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Deep Learning Market: Focus on Medical Image Processing, 2020-2030

  • ID: 5201293
  • Report
  • August 2020
  • Region: Global
  • 344 Pages
  • Roots Analysis
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FEATURED COMPANIES

  • 8VC
  • ChainZ Medical Technology
  • Fosun RZ Capital
  • iSchemaView (RapidAI)
  • Nines
  • SigTuple
  • MORE

Overview

Deep learning is a machine learning approach that involves the use of intuitive algorithms and artificial neural networks to facilitate unsupervised pattern recognition/insight generation from large volumes of unstructured data. This technology is gradually being incorporated in a variety of applications across the healthcare sector, including imaging-based medical diagnosis and data processing. Specifically concerning medical imaging, deep learning has the potential to be used to automate information processing and result interpretation for a variety of diagnostic images, such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography. In this context, it is worth mentioning that the manual examination of medical images is limited, both in terms of accuracy (resulting in misdiagnosis) and throughput (leading to delays in communication of results). As a result, in situations characterized by low physician/pathologist to patient ratios, the conventional mode of operation is rendered inadequate. Experts have predicted a shortage of 10,000 to 40,000 physicians, by 2030, in the US alone. Further, it is estimated that 90% of medical data generated in hospitals is in the form of images; this puts an immense burden on radiologists and other consulting physicians related to processing such large volumes of data. In fact, according to a study published in the American Journal of Medicine, ~15% of reported medical cases in developed countries, are misdiagnosed. In addition, close to 1.5 million individuals are estimated to die each year, across the world, due to misdiagnosis. On the other hand, accurate diagnosis at an early stage has been demonstrated to allow significant cost savings for both patients and healthcare providers. In this scenario, deep learning and other artificial intelligence-based technologies are currently being developed/investigated to automate such processes.

Over time, various industry stakeholders have designed proprietary deep learning algorithms for processing of medical images. Presently, many innovators claim to have developed the means to train computers to read and triage medical images, and recognize patterns related to both temporal and spatial changes (which are not even visible to the naked eye). Experts in this field also believe that the use of deep learning can actually speed up the processing and interpretation of radiology data by 20%, reducing the rate of false positives by approximately 10%. It is also worth mentioning that in the past few years, the FDA has provided the necessary clearances and approved the use for a variety of deep learning software. 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

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.

In addition to other elements, the report provides

  • 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.
  • An analysis of the partnerships that have been inked by stakeholders in the domain, during the time period 2016-2020 (till June), covering research/development agreements, solution utilization agreements, solution integration agreements, marketing/distribution agreements, other relevant types of deals.
  • An analysis of the investments made, including seed financing, venture capital financing, debt financing, grants and others, in companies that are focused on developing deep learning-based solutions intended for processing of medical images.
  • An elaborate valuation analysis of companies that are involved in applying deep learning in solutions intended for processing of medical images. Further, we have built a multi-variable dependent valuation model to estimate the current valuation of a number of companies engaged in this domain.
  • A clinical trial analysis of completed, ongoing and planned studies (available on ct.gov), focused on the assessment deep learning-based software solutions, based on various parameters, such as trial registration year, trial recruitment status, trial design, target therapeutic area, leading industry and non-industry players, and geographical locations of trials.
  • An in-depth analysis of over 3,000 patents related to deep learning and medical images that have been filed/granted till June 2020, highlighting key trends associated with these patents, across type of patent, publication year and application year, regional applicability, CPC symbols, emerging focus areas, leading patent assignees (in terms of number of patents filed/granted), patent benchmarking and valuation.
  • An insightful analysis highlighting cost saving potential associated with the use of deep learning solutions intended for processing of medical images, based on information gathered from close to 30 countries, taking into consideration various parameters, such as total number of radiologists, annual salary of radiologists, number of scans performed (across each type of image) and increase in efficiency by adoption of deep learning solutions.
  • An insightful discussion on the views presented by various industry and non-industry experts present across the globe, on various portals, such as YouTube and other media platforms. The summary of insights provided by each expert is discussed across focus area, current industry status/challenges and future outlook.

