No. - 81)
7.1 INTRODUCTION
7.2 MACHINE LEARNING
7.2.1 DEEP LEARNING
7.2.1.1 Deep learning enables machines to build hierarchical representations
7.2.2 SUPERVISED LEARNING
7.2.2.1 Classification and regression are major segments of supervised learning
7.2.3 REINFORCEMENT LEARNING
7.2.3.1 Reinforcement learning allows systems and software to determine ideal behavior for maximizing performance of systems
7.2.4 UNSUPERVISED LEARNING
7.2.4.1 Unsupervised learning includes clustering methods consisting of algorithms with unlabeled training data
7.2.5 OTHERS
7.3 NATURAL LANGUAGE PROCESSING
7.3.1 NLP IS WIDELY USED BY CLINICAL AND RESEARCH COMMUNITY IN HEALTHCARE
7.4 CONTEXT-AWARE COMPUTING
7.4.1 DEVELOPMENT OF MORE SOPHISTICATED HARD AND SOFT SENSORS HAS ACCELERATED GROWTH OF CONTEXT-AWARE COMPUTING
7.5 COMPUTER VISION
7.5.1 COMPUTER VISION TECHNOLOGY HAS SHOWN SIGNIFICANT APPLICATIONS IN SURGERY AND THERAPY
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END-USE APPLICATION (Page No. - 89)
8.1 INTRODUCTION
8.2 PATIENT DATA AND RISK ANALYSIS
8.2.1 GROWTH IN HEALTHCARE DATA HAS ESCALATED USE OF PATIENT DATA AND RISK ANALYSIS SOLUTIONS
8.3 INPATIENT CARE & HOSPITAL MANAGEMENT
8.3.1 DEMAND TO REDUCE OPERATIONAL COST IN HOSPITALS TO GENERATE DEMAND FOR AI-BASED INPATIENT CARE AND HOSPITAL MANAGEMENT SOLUTIONS
8.4 MEDICAL IMAGING & DIAGNOSTICS
8.4.1 GROWTH IN MEDICAL IMAGING DATA HAS PROPELLED USE OF AI IN MEDICAL IMAGING AND DIAGNOSTICS APPLICATION
8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING
8.5.1 AI SOLUTIONS FOR LIFESTYLE MANAGEMENT AND MONITORING HELP PATIENTS IN MAKING HEALTHIER LIFESTYLE CHANGES
8.6 VIRTUAL ASSISTANTS
8.6.1 INCREASING DEMAND TO IMPROVE FOLLOW-UP CARE, ESPECIALLY FOR PATIENTS WITH CHRONIC DISEASES, IS DRIVING GROWTH OF VIRTUAL ASSISTANTS SEGMENT
8.7 DRUG DISCOVERY
8.7.1 AI IS EXPECTED TO REDUCE TIME AND COST INVOLVED IN DRUG DISCOVERY
8.8 RESEARCH
8.8.1 USE OF AI ALGORITHMS BY BIOINFORMATICS RESEARCHERS FOR DATABASE CLASSIFICATION AND MINING DRIVES ADOPTION OF AI IN RESEARCH
8.9 HEALTHCARE ASSISTANCE ROBOTS
8.9.1 HEALTHCARE ASSISTANCE ROBOTS HAVE BEEN ADOPTED IN SEVERAL AREAS THAT DIRECTLY AFFECT PATIENT CARE
8.10 PRECISION MEDICINE
8.10.1 AI IS EXPECTED TO FULFIL DEMAND FOR PERSONALIZED TREATMENT PLANS FOR PATIENTS ADMINISTERED WITH PRECISION MEDICINE
8.11 EMERGENCY ROOM & SURGERY
8.11.1 LIMITED WORKFORCE IN EMERGENCY ROOMS AND DEMAND TO SUPPORT CLINICIANS WITH SURGICAL DATA TO DRIVE GROWTH OF AI IN EMERGENCY ROOM AND SURGERY
8.12 WEARABLES
8.12.1 WEARABLE DEVICES ARE CLINICALLY USEFUL FOR IMPROVING REAL-TIME MONITORING OF PATIENTS
8.13 MENTAL HEALTH
8.13.1 GLOBAL INCREASE IN MENTAL DISORDERS IS KEY SUPPORTING FACTOR FOR GROWING USE OF AI IN MENTAL HEALTH APPLICATION
8.14 CYBERSECURITY
8.14.1 AI IN HEALTHCARE CYBERSECURITY IS BECOMING CRITICAL FOR PROTECTING ONSITE SYSTEMS
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER (Page No. - 111)
