Artificial Intelligence (AI) is mainly applicable in treatment procedures, medication management and drug discovery. Increasing demand for treatments customised according to the requirements of an individual patient is predicted to boost the market growth. Furthermore, AI technology is employed to implement data mining to accelerate healthcare delivery services will increase the adoption of AI technology worldwide. Additionally, the emanation of promising and novel applications for diagnosis and monitoring of diseases will prove to be an integral factor responsible for the significant market growth.
Global Artificial Intelligence in Healthcare Market Overview:
Based on technology, the market is bifurcated into natural language processing (NLP), machine learning, context-aware computing and computer vision. Machine learning segment is further sub-segmented as deep learning, supervised learning, unsupervised learning and reinforcement learning.
In terms of offering, the market is categorised as software, hardware and services. The hardware segment further bifurcated into processor and memory while the services segment is further sub-segmented as deployment and integration and support & maintenance.
Depending on the end-use application, the market is bifurcated into in-patient care & hospital, patient data and risk analysis, medical imaging & diagnosis, lifestyle management & monitoring, virtual assistant, healthcare assistant robots, wearable and mental health. Apple watch series 4 was recently granted de novo status making it a class 2 medical device. This has opened new opportunities for the wearables segment to exhibit a soaring market growth globally.
End-user-wise, the market is categorised as patients, hospitals, biotechnology companies and high-tech hospitals & research labs
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Global Artificial Intelligence in Healthcare Market Segmentation:
1. By Region
• North America
• Europe
• Asia Pacific
• Latin America
• Rest of the World
2. By Technology
• Natural Language Processing (NLP)
• Machine Learning
o Deep Learning
o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning

• Context-Aware Computing
• Computer Vision
3. By Offering
• Software

• Hardware
o Processor
o Memory

• Services
o Deployment & Integration
o Support & Maintenance
4. By End-Use Application
• In-Patient Care & Hospital
• Patient Data and Risk Analysis
• Medical Imaging & Diagnostics
• Lifestyle Management & Monitoring
• Virtual Assistant
• Healthcare Assistant Robots
• Wearable
• Mental Health
5. By End User
• Patients
• Hospitals
• Biotechnology Companies
• High-tech Hospitals and Research Labs

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Competitive Landscape:
• Tier 1 players- established companies in the market with a major market share
• Tier 2 players
• Emerging players which are growing rapidly
• New Entrants
Key Market Players:
• IBM (Watson Health)
• AiCure
• APIXIO Inc.
• iCarbonX
• Insilico Medicine Inc.
• Sophia Genetics
• Welltok
• Zebra Medical Vision Ltd.
FutureWise Key Takeaways:
• With an ever-increasing patient population, the healthcare database volume shall be consistently increasing thus making the AI technology more crucial for database management
• Deep learning technology is currently at its nascent stage and further advancements in this technology shall expand the market significantly

Objectives of the Study:
• To provide with an exhaustive analysis on the artificial intelligence in healthcare market by region, by technology, by offering, by end-use application, by end user
• To cater comprehensive information on factors impacting market growth (drivers, restraints, opportunities, and industry-specific restraints)
• To evaluate and forecast micro-markets and the overall market
• To predict the market size, in key regions (along with countries)— North America, Europe, Asia Pacific, Latin America and rest of the world
• To record evaluate and competitive landscape mapping- product launches, technological advancements, mergers and expansions
• Profiling of companies to evaluate their market shares, strategies, financials and core competencies


Table of Contents
1. Introduction
1.1. Scope and Objective
1.2. Assumptions and Acronyms
1.3. Forecast Factors
1.4. Research Methodology
2. Executive Summary
2.1. Industry Cluster Analysis
2.2. Competition Matrix
2.3. Strategies Recommendations
3. Market Definition
3.1. Report Scope (Inclusions & Exclusions)
3.2. Market Segmentation
4. Global Artificial Intelligence in Healthcare Market Overview
4.1. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn)

