Global Deep Learning in Medical Image Processing Market to 2030 with IBM, GE Healthcare and Google Entering into Strategic Alliances with Big Pharma Players

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Dec 16, 2020, 09:15 ET

DUBLIN, Dec. 16, 2020 /PRNewswire/ -- The "Deep Learning Market: Focus on Medical Image Processing, 2020-2030" report has been added to ResearchAndMarkets.com's offering.

The 'Deep Learning Market: Focus on Medical Image Processing, 2020-2030' report features an extensive study on the current market landscape offering an informed opinion on the likely adoption of such solutions over the next decade. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain.

One of the key objectives of this report was to estimate the existing market size and the future growth potential within the deep learning market (medical image processing segment), such as global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have developed informed estimates on the financial evolution of the market, over the period 2020-2030.

Moreover, several technology-focused innovators, such as (in alphabetical order) IBM, GE Healthcare and Google, have entered into strategic alliances with big pharma players, in order to bring proprietary deep learning-based medical solutions to the market. This upcoming segment of the pharmaceutical industry that exists at the interface between medicine and information technology, has garnered the attention of prominent venture capital firms and strategic investors. In the long term, the market is anticipated witness significant growth as more machine learning based solutions are approved for use.

Scope of the Report

A detailed review of the current market landscape of deep learning solutions for medical image processing, along with information on their status of development (launched/under development), regulatory approvals (FDA, CE mark, others), type of offering (diagnostic software/tool, diagnostic software/tool + device), type of image processed (X-ray, MRI, CT, ultrasound), application area (lung infections/respiratory disorders, brain injuries/disorders, lung cancer, cardiac conditions/cardiovascular disorders, bone deformities/orthopedic disorders, breast cancer and others).

In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies.

An in-depth analysis of the contemporary market trends, presented using three schematic representations, including [A] a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and [B] an insightful map representation highlighting the geographical activity of the players.
Elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.

Key Questions Answered

Who are the leading developers of deep learning-based solutions for medical image processing?
What are the key application areas for deep learning solutions designed for processing of medical images, such as X-Ray, ultrasound, CT, MRI and others?
How many solutions based on deep learning technology for processing of medical images have been cleared by FDA or have received CE marking?
What is the impact of COVID-19 on the demand for deep learning solutions designed for processing of medical images?
What is the likely valuation/net worth of companies involved in this segment?
What is the likely cost saving potential associated with the use of deep learning-based solutions for processing of medical images?
How is the current and future opportunity likely to be distributed across key market segments?
What is the potential usability of deep learning-based medical image processing solutions for lung scanning in COVID-19 patients?
Which partnership models are commonly adopted by stakeholders in this industry?
What is the overall trend of funding and investments in this domain?
What are the opinions of key opinion leaders involved in the deep learning space?

Key Topics Covered:

1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. The Science of Learning
3.3. Artificial Intelligence
3.4. The Big Data Revolution
3.5. Applications of Deep Learning in Healthcare

4. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND
4.1. Chapter Overview
4.2. International Business Machines (IBM)
4.3. Google
4.4. IBM versus Google: Artificial Intelligence-related Acquisitions
4.5. IBM versus Google: Healthcare Focused Partnerships and Collaborations
4.6. IBM versus Google: Primary Concerns and Future Outlook

5. MARKET OVERVIEW
5.1. Chapter Overview
5.2. Deep Learning in Medical Image Processing: Overall Market Landscape
5.3. Deep Learning in Medical Image Processing: Information on Key Characteristics
5.4. Deep Learning in Medical Image Processing: List of Companies

6. COMPANY PROFILES
6.1. Chapter Overview
6.2. Artelus
6.3. Arterys
6.4. Butterfly Network
6.5. ContextVision
6.6. Enlitic
6.7. Echonous
6.8. GE Healthcare
6.9. InferVision
6.10. VUNO

7. PARTNERSHIPS AND COLLABORATIONS
7.1. Chapter Overview
7.2. Partnership Models
7.3. Deep Learning in Medical Image Processing: List of Partnerships and Collaborations
7.4. Concluding Remarks

8. FUNDING AND INVESTMENT ANALYSIS
8.1. Chapter Overview
8.2. Types of Funding
8.3. Deep Learning in Medical Image Processing: Recent Funding Instances

9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Methodology
9.3. Categorization by Parameters