9.1 INTRODUCTION
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
9.3 PATIENTS
9.3.1 SMARTPHONE APPLICATIONS AND WEARABLES TO DRIVE ADOPTION OF AI AMONG PATIENTS
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
9.5 HEALTHCARE PAYERS
9.5.1 HEALTHCARE PAYERS USE AI TOOLS MAINLY FOR MANAGING RISKS, IDENTIFYING CLAIMS TRENDS, AND MAXIMIZING PAYMENT ACCURACY
9.6 OTHERS
9.6.1 PATIENT DATA AND RISK ANALYSIS, AND HEALTHCARE ASSISTANCE ROBOTS TO DRIVE USE OF AI BY ACOS AND MCOS
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION (Page No. - 130)
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 US
10.2.1.1 High healthcare spending to complement the growth of AI in the US
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
10.2.3 MEXICO
10.2.3.1 AI-enabled devices for the healthcare sector have been gaining traction in Mexico
10.3 EUROPE
10.3.1 GERMANY
10.3.1.1 Government initiatives to expedite AI development supporting AI in healthcare market growth in Germany
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
10.3.3 FRANCE
10.3.3.1 Government endeavors to develop healthcare IT in France is likely to support AI in healthcare market
10.3.4 ITALY
10.3.4.1 Development of electronic health records and aging population is driving market growth in Italy
10.3.5 SPAIN
10.3.5.1 Growing awareness of AI in Spain is favoring AI in healthcare market growth
10.3.6 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 CHINA
10.4.1.1 Concrete government measures to accelerate AI development is fueling AI in healthcare market growth in China
10.4.2 JAPAN
10.4.2.1 AI application 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 is motivating growth of AI in healthcare market in South Korea
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
10.4.5 REST OF ASIA PACIFIC
10.5 REST OF THE WORLD
10.5.1 SOUTH AMERICA
10.5.1.1 Heavy investment in healthcare IT is 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
11 COMPETITIVE LANDSCAPE (Page No. - 167)
11.1 OVERVIEW
11.2 RANKING OF PLAYERS, 2019
11.3 COMPETITIVE LEADERSHIP MAPPING
11.3.1 VISIONARY LEADERS
11.3.2 DYNAMIC DIFFERENTIATORS
11.3.3 INNOVATORS
11.3.4 EMERGING COMPANIES
11.4 COMPETITIVE SCENARIO
11.4.1 PRODUCT DEVELOPMENTS AND LAUNCHES
11.4.2 COLLABORATIONS, PARTNERSHIPS, AND STRATEGIC ALLIANCES
11.4.3 ACQUISITIONS & JOINT VENTURES
12 COMPANY PROFILES (Page No. - 180)
(Business overview, Products offered, Recent developments, SWOT analysis & MnM View)*
12.1 KEY PLAYERS
12.1.1 NVIDIA
12.1.2 INTEL
12.1.3 IBM
12.1.4 GOOGLE
12.1.5 MICROSOFT
12.1.6 GENERAL ELECTRIC (GE) COMPANY
12.1.7 SIEMENS HEALTHINEERS (A STRATEGIC UNIT OF SIEMENS GROUP)
12.1.8 MEDTRONIC
12.1.9 MICRON TECHNOLOGY
12.1.10 AMAZON WEB SERVICES (AWS)
*Details on Business overview, Products offered, Recent developments, SWOT analysis & MnM View might not be captured in case of unlisted companies.
