Deep Learning Market

Table Of Contents

1. PREFACE
1.1. Introduction
1.2. Key Market Insights
1.3. Scope of the Report
1.4. Research Methodology
1.5. Frequently Asked Questions
1.6. 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

3.3. The Big Data Revolution
3.3.1. Overview of Big Data
3.3.2. Role of Internet of Things (IoT)
3.3.3. Key Application Areas of Big Data
3.3.3.1. Big Data Analytics in Healthcare
3.3.3.2. Machine Learning
3.3.3.3. Deep Learning

3.4. Deep Learning in Healthcare
3.4.1. Personalized Medicine
3.4.2. Lifestyle Management
3.4.3. Drug Discovery
3.4.4. Clinical Trial Management
3.4.5. Diagnostics

3.5. Concluding Remarks

4. MARKET OVERVIEW: DEEP LEARNING IN DRUG DISCOVERY
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery: Overall Market Landscape of Service / Technology Providers
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Application Area
4.2.5. Analysis by Focus Area
4.2.6. Analysis by Therapeutic Area
4.2.7. Analysis by Operational Model
4.2.7.1. Analysis by Service Centric Model
4.2.7.2. Analysis by Product Centric Model

5. MARKET OVERVIEW: DEEP LEARNING IN DIAGNOSTICS
5.1. Chapter Overview
5.2. Deep Learning in Diagnostics: Overall Market Landscape of Service / Technology Providers
5.2.1. Analysis by Year of Establishment
5.2.2. Analysis by Company Size
5.2.3. Analysis by Location of Headquarters
5.2.4. Analysis by Application Area
5.2.5. Analysis by Focus Area
5.2.6. Analysis by Therapeutic Area
5.2.7. Analysis by Type of Offering / Solution
5.2.8. Analysis by Compatible Device

6. COMPANY PROFILES
6.1. Chapter Overview
6.2. Aegicare
6.2.1. Company Overview
6.2.2. Service Portfolio
6.2.3. Recent Developments and Future Outlook

6.3. Aiforia Technologies
6.3.1. Company Overview
6.3.2. Financial Information
6.3.3. Service Portfolio
6.3.4. Recent Developments and Future Outlook

6.4. Ardigen
6.4.1. Company Overview
6.4.2. Financial Information
6.4.3. Service Portfolio
6.4.4. Recent Developments and Future Outlook

6.5. Berg
6.5.1. Company Overview
6.5.2. Service Portfolio
6.5.3. Recent Developments and Future Outlook

6.6. Google
6.6.1. Company Overview
6.6.2. Financial Information
6.6.3. Service Portfolio
6.6.4. Recent Developments and Future Outlook

6.7. Huawei
6.7.1. Company Overview
6.7.2. Financial Information
6.7.3. Service Portfolio
6.7.4. Recent Developments and Future Outlook

6.8. Merative
6.8.1. Company Overview
6.8.2. Service Portfolio
6.8.3. Recent Developments and Future Outlook

6.9. Nference
6.9.1. Company Overview
6.9.2. Service Portfolio
6.9.3. Recent Developments and Future Outlook

6.10. Nvidia
6.10.1. Company Overview
6.10.2. Financial Information
6.10.3. Service Portfolio
6.10.4. Recent Developments and Future Outlook

6.11. Owkin
6.11.1. Company Overview
6.11.2. Service Portfolio
6.11.3. Recent Developments and Future Outlook

6.12. Phenomic AI
6.12.1. Company Overview
6.12.2. Service Portfolio
6.12.3. Recent Developments and Future Outlook

6.13. Pixel AI
6.13.1. Company Overview
6.13.2. Service Portfolio
6.13.3. Recent Developments and Future Outlook

7. PORTER’S FIVE FORCES ANALYSIS
7.1. Chapter Overview
7.2. Methodology and Assumptions
7.3. Key Parameters
7.3.1. Threats of New Entrants
7.3.2. Bargaining Power of Companies Using Deep Learning for Drug Discovery and Diagnostics
7.3.3. Bargaining Power of Drug Developers
7.3.4. Threats of Substitute Technologies
7.3.5. Rivalry Among Existing Competitors

7.4. Concluding Remarks

8. CLINICAL TRIAL ANALYSIS
8.1. Chapter Overview
8.2. Scope and Methodology
8.3 Deep Learning Market: Clinical Trial Analysis
8.3.1. Analysis by Trial Registration Year
8.3.2. Analysis by Trial Status
8.3.3. Analysis by Trial Registration Year and Patient Enrollment
8.3.4. Analysis by Trial Registration Year and Trial Status
8.3.5. Analysis by Type of Sponsor / Collaborator
8.3.6. Analysis by Therapeutic Area
8.3.7. Word Cloud: Trial Focus Area
8.3.8. Analysis by Study Design
8.3.9. Geographical Analysis by Number of Clinical Trials
8.3.10. Geographical Analysis by Trial Registration Year and Patient Population
8.3.11. Leading Organizations: Analysis by Number of Registered Trials