10. CASE STUDY: ANALYSIS OF DEEP LEARNING-BASED CLINICAL TRIALS REGISTERED IN THE US
10.1. Chapter Overview
10.2. Scope and Methodology
10.3 Clinical Trial Analysis

11. PATENT ANALYSIS
11.1. Chapter Overview
11.2. Scope and Methodology
11.3. Deep Learning and Medical Image Processing: Patent Analysis
11.4. Patent Valuation Analysis

12. COST SAVING ANALYSIS
12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions, 2020-2030
12.4. X-Ray Images
12.5. MRI Images
12.6. CT Images
12.7. Ultrasound Images
12.8. Concluding Remarks: Cost Saving Scenarios

13. MARKET FORECAST
13.1. Chapter Overview
13.2 Forecast Methodology and Key Assumptions
13.3 Overall Deep Learning in Medical Image Processing Market
13.3 Deep Learning in Medical Image Processing Market: Distribution by Application Area
13.4 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed
13.5 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions
13.6 Concluding Remarks

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
14.1. Chapter Overview
14.2. Industry Experts
14.3. University and Hospital Experts
14.4. Other Expert Opinions

15. INTERVIEW TRANSCRIPTS
15.1 Chapter Overview
15.2. Advenio Technosys
15.3. Arterys
15.4. Arya.ai
15.5. AlgoSurg
15.6. ContextVision

16. IMPACT OF COVID-19 OUTBREAK ON DEEP LEARNING MARKET DYNAMICS
16.1. Chapter Overview
16.2. Evaluation of Impact of COVID-19 Pandemic
16.3. Response Strategies: A Perspective