12.2 RIGHT TO WIN
12.3 OTHER MAJOR COMPANIES
12.3.1 JOHNSON & JOHNSON SERVICES
12.3.2 KONINKLIJKE PHILIPS
12.3.3 GENERAL VISION
12.4 COMPANY PROFILES, BY APPLICATION
12.4.1 PATIENT DATA & RISK ANALYSIS
12.4.1.1 CloudMedx
12.4.1.2 Oncora medical
12.4.1.3 Anju life sciences software
12.4.1.4 CareSkore
12.4.1.5 Linguamatics
12.4.2 MEDICAL IMAGING & DIAGNOSTICS
12.4.2.1 Enlitic
12.4.2.2 Lunit
12.4.2.3 CureMetrix
12.4.2.4 Qure.ai
12.4.2.5 ContextVision
12.4.2.6 Caption health
12.4.2.7 Butterfly networks
12.4.2.8 Imagia cybernetics
12.4.3 PRECISION MEDICINE
12.4.3.1 Precision health AI
12.4.3.2 Cota
12.4.3.3 FDNA
12.4.4 DRUG DISCOVERY
12.4.4.1 Recursion pharmaceuticals
12.4.4.2 Atomwise
12.4.4.3 Deep genomics
12.4.4.4 Cloud pharmaceuticals
12.4.5 LIFESTYLE MANAGEMENT & MONITORING
12.4.5.1 Welltok
12.4.5.2 Vitagene
12.4.5.3 Lucina health
12.4.6 VIRTUAL ASSISTANTS
12.4.6.1 Next IT (A verint systems company)
12.4.6.2 Babylon
12.4.6.3 MDLIVE
12.4.7 WEARABLES
12.4.7.1 Magnea
12.4.7.2 PhysIQ
12.4.7.3 Cyrcadia health
12.4.8 EMERGENCY ROOM & SURGERY
12.4.8.1 Caresyntax
12.4.8.2 Gauss surgical
12.4.8.3 Perceive 3D
12.4.8.4 MaxQ AI
12.4.9 INPATIENT CARE & HOSPITAL MANAGEMENT
12.4.9.1 Qventus
12.4.9.2 WorkFusion
12.4.10 RESEARCH
12.4.10.1 iCarbonX
12.4.10.2 Desktop genetics
12.4.11 CYBERSECURITY
12.4.11.1 Darktrace
12.4.11.2 Cylance
12.4.11.3 LexisNexis risk solutions
12.4.11.4 Securonix
12.4.12 MENTAL HEALTH
12.4.12.1 Ginger.io
12.4.12.2 X2AI
12.4.12.3 BioBeats
12.4.13 HEALTHCARE ASSISTANCE ROBOTS
12.4.13.1 Pillo
12.4.13.2 Catalia health
13 APPENDIX (Page No. - 246)
13.1 INSIGHTS FROM 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
LIST OF TABLES (105 Tables)
TABLE 1 AI IN HEALTHCARE MARKET, BY OFFERING, 2017–2026 (USD MILLION)
TABLE 2 AI IN HEALTHCARE MARKET, BY HARDWARE, 2017–2026 (USD MILLION)
TABLE 3 AI IN HEALTHCARE MARKET FOR HARDWARE, BY REGION, 2017–2026 (USD MILLION)
TABLE 4 AI IN HEALTHCARE MARKET, BY PROCESSOR, 2017–2026 (USD MILLION)
TABLE 5 AI IN HEALTHCARE MARKET, BY SOFTWARE, 2017–2026 (USD MILLION)
TABLE 6 AI IN HEALTHCARE MARKET FOR SOFTWARE, BY REGION, 2017–2026 (USD MILLION)
TABLE 7 AI IN HEALTHCARE MARKET FOR SOLUTION, BY DEPLOYMENT, 2017–2026 (USD MILLION)
TABLE 8 AI IN HEALTHCARE MARKET FOR SOFTWARE, BY PLATFORM, 2017–2026 (USD MILLION)
TABLE 9 AI IN HEALTHCARE MARKET, BY SERVICES, 2017–2026 (USD MILLION)
TABLE 10 AI IN HEALTHCARE MARKET FOR SERVICES, BY REGION, 2017–2026 (USD MILLION)
TABLE 11 AI IN HEALTHCARE MARKET, BY TECHNOLOGY, 2017–2026 (USD MILLION)
TABLE 12 AI IN HEALTHCARE MARKET FOR MACHINE LEARNING, BY TYPE, 2017–2026 (USD MILLION)
TABLE 13 AI IN HEALTHCARE MARKET FOR NATURAL LANGUAGE PROCESSING, BY TYPE, 2017–2026 (USD MILLION)
TABLE 14 AI IN HEALTHCARE MARKET FOR CONTEXT-AWARE COMPUTING, BY TYPE, 2017–2026 (USD MILLION)
TABLE 15 AI IN HEALTHCARE MARKET, BY END-USE APPLICATION, 2017–2026 (USD MILLION)
TABLE 16 AI IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY REGION, 2017–2026 (USD MILLION)
TABLE 17 AI IN HEALTHCARE MARKET FOR PATIENT DATA AND RISK ANALYSIS, BY END USER, 2017–2026 (USD MILLION)
TABLE 18 AI IN HEALTHCARE MARKET FOR INPATIENT CARE & HOSPITAL MANAGEMENT, BY REGION, 2017–2026 (USD MILLION)