9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. Deep Learning Market: Funding and Investment Analysis
9.3.1. Analysis by Year of Funding
9.3.2. Analysis by Amount Invested
9.3.3. Analysis by Type of Funding
9.3.4. Analysis by Year and Type of Funding
9.3.5. Analysis by Focus Areas
9.3.6. Analysis by Therapeutic Area
9.3.7. Analysis by Geography
9.3.8. Most Active Players: Analysis by Number of Funding Instances
9.3.9. Most Active Players: Analysis by Amount Invested
9.3.10. Most Active Investors: Analysis by Number of Funding Instances

10. START-UP HEALTH INDEXING
10.1. Chapter Overview
10.2. Start-ups Focused on Deep Learning in Drug Discovery
10.2.1. Methodology and Key Parameters
10.2.2. Analysis by Location of Headquarters

10.3. Benchmarking Analysis of Start-ups Focused on Deep Learning in Drug Discovery
10.3.1. Analysis by Focus Area
10.3.2. Analysis by Therapeutic Area
10.3.3. Analysis by Operational Model
10.3.4. Start-up Health Indexing: Roots Analysis Perspective

10.4. Start-ups Focused on Deep Learning in Diagnostics
10.4.1. Methodology and Key Parameters
10.4.2. Analysis by Location of Headquarters

10.5. Benchmarking Analysis of Start-ups Focused on Deep Learning in Diagnostics
10.5.1. Analysis by Focus Area
10.5.2. Analysis by Therapeutic Area
10.5.3. Analysis by Compatible Device
10.5.4. Analysis by Type of Offering
10.5.5. Start-up Health Indexing: Roots Analysis Perspective

11. COMPANY VALUATION ANALYSIS
11.1. Chapter Overview
11.2. Company Valuation Analysis: Key Parameters
11.3. Methodology
11.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

12. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DRUG DISCOVERY
12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Deep Learning in Drug Discovery Market, 2023-2035
12.3.1. Deep Learning in Drug Discovery Market: Analysis by Therapeutic Area, 2023-2035
12.3.1.1. Deep Learning in Drug Discovery Market for Oncological Disorders, 2023-2035
12.3.1.2. Deep Learning in Drug Discovery Market for Infectious Diseases, 2023-2035
12.3.1.3. Deep Learning in Drug Discovery Market for Neurological Disorders, 2023-2035
12.3.1.4. Deep Learning in Drug Discovery Market for Immunological Disorders, 2023-2035
12.3.1.5. Deep Learning in Drug Discovery Market for Endocrine Disorders, 2023-2035
12.3.1.6. Deep Learning in Drug Discovery Market for Cardiovascular Disorders, 2023-2035
12.3.1.7. Deep Learning in Drug Discovery Market for Respiratory Disorders, 2023-2035
12.3.1.8. Deep Learning in Drug Discovery Market for Other Disorders, 2023-2035

12.3.2. Deep Learning in Drug Discovery Market: Analysis by Geography, 2023-2035
12.3.2.1. Deep Learning in Drug Discovery Market in North America, 2023-2035
12.3.2.1.1. Deep Learning in Drug Discovery Market in the US, 2023-2035
12.3.2.1.2. Deep Learning in Drug Discovery Market in Canada, 2023-2035

12.3.2.2. Deep Learning in Drug Discovery Market in Europe, 2023-2035
12.3.2.2.1. Deep Learning in Drug Discovery Market in the UK, 2023-2035
12.3.2.2.2. Deep Learning in Drug Discovery Market in France, 2023-2035
12.3.2.2.3. Deep Learning in Drug Discovery Market in Germany, 2023-2035
12.3.2.2.4. Deep Learning in Drug Discovery Market in Spain, 2023-2035
12.3.2.2.5. Deep Learning in Drug Discovery Market in Italy, 2023-2035
12.3.2.2.6. Deep Learning in Drug Discovery Market in Rest of Europe, 2023-2035

12.3.2.3. Deep Learning in Drug Discovery Market in Asia Pacific, 2023-2035
12.3.2.3.1. Deep Learning in Drug Discovery Market in China, 2023-2035
12.3.2.3.2. Deep Learning in Drug Discovery Market in India, 2023-2035
12.3.2.3.3. Deep Learning in Drug Discovery Market in Japan, 2023-2035
12.3.2.3.4. Deep Learning in Drug Discovery Market in Australia, 2023-2035
12.3.2.3.5. Deep Learning in Drug Discovery Market in South Korea, 2023-2035

12.3.2.4. Deep Learning in Drug Discovery Market in Rest of the World, 2023-2035

12.3.3. Deep Learning in Drug Discovery: Cost Saving Analysis
12.3.3.1. Likely Cost Saving Potential Associated with the Use of Deep Learning in Drug Discovery, 2023-2035

13. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DIAGNOSTICS
13.1. Chapter Overview
13.2. Key Assumptions and Methodology
13.3. Overall Deep Learning in Diagnostics Market, 2023-2035
13.3.1. Deep Learning in Diagnostics Market: Analysis by Therapeutic Area, 2023-2035
13.3.1.1. Deep Learning in Diagnostics Market for Oncological Disorders, 2023-2035
13.3.1.2. Deep Learning in Diagnostics Market for Cardiovascular Disorders, 2023-2035
13.3.1.3. Deep Learning in Diagnostics Market for Neurological Disorders, 2023-2035
13.3.1.4. Deep Learning in Diagnostics Market for Endocrine Disorders, 2023-2035
13.3.1.5. Deep Learning in Diagnostics Market for Respiratory Disorders, 2023-2035
13.3.1.6. Deep Learning in Diagnostics Market for Ophthalmic Disorders, 2023-2035
13.3.1.7. Deep Learning in Diagnostics Market for Infectious Diseases, 2023-2035
13.3.1.8. Deep Learning in Diagnostics Market for Musculoskeletal Disorders, 2023-2035
13.3.1.9. Deep Learning in Diagnostics Market for Inflammatory Disorders, 2023-2035
13.3.1.10. Deep Learning in Diagnostics Market for Other Disorders, 2023-2035

13.3.2. Deep Learning in Diagnostics Market: Analysis by Geography, 2023-2035
13.3.2.1. Deep Learning in Diagnostics Market in North America, 2023-2035
13.3.2.2. Deep Learning in Diagnostics Market in Europe, 2023-2035
13.3.2.3. Deep Learning in Diagnostics Market in Asia Pacific, 2023-2035
13.3.2.4. Deep Learning in Diagnostics Market in Rest of the World, 2023-2035

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
14.1. Chapter Overview
14.2. Sean Lane, Chief Executive Officer (Olive)
14.3. Junaid Kalia, Founder (NeuroCare.AI) and Adeel Memon, Assistant Professor, Neurology Specialist (West Virginia University Hospitals)
14.4. David Reich, President / Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (The Mount Sinai Hospital)
14.5. Elad Benjamin, Vice President, Business Leader Clinical Data Services (Philips) and Jonathan Laserson, Senior Deep Learning Researcher (Apple)
14.6. Kevin Lyman, Founder and Chief Science Officer (Enlitic)

15. CONCLUDING REMARKS

16. INTERVIEW TRANSCRIPTS
16.1. Chapter Overview
16.2. Nucleai
16.2.1. Company Overview
16.2.2. Interview Transcript: Avi Veidman, Chief Executive Officer, Yoav Blum, Director of AI and Ken Bloom, Head of Pathology

16.3. Mediwhale
16.3.1. Company Overview
16.3.2. Interview Transcript: Kevin Choi, Chief Executive Officer

16.4. Arterys
16.4.1. Company Overview
16.4.2. Interview Transcript: Babak Rasolzadeh, Former Vice President of Product and Software Development

16.5. AlgoSurg
16.5.1. Company Overview
16.5.2. Interview Transcript: Vikas Karade, Founder, Chief Executive Officer

16.6. ContextVision
16.6.1. Company Overview
16.6.2. Interview Transcript: Walter de Back, Former Research Scientist

16.7. Advenio Technosys
16.7.1. Company Overview
16.7.2. Interview Transcript: Mausumi Acharya, Chief Executive Officer

16.8. Arterys
16.8.1. Company Overview
16.8.2. Interview Transcript: Carla Leibowitz, Head of Strategy and Marketing

16.9. Arya.ai
16.9.1. Company Overview
16.9.2. Interview Transcript: Deekshith Marla, Founder and Chief Technology Officer and Sanjay Bhadra, Chief Business Officer