5. Key Inclusions
5.1. Porter’s Five Force analysis
5.2. Market Dynamics
5.3. Industry Trends
5.4. Regulatory Guidelines
5.5. Opportunities for Artificial Intelligence in Healthcare Market
6. Competition Dynamics
6.1. Company Share Analysis (2019)
7. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029 by Technology
7.1. Key Market Findings
7.2. Machine Learning
7.2.1 Deep Learning
7.2.2 Supervised Learning
7.2.3 Reinforcement Learning
7.2.4 Unsupervised Learning
7.3. Natural Language Processing
7.4. Context-Aware Computing
7.5. Computer Vision
8. Global Artificial Intelligence in Healthcare Market Market Revenue (USD Mn), 2019-2029 by End Use Application
8.1. Key Market Findings
8.2. Inpatient Care & Hospital
8.3. Patient Data and Risk Analysis
8.4. Medical Imaging & Diagnostics
8.5. Lifestyle Management & Monitoring
8.6. Virtual Assistant
8.7. Healthcare Assistance Robots
8.8. Wearable
8.9. Mental Health
9. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029 by Offering
9.1. Key Market Findings
9.2. Hardware (Processor, Memory)
9.3. Computing Architecture (Network)
9.4. Software
9.5. Solutions (Cloud, AI Platform, Own ML Frameworks)
10. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029 by End User
10.1. Key Market Findings
10.2. Patients
10.3. Hospitals and Providers
10.4. Biotechnology Companies
10.5. High tech Hospitals and Research labs (Healthcare Assistance Robots)

11. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029 by Region
11.1. Key Market Findings
11.2. Long Term ROI Segments
11.3. Revenue Opportunity Influencing Factors
11.4. Market Revenue (USD Mn) Assessment and Forecast by Region,2029
11.4.1 North America
11.4.2 Latin America
11.4.3 Europe
11.4.4 Asia Pacific
11.4.5 Rest of world
12. North America Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-2029
12.1. Key Market Findings
12.2. Long Term ROI Segments
12.3. Revenue Opportunity Influencing Factors
12.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
12.4.1 US
12.4.2 Canada
13. Latin America Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-2029
13.1. Key Market Findings
13.2. Long Term ROI Segments
13.3. Revenue Opportunity Influencing Factors
13.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
13.4.1 Brazil
13.4.2 Mexico
13.4.3 Argentina
13.4.4 Rest of Latin America
14. Europe America Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-2029
14.1. Key Market Findings
14.2. Long Term ROI Segments
14.3. Revenue Opportunity Influencing Factors
14.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
14.4.1 Germany
14.4.2 France
14.4.3 Spain
14.4.4 UK
14.4.5 Russia
14.4.6 Poland
14.4.7 Rest of Europe
15. Asia Pacific Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-2029
15.1. Key Market Findings
15.2. Long Term ROI Segments
15.3. Revenue Opportunity Influencing Factors
15.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
15.4.1 Emerging Asia
15.4.1.1 China
15.4.1.2 India
15.4.1.3 ASEAN-5
15.4.1.4 Rest of Emerging Asia
15.4.2 Japan
16. Rest of World Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-2029
16.1. Key Market Findings
16.2. Long Term ROI Segments
16.3. Revenue Opportunity Influencing Factors
16.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
16.4.1 Middle East
16.4.2 South Africa
16.4.3 North Africa
16.4.4 Rest of World
16.4.5 Others
17. Company Profiles
17.1. Competition Landscape
17.2. Global Company Share (USD Mn) Overview, 2019
17.3. Company Profiles
17.3.1 Nvidia
17.3.1.1 Corporate Overview
17.3.1.2 Financial Performance
17.3.1.3 Peer Comparison & 3C marketing equation
17.3.1.4 Company Strategy & Channel Management
17.3.2 Intel
17.3.2.1 Corporate Overview
17.3.2.2 Financial Performance
17.3.2.3 Peer Comparison & 3C marketing equation
17.3.2.4 Company Strategy & Channel Management
17.3.3 Google
17.3.3.1 Corporate Overview
17.3.3.2 Financial Performance
17.3.3.3 Peer Comparison & 3C marketing equation
17.3.3.4 Company Strategy & Channel Management
17.3.4 IBM
17.3.4.1 Corporate Overview
17.3.4.2 Financial Performance
17.3.4.3 Peer Comparison & 3C marketing equation
17.3.4.4 Company Strategy & Channel Management
17.3.5 Microsoft
17.3.5.1 Corporate Overview
17.3.5.2 Financial Performance
17.3.5.3 Peer Comparison & 3C marketing equation
17.3.5.4 Company Strategy & Channel Management
17.3.6 Medtronic
17.3.6.1 Corporate Overview
17.3.6.2 Financial Performance
17.3.6.3 Peer Comparison & 3C marketing equation
17.3.6.4 Company Strategy & Channel Management
17.3.7 General Electric
17.3.7.1 Corporate Overview
17.3.7.2 Financial Performance
17.3.7.3 Peer Comparison & 3C marketing equation
17.3.7.4 Company Strategy & Channel Management
17.3.8 Amazon Web Services
17.3.8.1 Corporate Overview
17.3.8.2 Financial Performance
17.3.8.3 Peer Comparison & 3C marketing equation
17.3.8.4 Company Strategy & Channel Management
17.3.9 Micron Technology
17.3.9.1 Corporate Overview
17.3.9.2 Financial Performance
17.3.9.3 Peer Comparison & 3C marketing equation
17.3.9.4 Company Strategy & Channel Management
17.3.10 Pillo
17.3.10.1 Corporate Overview
17.3.10.2 Financial Performance
17.3.10.3 Peer Comparison & 3C marketing equation
17.3.10.4 Company Strategy & Channel Management
17.3.11 Catalia Health
17.3.11.1 Corporate Overview
17.3.11.2 Financial Performance
17.3.11.3 Peer Comparison & 3C marketing equation
17.3.11.4 Company Strategy & Channel Management
17.3.12 Ginger.Io
17.3.12.1 Corporate Overview
17.3.12.2 Financial Performance
17.3.12.3 Peer Comparison & 3C marketing equation
17.3.12.4 Company Strategy & Channel Management
17.3.13 BioBeats
17.3.13.1 Corporate Overview
17.3.13.2 Financial Performance
17.3.13.3 Peer Comparison & 3C marketing equation
17.3.13.4 Company Strategy & Channel Management
17.3.14 Icarbonx
17.3.14.1 Corporate Overview
17.3.14.2 Financial Performance
17.3.14.3 Peer Comparison & 3C marketing equation
17.3.14.4 Company Strategy & Channel Management
17.3.15 Qventus
17.3.15.1 Corporate Overview
17.3.15.2 Financial Performance
17.3.15.3 Peer Comparison & 3C marketing equation
17.3.15.4 Company Strategy & Channel Management
17.3.16 Caresyntax
17.3.16.1 Corporate Overview
17.3.16.2 Financial Performance
17.3.16.3 Peer Comparison & 3C marketing equation
17.3.16.4 Company Strategy & Channel Management
17.3.17 Gauss Surgical
17.3.17.1 Corporate Overview
17.3.17.2 Financial Performance
17.3.17.3 Peer Comparison & 3C marketing equation
17.3.17.4 Company Strategy & Channel Management
17.3.18 Perceive3d
17.3.18.1 Corporate Overview
17.3.18.2 Financial Performance
17.3.18.3 Peer Comparison & 3C marketing equation
17.3.18.4 Company Strategy & Channel Management
17.3.19 Next IT (A Verint Systems Company)
17.3.19.1 Corporate Overview
17.3.19.2 Financial Performance
17.3.19.3 Peer Comparison & 3C marketing equation
17.3.19.4 Company Strategy & Channel Management
17.3.20 Atomwise
17.3.20.1 Corporate Overview
17.3.20.2 Financial Performance
17.3.20.3 Peer Comparison & 3C marketing equation
17.3.20.4 Company Strategy & Channel Management
18. Research Sources & Primary Verbatim

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