TABLE 19 AI IN HEALTHCARE MARKET FOR INPATIENT CARE AND HOSPITAL MANAGEMENT, BY END USER, 2017–2026 (USD MILLION)
TABLE 20 AI IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY REGION, 2017–2026 (USD MILLION)
TABLE 21 AI IN HEALTHCARE MARKET FOR MEDICAL IMAGING AND DIAGNOSTICS, BY END USER, 2017–2026 (USD MILLION)
TABLE 22 AI IN HEALTHCARE MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY REGION, 2017–2026 (USD MILLION)
TABLE 23 AI IN HEALTHCARE MARKET FOR LIFESTYLE MANAGEMENT & MONITORING, BY END USER, 2017–2026 (USD MILLION)
TABLE 24 AI IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANT, BY REGION, 2017–2026 (USD MILLION)
TABLE 25 AI IN HEALTHCARE MARKET FOR VIRTUAL ASSISTANTS, BY END USER, 2017–2026 (USD MILLION)
TABLE 26 AI IN HEALTHCARE MARKET FOR DRUG DISCOVERY, BY REGION, 2017–2026 (USD MILLION)
TABLE 27 AI IN HEALTHCARE MARKET FOR DRUG DISCOVERY, BY END USER, 2017–2026 (USD MILLION)
TABLE 28 AI IN HEALTHCARE MARKET FOR RESEARCH, BY REGION, 2017–2026 (USD MILLION)
TABLE 29 AI IN HEALTHCARE MARKET FOR RESEARCH, BY END USER, 2017–2026 (USD MILLION)
TABLE 30 AI IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY REGION, 2017–2026 (USD MILLION)
TABLE 31 AI IN HEALTHCARE MARKET FOR HEALTHCARE ASSISTANCE ROBOTS, BY END USER, 2017–2026 (USD MILLION)
TABLE 32 AI IN HEALTHCARE MARKET FOR PRECISION MEDICINE, BY REGION, 2017–2026 (USD MILLION)
TABLE 33 AI IN HEALTHCARE MARKET FOR PRECISION MEDICINE, BY END USER, 2017–2026 (USD MILLION)
TABLE 34 AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY REGION, 2017–2026 (USD MILLION)
TABLE 35 AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY, BY END USER, 2017–2026 (USD MILLION)
TABLE 36 AI IN HEALTHCARE MARKET FOR WEARABLES, BY REGION, 2017–2026 (USD MILLION)
TABLE 37 AI IN HEALTHCARE MARKET FOR WEARABLES, BY END USER, 2017–2026 (USD MILLION)
TABLE 38 AI IN HEALTHCARE MARKET FOR MENTAL HEALTH, BY REGION, 2017–2026 (USD MILLION)
TABLE 39 AI IN HEALTHCARE MARKET FOR MENTAL HEALTH, BY END USER, 2017–2026 (USD MILLION)
TABLE 40 AI IN HEALTHCARE MARKET FOR CYBERSECURITY, BY REGION, 2017–2026 (USD MILLION)
TABLE 41 AI IN HEALTHCARE MARKET FOR CYBERSECURITY, BY END USER, 2017–2026 (USD MILLION)
TABLE 42 AI IN HEALTHCARE MARKET, BY END USER, 2017–2026 (USD MILLION)
TABLE 43 AI IN HEALTHCARE MARKET FOR HOSPITAL AND HEALTHCARE PROVIDERS, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 44 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS, BY REGION, 2017–2026 (USD MILLION)
TABLE 45 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 46 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 47 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 48 AI IN HEALTHCARE MARKET FOR HOSPITALS AND HEALTHCARE PROVIDERS IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 49 AI IN HEALTHCARE MARKET FOR PATIENTS, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 50 AI IN HEALTHCARE MARKET FOR PATIENTS, BY REGION, 2017–2026 (USD MILLION)