17. APPENDIX 1: TABULATED DATA

18. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

Figure 2.1 Executive Summary: Market Overview (Deep Learning in Drug Discovery)
Figure 2.2 Executive Summary: Market Overview (Deep Learning in Diagnostics)
Figure 2.3 Executive Summary: Clinical Trial Analysis
Figure 2.4 Executive Summary: Funding  and Investment Analysis
Figure 2.5 Executive Summary: Start-up Health Indexing
Figure 2.6 Executive Summary: Company Valuation Analysis
Figure 2.7 Executive Summary: Market Sizing and Opportunity Analysis (Deep Learning in Drug Discovery)
Figure 2.8 Executive Summary: Market Sizing and Opportunity Analysis (Deep Learning in Diagnostics)
Figure 3.1 Key Stages of Observational Learning
Figure 3.2 Understanding Neurons and the Human Brain: Key Scientific Contributors
Figure 3.3 Big Data: The Three V’s
Figure 3.4 Internet of Things: Framework
Figure 3.5 Internet of Things: Applications in Healthcare
Figure 3.6 Big Data: Application Areas
Figure 3.7 Big Data: Opportunities in Healthcare
Figure 3.8 Machine Learning Algorithm: Workflow
Figure 3.9 Neural Networks: Architecture
Figure 3.10 Deep Learning: Image Recognition
Figure 3.11 Deep Learning Frameworks: Relative Performance
Figure 3.12 Personalized Medicine: Applications in Healthcare
Figure 4.1 Deep Learning in Drug Discovery: Distribution by Year of Establishment
Figure 4.2 Deep Learning in Drug Discovery: Distribution by Company Size
Figure 4.3 Deep Learning in Drug Discovery: Distribution by Location of Headquarters (Region-wise)
Figure 4.4 Deep Learning in Drug Discovery: Distribution by Location of Headquarters (Country-wise)
Figure 4.5 Deep Learning in Drug Discovery: Distribution by Application Area
Figure 4.6 Deep Learning in Drug Discovery: Distribution by Focus Area
Figure 4.7 Deep Learning in Drug Discovery: Distribution by Therapeutic Area
Figure 4.8 Deep Learning in Drug Discovery: Distribution by Operational Model
Figure 4.9 Deep Learning in Drug Discovery: Distribution by Company Size and Operational Model
Figure 4.10 Deep Learning in Drug Discovery: Distribution by Service Centric Model
Figure 4.11 Deep Learning in Drug Discovery: Distribution by Product Centric Model
Figure 5.1 Deep Learning in Diagnostics: Distribution by Year of Establishment
Figure 5.2 Deep Learning in Diagnostics: Distribution by Company Size
Figure 5.3 Deep Learning in Diagnostics: Distribution by Location of Headquarters (Region-wise)
Figure 5.4 Deep Learning in Diagnostics: Distribution by Location of Headquarters (Country-wise)
Figure 5.5 Deep Learning in Diagnostics: Distribution by Application Area
Figure 5.6 Deep Learning in Diagnostics: Distribution by Focus Area
Figure 5.7 Deep Learning in Diagnostics: Distribution by Therapeutic Area
Figure 5.8 Deep Learning in Diagnostics: Distribution by Type of Offering / Solution
Figure 5.9 Deep Learning in Diagnostics: Distribution by Company Size and Type of Offering / Solution
Figure 5.10 Deep Learning in Diagnostics: Distribution by Compatible Device
Figure 6.1 Aegicare: Deep Learning Derived Service Portfolio
Figure 6.2 Aiforia Technologies: Annual Revenues, 2019-H1 2022 (EUR Thousand)
Figure 6.3 Aiforia Technologies: Deep Learning Derived Service Portfolio
Figure 6.4 Ardigen: Annual Revenues, 2019-9M 2022 (EUR Million)
Figure 6.5 Ardigen: Deep Learning Derived Service Portfolio
Figure 6.6 Berg: Deep Learning Derived Service Portfolio
Figure 6.7 Google: Annual Revenues, 2019-2022 (USD Billion)
Figure 6.8 Google: Deep Learning Derived Service Portfolio
Figure 6.9 Huawei: Annual Revenues, 2019-9M 2022 (CNY Billion)
Figure 6.10 Huawei: Deep Learning Derived Service Portfolio
Figure 6.11 Merative: Deep Learning Derived Service Portfolio
Figure 6.12 Nference: Deep Learning Derived Service Portfolio
Figure 6.13 Nvidia: Annual Revenues, 2019-2022 (USD Billion)
Figure 6.14 Nvidia: Deep Learning Derived Service Portfolio
Figure 6.15 Owkin: Deep Learning Derived Service Portfolio
Figure 6.16 Phenomic AI: Deep Learning Derived Service Portfolio
Figure 6.17 Pixel AI: Deep Learning Derived Service Portfolio
Figure 7.1 Porter’s Five Forces: Key Parameters
Figure 7.2 Threats of New Entrants: Key Factors
Figure 7.3 Bargaining Power of Companies Focused on Deep Learning: Key Factors