17. CONCLUSION

18. APPENDIX 1: TABULATED DATA

Companies Mentioned

8VC
Accel
Acequia Capital
Advantech Capital
Advenio Technosys
Aetion
Affidea
Agfa HealthCare
AiCure
Aidence
Aidoc
Alberta Innovates
AlbionVC
AlchemyAPI
Alder Hey Children's Hospital
AlgoMedica
AlgoSurg
Allen Institute for AI
ALMatter
Almaworks
Amazon Web Services
AME Cloud Ventures
AME Cloud Ventures
American Cancer Society
American Diabetes Association
American Heart Association
American Sleep Apnea Association
aMoon
Amplify Partners
Analytics Ventures
Anand Diagnostic Laboratory
Anthem
Antwerp University Hospital (UZA)
Apollo Hospitals
Apple
Apposite Capital
Artelus
Arterys
Arya.ai
Asan Medical Center
Asset Management Ventures
AT&T Labs
Atomico
Atrium Health
Aurum
Avicenna
Axilor Ventures
Ayce Capital
AZ Maria Middelares
Baidu.ventures
Baillie Gifford
Bar-llan University
Behold.ai
Beijing Dongfang Hongtai Technology
Beijing Hao Yun Dao Information & Technology (Paiyipai)
BenevolentAI
Benslie Investment Group
BI INVESTMENTS
Bill & Melinda Gates Foundation
BinomixRay
Bioinfogate
Biotechnology Industry Research Assistance Council (BIRAC)
Blackford Analysis
BlueCross BlueShield Venture
Boca Raton Regional Hospital
Boehringer Ingelheim
Boehringer Ingelheim
Bold Brain Ventures
Bold Capital Partners
Bolton NHS Foundation Trust
Boston Children's Hospital
Brainomix
Bridge Bank
Bridge to Health USA
Buckinghamshire Healthcare NHS Trust
Business Development Bank of Canada (BDC)
Butterfly Network
Cadens Medical Imaging
Campus Bio-Medico University Hospital
Canon Medical Systems
Capital Health
Capitol Health
Capricorn Partners
Caption Health
Carestream Health
CDH Investments
Cedars-Sinai
Cemag Invest
Cenkos Securities
Centre for Advanced Research in Imaging
ChainZ Medical Technology
Change Healthcare
Chimera Partners
Chiratae Ventures
Chiratae Ventures (Formerly IDG Ventures)
Clalit Research Institute
Cleveland Clinic
Clever Sense
Cloud DX
Co-Diagnostics
Cognea
Connect Ventures
Connecticut Innovations
ContextVision
CorTechs Labs
Cota Capital
Crouse Health
CRV (acquired by Microsoft)
Ctrip
CuraCloud
CureMetrix
Danhua Capital (DHVC)
Daotong Capital
Dark Blue Labs
Dartford and Gravesham NHS Trust
Dartmouth College
Data Collective
Data Collective (DCVC)
Dataminr
Deep Genomics
DeepMind
DeepTek
Deepwise
DEFTA Partners
Dell
DePuy Synthes
DiA Imaging Analysis
DigitalOcean
DNA Capital
DNNresearch
doc.ai
DocPanel
Dolby Family Ventures
Dong Kook Lifescience
Dr. Susan Love Foundation for Breast Cancer Research
Dubai Diabetes Center
Duke University
East Seattle Partners
EBSCO
EchoNous
Edan Instruments
Edwards Lifesciences
eInfochips
Elekta
Emergent Connect
Emergent Medical Partners
Emu Technology
Endiya Partners
Enlitic
Erlanger Health System
European Commission
Exigent Capital Group
Exilant Technologies
Exor
Explorys, an IBM Company
Fang Danhua Capital
fast.ai
FbStart
FemtoDx
Fertility Road
ff Venture Capital
Fidelis Care
Fidelity Investments
FIDI (Imaging Diagnostic Research Institute Foundation)
Forestay Capital
Forge
Formation 8
Fosun RZ Capital
Founder Friendly Labs (FFL)
Fractal Analytics
Frazier Healthcare Partners
Froedtert & the Medical College of Wisconsin Cancer Network
Frost Data Capital
Fujifilm Medical Systems USA
FUJIFILM Sonosite
Fujita Health University
Future Play Green Cross Holdings
Fysicon
Gachon University Gil Medical Center
GE Healthcare
GE Ventures
Genentech
gener8tor
General City Hospital, Aalst
Genesis Capital Advisors
Georges Harik
GF Securities
Google
Google Ventures
Government of Canada
Granata Decision Systems (acquired by Google)
Green House Ventures (GHV) Accelerator
Greenbox Venture Partners
Greenoaks Capital
Greycroft
Guerbet
Haitong Leading Capital Management
Halli Labs
HALO Diagnostics
Hanfor Capital Management
Hangzhou CognitiveCare
Harrow Council
HB Investment
Health Innovations
HealthKonnect India
HealthNet Global
HeartFlow
HelpAround
henQ
Hera Investment Funds
Herman Verrelst
Highmark Health
Holland Capital
Hongdao Capital
Hoxton Ventures
HTC
Huntington Hospital
Hyundai Investment Partners
IBM
iCAD
icometrix
iLabs Capital
Illumina
IMADIS Teleradiologie
Imagia Cybernetics
Imaging Biometrics
Imbio
ImFusion
IMM Investment
Imperial College London
Incepto
Indira IVF
Infervision
InHealth
INKEF Capital
In-Med Prognostics
Innova Salud
Innovacom
Innovate UK
Innovation Endeavors
InnovationQuarter
Institut Curie
Institute for Data Valorization (IVADO)
Intel
Intelerad Medical Systems
Intelligent Ultrasound
Intermountain Healthcare
Intervest