TABLE 51 AI IN HEALTHCARE MARKET FOR PATIENTS IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 52 AI IN HEALTHCARE MARKET FOR PATIENTS IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 53 AI IN HEALTHCARE MARKET FOR PATIENTS IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 54 AI IN HEALTHCARE MARKET FOR PATIENTS IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 55 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 56 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES, BY REGION, 2017–2026 (USD MILLION)
TABLE 57 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 58 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 59 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 60 AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 61 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 62 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS, BY REGION, 2017–2026 (USD MILLION)
TABLE 63 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 64 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 65 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 66 AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 67 AI IN HEALTHCARE MARKET FOR OTHERS, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 68 AI IN HEALTHCARE MARKET FOR OTHERS, BY REGION, 2017–2026 (USD MILLION)
TABLE 69 AI IN HEALTHCARE MARKET FOR OTHERS IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 70 AI IN HEALTHCARE MARKET FOR OTHERS IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 71 AI IN HEALTHCARE MARKET FOR OTHERS IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 72 AI IN HEALTHCARE MARKET FOR OTHERS IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 73 AI IN HEALTHCARE MARKET, BY REGION, 2017–2026 (USD MILLION)
TABLE 74 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 75 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY END USER, 2017–2026 (USD MILLION)
TABLE 76 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY OFFERING, 2017–2026 (USD MILLION)
TABLE 77 AI IN HEALTHCARE MARKET IN NORTH AMERICA, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 78 AI IN HEALTHCARE MARKET IN US, BY END USER, 2017–2026 (USD MILLION)
TABLE 79 AI IN HEALTHCARE MARKET IN CANADA, BY END USER, 2017–2026 (USD MILLION)
TABLE 80 AI IN HEALTHCARE MARKET IN MEXICO, BY END USER, 2017–2026 (USD MILLION)
TABLE 81 AI IN HEALTHCARE MARKET IN EUROPE, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 82 AI IN HEALTHCARE MARKET IN EUROPE, BY END USER, 2017–2026 (USD MILLION)
TABLE 83 AI IN HEALTHCARE MARKET IN EUROPE, BY OFFERING, 2017–2026 (USD MILLION)
TABLE 84 AI IN HEALTHCARE MARKET IN EUROPE, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 85 AI IN HEALTHCARE MARKET IN GERMANY, BY END USER, 2017–2026 (USD MILLION)
TABLE 86 AI IN HEALTHCARE MARKET IN UK, BY END USER, 2017–2026 (USD MILLION)
TABLE 87 AI IN HEALTHCARE MARKET IN FRANCE, BY END USER, 2017–2026 (USD MILLION)
TABLE 88 AI IN HEALTHCARE MARKET IN ITALY, BY END USER, 2017–2026 (USD MILLION)
TABLE 89 AI IN HEALTHCARE MARKET IN SPAIN, BY END USER, 2017–2026 (USD MILLION)
TABLE 90 AI IN HEALTHCARE MARKET IN REST OF EUROPE, BY END USER, 2017–2026 (USD MILLION)
TABLE 91 AI IN HEALTHCARE MARKET IN APAC, BY COUNTRY, 2017–2026 (USD MILLION)
TABLE 92 AI IN HEALTHCARE MARKET IN APAC, BY END USER, 2017–2026 (USD MILLION)