Figure 7.4 Bargaining Power of Drug Developers: Key Factors
Figure 7.5 Threats of Substitute Technologies: Key Factors
Figure 7.6 Rivalry Among Existing Competitors: Key Factors
Figure 7.7 Porter’s Five Forces: Concluding Remarks
Figure 8.1 Clinical Trial Analysis: Scope and Methodology
Figure 8.2 Clinical Trial Analysis: Distribution by Trial Registration Year, Pre-2018-2022
Figure 8.3 Clinical Trial Analysis: Distribution by Trial Status
Figure 8.4 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment, 2019-2022
Figure 8.5 Clinical Trial Analysis: Distribution by Trial Registration Year and Trial Status, Pre-2018-2022
Figure 8.6 Clinical Trial Analysis: Distribution by Type of Sponsor / Collaborator
Figure 8.7 Clinical Trial Analysis: Distribution by Therapeutic Area
Figure 8.8 Word Cloud: Trial Focus Area
Figure 8.9 Clinical Trial Analysis: Distribution by Study Design
Figure 8.10 Clinical Trial Analysis: Geographical Distribution of Trials
Figure 8.11 Clinical Trial Analysis: Geographical Distribution by Trial Registration Year and Patient Enrollment
Figure 8.12 Leading Organizations: Distribution by Number of Registered Trials
Figure 9.1 Funding and Investment Analysis: Cumulative Distribution of Number of Instances by Year, 2019-2022
Figure 9.2 Funding and Investment Analysis: Cumulative Distribution of Amount Invested, 2019-2022 (USD Million)
Figure 9.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding
Figure 9.4 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
Figure 9.5 Funding and Investment Analysis: Distribution of Instances by Year and Type of Funding
Figure 9.6 Funding and Investment Analysis: Distribution of Instances by Focus Area
Figure 9.7 Funding and Investment Analysis: Distribution Instances by Therapeutic Area
Figure 9.8 Funding and Investment Analysis: Geographical Distribution of Funding Instances
Figure 9.9 Funding and Investment Analysis: Geographical Distribution by Amount Invested (USD Million)
Figure 9.10 Most Active Players: Distribution by Number of Funding Instances
Figure 9.11 Most Active Players: Distribution by Amount Invested (USD Million)
Figure 9.12 Most Active Investors: Distribution by Number of Funding Instances
Figure 9.13 Summary of Funding and Investments, 2019-2022 (USD Million)
Figure 10.1 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Location of Headquarters
Figure 10.2 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Focus Area
Figure 10.3 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Therapeutic Area
Figure 10.4 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Operational Model
Figure 10.5 Start-ups Focused on Deep Learning in Drug Discovery: Roots Analysis Perspective
Figure 10.6 Start-ups Focused on Deep Learning in Drug Discovery: Wind Rose Analysis
Figure 10.7 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Location of Headquarters
Figure 10.8 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Focus Area
Figure 10.9 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Therapeutic Area
Figure 10.10 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Compatible Device
Figure 10.11 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Type of Offering
Figure 10.12 Start-ups Focused on Deep Learning in Diagnostics: Roots Analysis Perspective
Figure 10.13 Start-ups Focused on Deep Learning in Diagnostics: Wind Rose Analysis
Figure 12.1 Overall Deep Learning in Drug Discovery Market, 2023-2035 (USD Billion)
Figure 12.2 Deep Learning in Drug Discovery Market: Distribution by Target Therapeutic Area, 2023-2035 (USD Million)
Figure 12.3 Deep Learning in Drug Discovery Market for Oncological Disorders, 2023-2035 (USD Million)
Figure 12.4 Deep Learning in Drug Discovery Market for Infectious Diseases, 2023-2035 (USD Million)
Figure 12.5 Deep Learning in Drug Discovery Market for Neurological Disorders, 2023-2035 (USD Million)
Figure 12.6 Deep Learning in Drug Discovery Market for Immunological Disorders, 2023-2035 (USD Million)
Figure 12.7 Deep Learning in Drug Discovery Market for Endocrine Disorders, 2023-2035 (USD Million)
Figure 12.8 Deep Learning in Drug Discovery Market for Cardiovascular Disorders, 2023-2035 (USD Million)
