Artificial Intelligence in Healthcare Market with COVID-19 Impact Analysis by Offering, Technology, End-Use Application, End-user and Region - Global Forecast to 2026

Dublin, June 25, 2020 (GLOBE NEWSWIRE) -- The "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" report has been added to ResearchAndMarkets.com's offering.

The AI in healthcare market is expected to be valued at USD 4.9 billion in 2020 and is likely to 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 growth of the market are the increasing volume of healthcare data and growing complexities of datasets, the intensifying need to reduce towering 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 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. The major restraint for the market is the reluctance among medical practitioners to adopt AI-based technologies and lack of a skilled workforce.

MPU processor segment expected to hold the largest share in AI in healthcare in 2020

An MPU contains all or most of the CPU functions and is the engine that goes into motion when the computer is on. A microprocessor is specially designed to perform arithmetic and logic operations that use small number-holding areas called registers. Typical microprocessor operations include adding, subtracting, comparing two numbers, and fetching numbers. These operations are the result of a set of instructions that are part of the microprocessor design.

AI in the healthcare market for machine learning projected to grow at the highest CAGR during the 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.

Patient data & risk analysis segment to capture the largest share of AI in the healthcare market

The growth of the patient data & risk analysis segment is attributed to the increasing adoption of EMRs and various advantages offered by AI systems to healthcare service providers, patients, pharmaceuticals companies, and payers.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary
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
4.1 Attractive Opportunities in AI in the 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
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
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
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
8.1 Introduction
8.2 Patient Data and Risk Analysis
8.3 Inpatient Care & Hospital Management
8.4 Medical Imaging & Diagnostics
8.5 Lifestyle Management & Remote Patient Monitoring
8.6 Virtual Assistants
8.7 Drug Discovery
8.8 Research
8.9 Healthcare Assistance Robots
8.10 Precision Medicine
8.11 Emergency Room & Surgery
8.12 Wearables
8.13 Mental Health
8.14 Cybersecurity

9 Artificial Intelligence in Healthcare Market, by End-user
9.1 Introduction
9.2 Hospitals and Healthcare Providers
9.3 Patients
9.4 Pharmaceuticals & Biotechnology Companies
9.5 Healthcare Payers
9.6 Others

10 Artificial Intelligence in Healthcare Market, by Region
10.1 Introduction
10.2 North America
10.3 Europe
10.4 Asia-Pacific
10.5 Rest of the World

11 Competitive Landscape
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
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)
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

For more information about this report visit https://www.researchandmarkets.com/r/hzronp

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