TABLE 93 AI IN HEALTHCARE MARKET IN APAC, BY OFFERING, 2017–2026 (USD MILLION)
TABLE 94 AI IN HEALTHCARE MARKET IN APAC, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 95 AI IN HEALTHCARE MARKET IN CHINA, BY END USER, 2017–2026 (USD MILLION)
TABLE 96 AI IN HEALTHCARE MARKET IN JAPAN, BY END USER, 2017–2026 (USD MILLION)
TABLE 97 AI IN HEALTHCARE MARKET IN SOUTH KOREA, BY END USER, 2017–2026 (USD MILLION)
TABLE 98 AI IN HEALTHCARE MARKET IN INDIA, BY END USER, 2017–2026 (USD MILLION)
TABLE 99 AI IN HEALTHCARE MARKET IN REST OF ASIA PACIFIC, BY END USER, 2017–2026 (USD MILLION)
TABLE 100 AI IN HEALTHCARE MARKET IN ROW, BY REGION, 2017–2026 (USD MILLION)
TABLE 101 AI IN HEALTHCARE MARKET IN ROW, BY END USER, 2017–2026 (USD MILLION)
TABLE 102 AI IN HEALTHCARE MARKET IN ROW, BY OFFERING, 2017–2026 (USD MILLION)
TABLE 103 AI IN HEALTHCARE MARKET IN ROW, BY APPLICATION, 2017–2026 (USD MILLION)
TABLE 104 AI IN HEALTHCARE MARKET IN SOUTH AMERICA, BY END USER, 2017–2026 (USD MILLION)
TABLE 105 AI IN HEALTHCARE MARKET IN MIDDLE EAST & AFRICA, BY END USER, 2017–2026 (USD MILLION)
LIST OF FIGURES (59 Tables)
FIGURE 1 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN
FIGURE 2 MARKET SIZE ESTIMATION METHODOLOGY: APPROACH 1 (SUPPLY SIDE)—REVENUE GENERATED FROM AI IN HEALTHCARE OFFERINGS
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
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
FIGURE 7 DATA TRIANGULATION
FIGURE 8 ASSUMPTIONS FOR THE RESEARCH STUDY
FIGURE 9 AI IN HEALTHCARE MARKET, BY OFFERING, 2020 VS. 2026 (USD MILLION)
FIGURE 10 AI IN HEALTHCARE MARKET, BY PROCESSOR, 2020 VS. 2026 (USD MILLION)
FIGURE 11 AI IN HEALTHCARE MARKET, BY TECHNOLOGY, 2017–2026 (USD MILLION)
FIGURE 12 AI IN HEALTHCARE MARKET, BY APPLICATION, 2020 VS. 2026 (USD MILLION)
FIGURE 13 AI IN HEALTHCARE MARKET, BY END USER, 2020 VS. 2026 (USD MILLION)
FIGURE 14 AI IN HEALTHCARE MARKET, BY REGION, 2020
FIGURE 15 ANALYZING AI IN HEALTHCARE MARKET SCENARIOS
FIGURE 16 AVAILABILITY OF BIG DATA IN HEALTHCARE AND INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES ARE MAJOR FACTORS DRIVING MARKET GROWTH
FIGURE 17 SOFTWARE TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
FIGURE 18 AI IN HEALTHCARE MARKET FOR MACHINE LEARNING TO HOLD LARGEST SIZE FROM 2020 TO 2026
FIGURE 19 GERMANY EXPECTED TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET IN 2020 IN EUROPE
FIGURE 20 US TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET IN 2020
FIGURE 21 INCREASINGLY LARGE AND COMPLEX DATASETS AND GROWING DEMAND TO REDUCE HEALTHCARE COSTS ARE DRIVING MARKET GROWTH
FIGURE 22 TYPES OF HEALTHCARE BREACHES REPORTED TO US DEPARTMENT OF HEALTH AND HUMAN SERVICES (2018 TO 2020)
FIGURE 23 AI IN HEALTHCARE MARKET VALUE CHAIN IN 2019
FIGURE 24 SOFTWARE TO HOLD LARGEST SIZE OF AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
FIGURE 25 AI IN HEALTHCARE PROCESSOR MARKET FOR GPU TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 26 NORTH AMERICA TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET FOR SOFTWARE DURING FORECAST PERIOD
FIGURE 27 MACHINE LEARNING TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