Figure 12.9 Deep Learning in Drug Discovery Market for Respiratory Disorders, 2023-2035 (USD Million)
Figure 12.10 Deep Learning in Drug Discovery Market for Other Disorders, 2023-2035 (USD Million)
Figure 12.11 Deep Learning in Drug Discovery Market: Distribution by Key Geographical Regions, 2023-2035 (USD Million)
Figure 12.12 Deep Learning in Drug Discovery Market in North America, 2023-2035 (USD Million)
Figure 12.13 Deep Learning in Drug Discovery Market in the US, 2023-2035 (USD Million)
Figure 12.14 Deep Learning in Drug Discovery Market in Canada, 2023-2035 (USD Million)
Figure 12.15 Deep Learning in Drug Discovery Market in Europe, 2023-2035 (USD Million)
Figure 12.16 Deep Learning in Drug Discovery Market in the UK, 2023-2035 (USD Million)
Figure 12.17 Deep Learning in Drug Discovery Market in France, 2023-2035 (USD Million)
Figure 12.18 Deep Learning in Drug Discovery Market in Germany, 2023-2035 (USD Million)
Figure 12.19 Deep Learning in Drug Discovery Market in Spain, 2023-2035 (USD Million)
Figure 12.20 Deep Learning in Drug Discovery Market in Italy, 2023-2035 (USD Million)
Figure 12.21 Deep Learning in Drug Discovery Market in Rest of Europe, 2023-2035 (USD Million)
Figure 12.22 Deep Learning in Drug Discovery Market in Asia Pacific, 2023-2035 (USD Million)
Figure 12.23 Deep Learning in Drug Discovery Market in China, 2023-2035 (USD Million)
Figure 12.24 Deep Learning in Drug Discovery Market in India, 2023-2035 (USD Million)
Figure 12.25 Deep Learning in Drug Discovery Market in Japan, 2023-2035 (USD Million)
Figure 12.26 Deep Learning in Drug Discovery Market in Australia, 2023-2035 (USD Million)
Figure 12.27 Deep Learning in Drug Discovery Market in South Korea, 2023-2035 (USD Million)
Figure 12.28 Deep Learning in Drug Discovery Market in Rest of the World, 2023-2035 (USD Million)
Figure 12.29 Likely Cost Saving Potential Associated with the Use of Deep Learning in Drug Discovery, 2023-2035 (USD Billion)
Figure 13.1 Overall Deep Learning in Diagnostics Market, 2023-2035 (USD Billion)
Figure 13.2 Deep Learning in Diagnostics Market: Distribution by Target Therapeutic Area, 2023-2035 (USD Million)
Figure 13.3 Deep Learning in Diagnostics Market for Oncological Disorders, 2023-2035 (USD Million)
Figure 13.4 Deep Learning in Diagnostics Market for Cardiovascular Disorders, 2023-2035 (USD Million)
Figure 13.5 Deep Learning in Diagnostics Market for Neurological Disorders, 2023-2035 (USD Million)
Figure 13.6 Deep Learning in Diagnostics Market for Endocrine Disorders, 2023-2035 (USD Million)
Figure 13.7 Deep Learning in Diagnostics Market for Respiratory Disorders, 2023-2035 (USD Million)
Figure 13.8 Deep Learning in Diagnostics Market for Ophthalmic Disorders, 2023-2035 (USD Million)
Figure 13.9 Deep Learning in Diagnostics Market for Infectious Diseases, 2023-2035 (USD Million)
Figure 13.10 Deep Learning in Diagnostics Market for Musculoskeletal Disorders, 2023-2035 (USD Million)
Figure 13.11 Deep Learning in Diagnostics Market for Inflammatory Disorders, 2023-2035 (USD Million)
Figure 13.12 Deep Learning in Diagnostics Market for Other Disorders, 2023-2035 (USD Million)
Figure 13.13 Deep Learning in Diagnostics Market: Distribution by Key Geographical Regions, 2023-2035 (USD Million)
Figure 13.14 Deep Learning in Diagnostics Market in North America, 2023-2035 (USD Million)
Figure 13.15 Deep Learning in Diagnostics Market in Europe, 2023-2035 (USD Million)
Figure 13.16 Deep Learning in Diagnostics Market in Asia Pacific, 2023-2035 (USD Million)
Figure 13.17 Deep Learning in Diagnostics Market in Rest of the World, 2023-2035 (USD Million)
Figure 15.1 Concluding Remarks: Market Overview (Deep Learning in Drug Discovery)
Figure 15.2 Concluding Remarks: Market Overview (Deep Learning in Diagnostics)
Figure 15.3 Concluding Remarks: Clinical Trial Analysis
Figure 15.4 Concluding Remarks: Funding and Investment Analysis
Figure 15.5 Concluding Remarks: Start-up Health Indexing
Figure 15.6 Concluding Remarks: Company Valuation Analysis
Figure 15.7 Concluding Remarks: Market Sizing and Opportunity Analysis (Deep Learning in Drug Discovery)
Figure 15.8 Concluding Remarks: Market Sizing and Opportunity Analysis (Deep Learning in Diagnostics)