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. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] application area (lung infections/respiratory disorders, brain injuries/disorders, lung cancer, cardiac conditions/cardiovascular disorders, bone deformities/orthopedic disorders, breast cancer and others), [B] type of image processed (X-ray, MRI, CT, ultrasound) and [C] region (North America, Europe and Asia Pacific/Rest of the World). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s growth.

The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain.

The report features detailed transcripts of interviews held with the following individuals (in alphabetical order):

  • Walter de Back (Research Scientist, Context Vision, Q2 2020)
  • Dr. Vikas Karade (CEO, AlgoSurg, Q2 2020)
  • Babak Rasolzadeh (Senior Director of Product, Arterys, Q2 2020)
  • Carla Leibowitz, (Head of Strategy and Marketing, Arterys, Q2 2017)
  • Mausumi Acharya, (CEO, Advenio Technosys, Q2 2017)
  • Deekshith Marla, (CTO, Arya.ai) and Sanjay Bhadra, (COO, Arya.ai, Q2 2017)

All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.

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?
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FEATURED COMPANIES

  • 8VC
  • ChainZ Medical Technology
  • Fosun RZ Capital
  • iSchemaView (RapidAI)
  • Nines
  • SigTuple
  • MORE

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
3.2.1. Teaching Machines
3.2.1.1. Machines for Computing
3.2.1.2. Artificial Intelligence for Understanding the Human Brain
3.3. Artificial Intelligence
3.4. The Big Data Revolution
3.4.1. Overview of Big Data
3.4.2. Role of Internet of Things (IoT)
3.4.3. Growing Adoption of Big Data
3.4.4. Key Application Areas
3.4.4.1. Big Data Analytics in Healthcare
3.4.4.2. Machine Learning
3.4.4.3. Deep Learning: The Amalgamation of Machine Learning and Big Data
3.5. Applications of Deep Learning in Healthcare
3.5.1. Personalized Medicine
3.5.2. Personal Fitness and Lifestyle Management
3.5.3. Drug Discovery
3.5.4. Clinical Trial Management
3.5.5. Medical Image Processing

4. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND
4.1. Chapter Overview
4.2. International Business Machines (IBM)
4.2.1. Company Overview
4.2.2. Financial Information
4.2.3. IBM Watson
4.3. Google
4.3.1. Company Overview
4.3.2. Financial Information
4.3.3. Google DeepMind
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
5.2.1. Analysis by Status of Development
5.2.1.1 Analysis by Regulatory Approvals Received
5.2.2. Analysis by Type of Offering
5.3.3. Analysis by Type of Image Processed
5.2.4. Analysis by Anatomical Region
5.2.5. Analysis by Application Area
5.2.6. Grid Representation: Analysis by Type of Offering, Type of Image Processed and Application Area
5.3. Deep Learning in Medical Image Processing: Information on Key Characteristics
5.4. Deep Learning in Medical Image Processing: List of Companies
5.4.1. Analysis by Year of Establishment
5.4.2. Analysis by Company Size
5.4.3. Analysis by Location of Headquarters
5.4.3.1. World Map Representation: Regional Activity
5.4.4. Analysis by Type of Deployment Model
5.4.5. Leading Companies: Analysis by Number of Solutions

6. COMPANY PROFILES
6.1. Chapter Overview
6.2. Artelus
6.2.1. Company Overview
6.2.2. Product/Technology Portfolio
6.2.3. Recent Developments and Future Outlook
6.3. Arterys
6.3.1. Company Overview
6.3.2. Product/Technology Portfolio
6.3.3. Recent Developments and Future Outlook
6.4. Butterfly Network
6.4.1. Company Overview
6.4.2. Product/Technology Portfolio
6.4.3. Recent Developments and Future Outlook
6.5. ContextVision
6.5.1. Company Overview
6.5.2. Product/Technology Portfolio
6.5.3. Recent Developments and Future Outlook
6.6. Enlitic
6.6.1. Company Overview
6.6.2. Product/Technology Portfolio
6.6.3. Recent Developments and Future Outlook
6.7. Echonous
6.7.1. Company Overview
6.7.2. Product/Technology Portfolio
6.7.3. Recent Developments and Future Outlook
6.8. GE Healthcare
6.8.1. Company Overview
6.8.2. Product/Technology Portfolio
6.8.3. Recent Developments and Future Outlook
6.9. InferVision
6.9.1. Company Overview
6.9.2. Product/Technology Portfolio
6.9.3. Recent Developments and Future Outlook
6.10. VUNO
6.10.1. Company Overview
6.10.2. Product/Technology Portfolio
6.10.3. Recent Developments and Future Outlook