FIGURE 28 MEDICAL IMAGING & DIAGNOSTICS TO ACCOUNT FOR LARGEST SIZE OF AI IN HEALTHCARE MARKET IN 2026
FIGURE 29 NORTH AMERICA TO LEAD LIFESTYLE MANAGEMENT & MONITORING APPLICATION DURING FORECAST PERIOD
FIGURE 30 NORTH AMERICA TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET FOR RESEARCH DURING FORECAST PERIOD
FIGURE 31 NORTH AMERICA TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET FOR EMERGENCY ROOM & SURGERY APPLICATION IN 2020
FIGURE 32 HOSPITAL AND HEALTHCARE PROVIDERS TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET IN 2020 AND 2026
FIGURE 33 VIRTUAL ASSISTANTS APPLICATION TO HOLD LARGEST SHARE OF AI IN HEALTHCARE MARKET FOR PATIENTS DURING FORECAST PERIOD
FIGURE 34 MEDICAL IMAGING & DIAGNOSTICS APPLICATION TO WITNESS HIGHEST CAGR IN AI IN HEALTHCARE MARKET FOR PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES DURING FORECAST PERIOD
FIGURE 35 CYBERSECURITY APPLICATION TO HOLD LARGER SHARE OF AI IN HEALTHCARE MARKET FOR HEALTHCARE PAYERS DURING FORECAST PERIOD
FIGURE 36 CHINA AND US ARE EMERGING AS NEW HOTSPOTS FOR AI IN HEALTHCARE MARKET
FIGURE 37 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 38 NORTH AMERICA: SNAPSHOT OF AI IN HEALTHCARE MARKET
FIGURE 39 NORTH AMERICA AI IN HEALTHCARE MARKET FOR SERVICE OFFERINGS TO CAPTURE HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 40 EUROPE: SNAPSHOT OF AI IN HEALTHCARE MARKET
FIGURE 41 HOSPITALS & PROVIDERS SEGMENT TO HOLD LARGEST SHARE OF EUROPEAN AI IN HEALTHCARE MARKET IN 2020
FIGURE 42 APAC: SNAPSHOT OF AI IN HEALTHCARE MARKET
FIGURE 43 SERVICE OFFERINGS TO WITNESS HIGHEST CAGR IN APAC AI IN HEALTHCARE MARKET DURING FORECAST PERIOD
FIGURE 44 ROW: SNAPSHOT OF AI IN HEALTHCARE MARKET
FIGURE 45 HOSPITALS & PROVIDERS TO BE LARGEST END USER SEGMENT IN ROW AI IN HEALTHCARE MARKET IN 2020
FIGURE 46 KEY DEVELOPMENTS ADOPTED BY TOP PLAYERS IN AI IN HEALTHCARE MARKET FROM 2017 TO MID-2020
FIGURE 47 RANKING OF KEY COMPANIES IN AI IN HEALTHCARE MARKET (2019)
FIGURE 48 AI IN HEALTHCARE MARKET (GLOBAL) COMPETITIVE LEADERSHIP MAPPING, 2019
FIGURE 49 COLLABORATIONS, PARTNERSHIPS, AND STRATEGIC ALLIANCES WAS THE KEY STRATEGY ADOPTED BY MARKET PLAYERS FROM JANUARY 2017 TO MARCH 2020
FIGURE 50 NVIDIA: COMPANY SNAPSHOT
FIGURE 51 INTEL: COMPANY SNAPSHOT
FIGURE 52 IBM: COMPANY SNAPSHOT
FIGURE 53 GOOGLE: COMPANY SNAPSHOT
FIGURE 54 MICROSOFT: COMPANY SNAPSHOT
FIGURE 55 GE: COMPANY SNAPSHOT
FIGURE 56 SIEMENS HEALTHINEERS: COMPANY SNAPSHOT
FIGURE 57 MEDTRONIC: COMPANY SNAPSHOT
FIGURE 58 MICRON TECHNOLOGY: COMPANY SNAPSHOT
FIGURE 59 AWS: COMPANY SNAPSHOT
The study involved the estimation of the current size of the AI in healthcare market. Exhaustive secondary research was conducted to collect information on the market, its peer markets, and its parent market. This was followed by the validation of these findings, assumptions, and sizing with the industry experts identified across the value chain through primary research. Both the top-down and bottom-up approaches were employed to estimate the overall size of the market. It was followed by the market breakdown and data triangulation procedures, which were used to estimate the size of the market based on different segments and subsegments.