List Of Tables

Table 3.1 Machine Learning: A Brief History
Table 4.1 Deep Learning in Drug Discovery: List of Service / Technology Providers
Table 4.2 Deep Learning in Drug Discovery Services / Technology Providers: Information on Application Area, Focus Area, Therapeutic Area and Operational Model
Table 4.3 Deep Learning in Drug Discovery Services / Technology Providers: Information on Operational Model
Table 4.4 Deep Learning in Drug Discovery Services / Technology Providers: Information on Service Centric Model
Table 4.5 Deep Learning in Drug Discovery Services / Technology Providers: Information on Product Centric Model
Table 5.1 Deep Learning in Diagnostics: List of Service / Technology Providers
Table 5.2 Deep Learning in Diagnostics Services / Technology Providers: Information on Application Area, Focus Area and Therapeutic Area
Table 5.3 Deep Learning in Diagnostics Services / Technology Providers: Information on Type of Offering / Solution and Compatible Device
Table 6.1 List of Companies Profiled
Table 6.2 Aegicare: Company Overview
Table 6.3 Aiforia Technologies: Company Overview
Table 6.4 Aiforia Technologies: Recent Developments and Future Outlook
Table 6.5 Ardigen: Company Overview
Table 6.6 Ardigen: Recent Developments and Future Outlook
Table 6.7 Berg: Company Overview
Table 6.8 Berg: Recent Developments and Future Outlook
Table 6.9 Google: Company Overview
Table 6.10 Google: Recent Developments and Future Outlook
Table 6.11 Huawei: Company Overview
Table 6.12 Huawei: Recent Developments and Future Outlook
Table 6.13 Merative: Company Overview
Table 6.14 Nference: Company Overview
Table 6.15 Nference: Recent Developments and Future Outlook
Table 6.16 Nvidia: Company Overview
Table 6.17 Nvidia: Recent Developments and Future Outlook
Table 6.18 Owkin: Company Overview
Table 6.19 Owkin: Recent Developments and Future Outlook
Table 6.20 Phenomic AI: Company Overview
Table 6.21 Pixel AI: Company Overview
Table 9.1 Deep Learning Market: List of Funding and Investments, 2019-2022
Table 9.2 Funding and Investment Analysis: Summary of Investments
Table 9.3 Funding and Investment Analysis: Summary of Venture Capital Funding
Table 10.1 List of Start-ups Focused on Deep Learning in Drug Discovery
Table 10.2 List of Start-ups Focused on Deep Learning in Diagnostics
Table 11.1 Company Valuation Analysis: Scoring Sheet
Table 11.2 Company Valuation Analysis: Estimated Valuation by Years of Experience
Table 11.3 Company Valuation Analysis: Estimated Valuation by Employee Strength
Table 16.1 Mediwhale: Key Highlights
Table 16.2 Advenio Technosys: Key Highlights
Table 16.3 Arterys: Key Highlights
Table 16.4 Arya.ai: Key Highlights
Table 17.1 Deep Learning in Drug Discovery: Distribution by Year of Establishment
Table 17.2 Deep Learning in Drug Discovery: Distribution by Company Size
Table 17.3 Deep Learning in Drug Discovery: Distribution by Location of Headquarters (Region-wise)
Table 17.4 Deep Learning in Drug Discovery: Distribution by Location of Headquarters (Country-wise)
Table 17.5 Deep Learning in Drug Discovery: Distribution by Application Area
Table 17.6 Deep Learning in Drug Discovery: Distribution by Focus Area
Table 17.7 Deep Learning in Drug Discovery: Distribution by Therapeutic Area
Table 17.8 Deep Learning in Drug Discovery: Distribution by Operational Model
Table 17.9 Deep Learning in Drug Discovery: Distribution by Company Size and Operational Model
Table 17.10 Deep Learning in Drug Discovery: Distribution by Service Centric Model
Table 17.11 Deep Learning in Drug Discovery: Distribution by Product Centric Model
Table 17.12 Deep Learning in Diagnostics: Distribution by Year of Establishment
Table 17.13 Deep Learning in Diagnostics: Distribution by Company Size
Table 17.14 Deep Learning in Diagnostics: Distribution by Location of Headquarters (Region-wise)
Table 17.15 Deep Learning in Diagnostics: Distribution by Location of Headquarters (Country-wise)
Table 17.16 Deep Learning in Diagnostics: Distribution by Application Area
Table 17.17 Deep Learning in Diagnostics: Distribution by Focus Area
Table 17.18 Deep Learning in Diagnostics: Distribution by Therapeutic Area
Table 17.19 Deep Learning in Diagnostics: Distribution by Type of Offering / Solution
Table 17.20 Deep Learning in Diagnostics: Distribution by Company Size and Type of Offering / Solution
Table 17.21 Deep Learning in Diagnostics: Distribution by Compatible Device
Table 17.22 Aiforia Technologies: Annual Revenues, 2019 - H1 2022 (EUR Thousand)
Table 17.23 Ardigen: Annual Revenues, 2019 - 9M 2022 (EUR Million)
Table 17.24 Google: Annual Revenues, 2019-2022 (USD Billion)
Table 17.25 Huawei: Annual Revenues, 2019 - 9M 2022 (CNY Billion)
Table 17.26 Nvidia: Annual Revenues, 2019-2022 (USD Billion)
Table 17.27 Clinical Trial Analysis: Distribution by Trial Registration Year, Pre-2018 - 2022
Table 17.28 Clinical Trial Analysis: Distribution by Trial Status
Table 17.29 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment, 2019-2022
Table 17.30 Clinical Trial Analysis: Distribution by Trial Registration Year and Trial Status, Pre-2018 - 2022
Table 17.31 Clinical Trial Analysis: Distribution by Type of Sponsor / Collaborator
Table 17.32 Clinical Trial Analysis: Distribution by Therapeutic Area
Table 17.33 Clinical Trial Analysis: Distribution by Study Design
Table 17.34 Clinical Trial Analysis: Geographical Distribution of Trials
Table 17.35 Clinical Trial Analysis: Geographical Distribution by Trial Registration Year and Enrolled Patient Population
Table 17.36 Leading Organizations: Distribution by Number of Registered Trials
Table 17.37 Funding and Investment Analysis: Cumulative Distribution of Number of Instances by Year, 2019-2022
Table 17.38 Funding and Investment Analysis: Cumulative Distribution of Amount Invested, 2019-2022 (USD Million)
Table 17.39 Funding and Investment Analysis: Distribution of Instances by Type of Funding
Table 17.40 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
Table 17.41 Funding and Investment Analysis: Distribution of Instances by Year and Type of Funding
Table 17.42 Funding and Investments: Distribution of Instances by Focus Area
Table 17.43 Funding and Investment Analysis: Distribution of Instances by Therapeutic Area
Table 17.44 Funding and Investment Analysis: Geographical Distribution of Funding Instances
Table 17.45 Funding and Investment Analysis: Geographical Distribution by Amount Invested (USD Million)
Table 17.46 Most Active Players: Distribution by Number of Funding Instances