Intrasense
Invenshure
IQ Capital
iSchemaView (RapidAI)
iSono Health
Israel Innovation Authority
Jetpac (Justice Education Technology Political Advocacy Center)
Johns Hopkins University
Johnson & Johnson
joule
Kaggle
Kakao Ventures
Karos Health
KB Investment
Kentuckiana Health Collaborative (KHC)
Keshif Ventures
Kheiron Medical Technologies
Khosla Ventures
Kinzon Capital
Kinzon Capital
Kleiner Perkins
Koinvesticinis Fondas
Koios Medical
Konica Minolta
Korea Development Bank
Korea Telecom
Kt Investments
Kumamoto University
L2 Ventures
La Costa Investment Group
Legend Capital
Lenovo
Lenovo
LG CNS
Linkoping University
LPIXEL
LucidHealth
Lumenis
Luminous Ventures
Lunit
M3
Maccabi Healthcare Services
Mach7 Technologies
Manipal Hospitals
Marubeni
MassMutual Ventures (MMV)
MaxQ AI
Mayo Clinic
MBM Company
McGill University
MD Anderson Cancer Center
MedAxiom
MedGlobal
Medica Superspecialty Hospital
Mediscan Systems
MEDNAX
MedNetwork
MEDO.ai
Medsynaptic
MEDTEQ
Medtronic
Merge Healthcare
Methinks
Microsoft
Mindshare Medical
Minneapolis Heart Institute Ventures
Mirada Medical
Mirae Asset Venture Investment
MLP Care
Monash IVF
Montefiore Nyack Hospital
Montreal Institute for Learning Algorithms (MILA)
Moodstocks
Moorfields Eye Hospital
Morado Venture Partners
Mount Sinai Hospital
Myongji Hospital
Nanox
National Health Service (NHS) Trust
National Imaging Academy Wales
National Institute of General Medical Sciences
National Institutes of Health
National Science Foundation
Nauto
NeuralSeg
New York Genome Center (NYGC)
New York University (NYU)
NewMargin Ventures
NewYork-Presbyterian Hospital
Nico.lab
Nightingale Hospital
Nines
NMC Healthcare
Nobori
Nordic Medtech
Northwell Health
Northzone
Norwich Ventures
Novo Nordisk
NTT DATA
Nuance Communications
NVIDIA
NXC Imaging
Nyansa (now a part of VMware)
ODH Solutions
Olea Medical
Optellum
Optina Diagnostics
Optum Ventures
ORI Capital
OurCrowd
Ovation Fertility
Oxipit
Panorama Point Partners
Parkwalk Advisors
Partners HealthCare
Pathway Genomics
Pentathlon Ventures
Philips
Phytel, An IBM Company
pi Ventures
platform.ai
PointGrab
PowerCloud Venture Capital
Practica Capital
Prairie Cardiovascular
Precision Vascular
Presence Capital
Qiming Venture Partners
Qingsong Fund
Qualcomm Design
Quantib
Quest Diagnostics
QuEST Global
QUIBIM
Qure.ai
Rabo Ventures
RADLogics
RaySearch Laboratories
Realize
Red Hat
Regal Funds Management
Revelation Partners
Rhon-Klinikum
Riverain Technologies
Roche
Royal Berkshire NHS Foundation Trust
Royal United Hospitals
R-Pharm
Samsung
San Raffaele Hospital
Sana Kliniken
Satis Operations
SB Investment
SBRI Healthcare
ScreenPoint Medical
SeeAI
Segunda Lectura Diagnostica
Sejong Hospital
SELECT Healthcare Solutions
SEMA Translink Investment
SemanticMD
Semmelweis University
Sentient Technologies
Seoul National University Hospital
Sequoia Capital
ShengJing360
Shinhan Investment
Siemens Healthineers
SigTuple
Skope Magnetic Resonance Technologies
Smilegate Investment
SoftBank Ventures Asia
SpaceX
Square Peg Capital
SRI Ventures
St. John's College
Stanford University
StartX
Subtle Medical
Sunland Fund
Sunshine Insurance Group
Taihe Capital
Tech Transfer UPV
Tekes - the Finnish Funding Agency for Technology and Innovation
Telemedicine Clinic
Telerad Tech
Temasek
Temecula Valley Hospital
Tencent
TeraRecon
Terason
Teva Pharmaceuticals
Texas Medical Center
The Alan Turing Institute
The American College of Radiology (ACR) Data Science Institute(DSI)
The Inventor's Guild
The Israel Innovation Authority
The Jagen Group
The Oncopole
The Scottish Government
The Venture Reality Fund
Thorney Investment Group
Threshold Ventures
Tiatros
Timeful (acquired by Google)
TLV Partners
Tongdu Capital
Tracxn Technologies
Trakterm
Trillium Health Partners
Trusted Insight
Truven Health Analytics
Tsingyuan Ventures
Twitter Cortex
University of Antwerp
University of Bordeaux
University of California
University of Cambridge
University of Dundee
University of Edinburgh
University of Florida
University of Hertfordshire
University of Montreal
University of Oxford
University of Oxford
University of San Francisco
University of Sheffield
UW Medicine
Varian Medical Systems
VH Capital
Vision Factory
Vivo
Viz.ai
Vizyon
Volpara Solutions
VoxelCloud
VUNO
Wavemaker Partners
WeDoctor
Wellbeing Software
Wellington Management
Wisemont Capital
Wish
Women's Imaging Associates
XB Ventures
Xiang He Capital
Y Combinator
Yongin Severance Hospital
Zebra Medical Vision
ZhenFund

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