Secondary Research
In the secondary research process, various secondary sources were referred to for the identification and collection of relevant information for this study on the AI in healthcare market. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers; journals and certified publications; and articles by recognized authors, websites, directories, and databases. Secondary research was conducted to obtain the key information regarding the supply chain and value chain of the industry, total pool of key players, market segmentation according to industry trends (to the bottom-most level), geographic markets, and key developments from market- and technology-oriented perspectives. Secondary data was collected and analyzed to arrive at the overall size of the AI in healthcare market, which was further validated by primary research.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information relevant to this report. Several primary interviews were conducted with the market experts from both demand and supply sides. The primary data was collected through questionnaires, emails, and telephonic interviews. Primary sources included industry experts, such as chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology and innovation directors, and related executives from various key companies and organizations operating in the AI Healthcare Market.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
The top-down and bottom-up approaches were implemented to estimate and validate the total size of the AI in healthcare market. These methods were used extensively to estimate the size of the market based on various segments and subsegments. The research methodology used to estimate the market size included the following steps:
- Key players in the industry were identified through extensive secondary research.
- The industry’s supply chain was identified, and the market size, in terms of value, was determined through primary and secondary research processes.
- All percentage shares, splits, and breakdowns were determined using secondary sources and verified through primary sources.
Data Triangulation
After arriving at the overall size of the AI in healthcare market—using the market size estimation processes as explained above—the market was split into several segments and subsegments. The data triangulation and market breakdown procedures were employed, wherever applicable, to complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment. The data was triangulated by studying various factors and trends from the demand and supply sides across different applications.
Study Objectives
- To describe and forecast the artificial intelligence (AI) in healthcare market, in terms of value,
by offering, technology, end-use application, end user. - To describe and forecast the AI in healthcare market, in terms of value, for 4 regions—
North America, Europe, Asia Pacific (APAC), and 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 micromarkets with respect to individual growth trends, prospects, and contributions to the total market
- To profile key players and comprehensively analyze their market position in terms of ranking and core competencies, and provide a detailed competitive landscape of the market
- To analyze 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
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- What are the Known and Unknown Adjacencies Impacting the Artificial Intelligence in Healthcare Market
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Growth opportunities and latent adjacency in Artificial Intelligence in Healthcare Market
I am conducting a research project on AI in healthcare as a part of my MHA/MBA marketing course. Could you share some relevant information in the form of sample brochure and estimated cost of the report, post discount mentioned on the website?
I am interested in understanding the market size and related insights on computer-assisted physician documentation (CAPD), clinical documentation improvement (CDI), computer-assisted coding (CAC), ambient voice and voice assistants, NLP, and machine learning for clinical, operational, and financial healthcare scenarios in AI in healthcare.
We are redeveloping our chart for Artificial Intelligence in Healthcare Market. Does your report covers regional market insights.
I am an automation enthusiast and would like to understand the impact of AI in healthcare. Could you provide me some brochure and sample to get into details.
I am looking to purchase this report to see the implications of AI on the workforce in Norway.
We have specific interests in global AI in healthcare market and the US AI in healthcare market. Any further details related to market size of AI for early disease detection (for global and USA) would be appreciated.
I was going through the ToC of AI in Healthcare market, I would like to understand, what are the requirements to perform in the fields of AI?
I was going through the ToC of AI in Healthcare market, I would like to understand, what are the requirements to perform in the fields of AI?