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
7.3.1. Analysis by Year of Partnership
7.3.2. Analysis by Type of Partnership
7.3.3. Analysis by Year and Type of Partnership
7.3.4. Analysis by Type of Partner
7.3.5. Analysis by Therapeutic Area
7.3.6. Most Active Players: Analysis by Number of Partnerships
7.3.7. Regional Analysis
7.3.8. Intercontinental and Intracontinental Agreements
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
8.3.1. Analysis by Number of Funding Instances
8.3.2. Analysis by Amount Invested
8.3.3. Analysis by Type of Funding
8.3.4. Most Active Players: Analysis by Number of Funding Instances and Amount Invested
8.3.5. Most Active Investors: Analysis by Number of Funding Instances
8.3.6. Geographical Analysis by Amount Invested

9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Methodology
9.3. Categorization by Parameters
9.3.1. Twitter Followers Score
9.3.2. Google Hits Score
9.3.3. Partnerships Score
9.3.3. Weighted Average Score
9.3.4. Company Valuation: Proprietary Scores

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
10.3.1. Analysis by Trial Registration Year
10.3.2. Analysis by Trial Registration Year and Recruitment Status
10.3.3. Analysis by Trial Registration Year and Patient Enrollment
10.3.4. Analysis by Trial Design
10.3.5. Analysis by Patient Segment
10.3.6. Analysis by Therapeutic Area
10.3.7. Analysis by Trial Objective
10.3.8. Analysis by Focus Areas
10.3.9. Analysis by Type of Image Processed
10.3.8. Most Active Players: Analysis by Number of Clinical Trials
10.3.9. Analysis by Number of Clinical Trials and Geography
10.3.10. Analysis by Enrolled Patient Population and Geography

11. PATENT ANALYSIS
11.1. Chapter Overview
11.2. Scope and Methodology
11.3. Deep Learning and Medical Image Processing: Patent Analysis
11.3.1. Analysis by Application Year and Publication Year
11.3.2. Analysis by Issuing Authority/Patent Offices Involved
11.3.3. Analysis by IPCR Symbols
11.3.4. Emerging Focus Areas
11.3.5. Leading Assignees: Analysis by Number of Patents
11.3.6. Patent Benchmarking Analysis
11.3.6.1. Analysis by Patent Characteristics
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
12.4.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images): Analysis by Geography
12.4.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in North America, 2020-2030
12.4.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Europe, 2020-2030
12.4.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Asia-Pacific and RoW, 2020-2030
12.4.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions: Analysis by Economic Strength
12.4.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in High Income Countries, 2020-2030
12.4.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, 2020-2030
12.5. MRI Images
12.5.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images): Analysis by Geography
12.5.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in North America, 2020-2030
12.5.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Europe, 2020-2030
12.5.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Asia-Pacific and RoW, 2020-2030
12.5.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images): Analysis by Economic Strength
12.5.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in High Income Countries, 2020-2030
12.5.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Middle Income Countries, 2020-2030
12.6. CT Images
12.6.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images): Analysis by Geography
12.6.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in North America, 2020-2030
12.6.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Europe, 2020-2030
12.6.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Asia-Pacific and RoW, 2020-2030
12.6.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images): Analysis by Economic Strength
12.6.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in High Income Countries, 2020-2030
12.6.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Middle Income Countries, 2020-2030
12.7. Ultrasound Images
12.7.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images): Analysis by Geography
12.7.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in North America, 2020-2030
12.7.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Europe, 2020-2030
12.7.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Asia-Pacific and RoW, 2020-2030
12.7.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images): Analysis by Economic Strength
12.7.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, 2020-2030
12.7.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, 2020-2030
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
13.3.1 Deep Learning in Medical Image Processing Market for Brain Abnormalities/Neurological Disorders
13.3.2 Deep Learning in Medical Image Processing Market for Cardiac Abnormalities/Cardiovascular Disorders
13.3.3 Deep Learning in Medical Image Processing Market for Breast Cancer
13.3.4 Deep Learning in Medical Image Processing Market for Bone Deformities/Orthopedic Disorders
13.3.5 Deep Learning in Medical Image Processing Market for Lung Infections/Lung Disorders
13.3.6 Deep Learning in Medical Image Processing Market for Other Disorders
13.4 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed
13.4.1 Deep Learning in Medical Image Processing Market for X-Rays
13.4.2 Deep Learning in Medical Image Processing Market for MRI
13.4.3 Deep Learning in Medical Image Processing Market for CT
13.4.3 Deep Learning in Medical Image Processing Market for Ultrasound
13.5 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions
13.5.1 Deep Learning in Medical Image Processing Market in North America
13.5.2 Deep Learning in Medical Image Processing Market in Europe
13.5.3 Deep Learning in Medical Image Processing Market in Asia Pacific/RoW
13.6 Concluding Remarks

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
14.1. Chapter Overview
14.2. Industry Experts
14.2.1. David Reich, President/Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (The Mount Sinai Hospital)
14.2.2. Elad Benjamin, Vice President of Radiology Informatics (Philips) and Jonathan Laserson, Lead AI Strategist (Zebra Medical Vision)
14.2.3. Kevin Lyman, Chief Executive Officer (Enlitic)
14.2.4. Alejandro Jaimes, Chief Scientist and Senior Vice President (Dataminr)
14.2.5. Jeremy Howard, Founder and Researcher (Fast.ai)
14.2.6. Riley Doyle, Serial Entrepreneur and Data Engineer
14.3. University and Hospital Experts
14.3.1. Dr Steven Alberts, Chairman of Medical Oncology (Mayo Clinic)
14.3.2. Neil Lawrence, Professor (University of Cambridge and University of Sheffield) and Senior AI Fellowship (Alan Turing Institute)
14.3.3. Yoshua Bengio, Professor (Université de Montréal) and Scientific Director (IVADO)
14.4. Other Expert Opinions

15. INTERVIEW TRANSCRIPTS
15.1 Chapter Overview
15.2. Advenio Technosys
15.2.1. Company Snapshot
15.2.2. Interview Transcript: Mausumi Acharya (CEO, Advenio Technosys, Q2 2017)
15.3. Arterys
15.3.1. Company Snapshot
15.3.2. Interview Transcript: Carla Leibowitz (Head of Strategy and Marketing, Arterys, Q2 2017)
15.3.3. Interview Transcript: Babak Rasolzadeh (Senior Director of Product, Arterys, Q2 2020)
15.4. Arya.ai
15.4.1. Company Snapshot
15.4.2. Interview Transcript: Deekshith Marla (CTO, Arya.ai) and Sanjay Bhadra (COO, Arya.ai, Q2 2017)
15.5. AlgoSurg
15.5.1. Company Snapshot
15.5.2. Interview Transcript: Dr. Vikas Karade (Founder/CEO, Q2 2020)
15.6. ContextVision
15.6.1. Company Snapshot
15.6.2. Interview Transcript: Walter de Back (Research Scientist, Context Vision, Q2 2020)

16. IMPACT OF COVID-19 OUTBREAK ON DEEP LEARNING MARKET DYNAMICS
16.1. Chapter Overview
16.2. Evaluation of Impact of COVID-19 Pandemic
16.2.1. Current Initiatives and Recuperative Strategies of Key Players
16.2.2. Impact on Opportunity for Deep Learning in Medical Image Processing Market
16.3. Response Strategies: A Perspective
16.3.1. Propositions for Immediate Implementation
16.3.2. Propositions for Short/Long Term Implementation

17. CONCLUSION

18. APPENDIX 1: TABULATED DATA

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  •  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 Téléradiologie
  • 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
  • Linköping 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
  • Rhön-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 Diagnóstica
  • 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
Note: Product cover images may vary from those shown
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