Table 17.47 Most Active Players: Distribution by Amount Invested (USD Million)
Table 17.48 Most Active Investors: Distribution by Number of Funding Instances
Table 17.49 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Location of Headquarters
Table 17.50 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Focus Area
Table 17.51 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Therapeutic Area
Table 17.52 Start-ups Focused on Deep Learning in Drug Discovery: Distribution by Operational Model
Table 17.53 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Location of Headquarters
Table 17.54 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Focus Area
Table 17.55 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Therapeutic Area
Table 17.56 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Compatible Device
Table 17.57 Start-ups Focused on Deep Learning in Diagnostics: Distribution by Type of Offering
Table 17.58 Overall Deep Learning in Drug Discovery Market: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Billion)
Table 17.59 Deep Learning in Drug Discovery Market: Distribution by Therapeutic Area, 2023-2035 (USD Million)
Table 17.60 Deep Learning in Drug Discovery Market for Oncological Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.61 Deep Learning in Drug Discovery Market for Infectious Diseases: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.62 Deep Learning in Drug Discovery Market for Neurological Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.63 Deep Learning in Drug Discovery Market for Immunological Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.64 Deep Learning in Drug Discovery Market for Endocrine Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.65 Deep Learning in Drug Discovery Market for Cardiovascular Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.66 Deep Learning in Drug Discovery Market for Respiratory Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.67 Deep Learning in Drug Discovery Market for Other Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.68 Deep Learning in Drug Discovery Market: Distribution by Geography, 2023-2035 (USD Billion)
Table 17.69 Deep Learning in Drug Discovery Market in North America: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.70 Deep Learning in Drug Discovery Market in the US: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.71 Deep Learning in Drug Discovery Market in Canada: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.72 Deep Learning in Drug Discovery Market in Europe: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.73 Deep Learning in Drug Discovery Market in the UK: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.74 Deep Learning in Drug Discovery Market in France: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.75 Deep Learning in Drug Discovery Market in Germany: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.76 Deep Learning in Drug Discovery Market in Spain: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.77 Deep Learning in Drug Discovery Market in Italy: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.78 Deep Learning in Drug Discovery Market in Rest of Europe: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.79 Deep Learning in Drug Discovery Market in Asia Pacific: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.80 Deep Learning in Drug Discovery Market in China: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.81 Deep Learning in Drug Discovery Market in India: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.82 Deep Learning in Drug Discovery Market in Japan: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.83 Deep Learning in Drug Discovery Market in Australia: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.84 Deep Learning in Drug Discovery Market in South Korea: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.85 Deep Learning in Drug Discovery Market in Rest of the World: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.86 Likely Cost Saving Potential Associated with the Use of Deep Learning in Drug Discovery, 2023-2035 (USD Billion)
Table 17.87 Overall Deep Learning in Diagnostics Market: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Billion)
Table 17.88 Deep Learning in Diagnostics Market: Distribution by Therapeutic Area, 2023-2035 (USD Billion)
Table 17.89 Deep Learning in Diagnostics Market for Oncological Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.90 Deep Learning in Diagnostics Market for Cardiovascular Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.91 Deep Learning in Diagnostics Market for Neurological Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.92 Deep Learning in Diagnostics Market for Endocrine Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.93 Deep Learning in Diagnostics Market for Respiratory Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.94 Deep Learning in Diagnostics Market for Ophthalmic Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.95 Deep Learning in Diagnostics Market for Infectious Diseases: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.96 Deep Learning in Diagnostics Market for Musculoskeletal Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.97 Deep Learning in Diagnostics Market for Inflammatory Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.98 Deep Learning in Diagnostics Market for Other Disorders: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.99 Deep Learning in Diagnostics Market: Distribution by Key Geographical Regions, 2023-2035 (USD Billion)
Table 17.100 Deep Learning in Diagnostics Market in North America: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.101 Deep Learning in Diagnostics Market in Europe: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.102 Deep Learning in Diagnostics Market in Asia Pacific: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)
Table 17.103 Deep Learning in Diagnostics Market in Rest of the World: Conservative, Base and Optimistic Scenarios, 2023-2035 (USD Million)

List Of Companies

The following companies and organizations have been mentioned in the report:

1. 3E Bioventures Capital
2. 3W Healthcare Fund
3. 83North
4. A.I. VALI
5. A2A Pharmaceuticals
6. Absci
7. Accutar Biotech
8. Acellera
9. Ackyee
10. Action Potential Venture Capital
11. Adara Ventures
12. Advenio TecnoSys
13. Aegicare
14. Aganitha Cognitive Solutions
15. AI Therapeutics
16. Aidence
17. Aidoc
18. Aiforia Technologies
19. Air Street Capital
20. AIRA MATRIX
21. Alchimia Investments
22. AlgoSurg
23. Alibaba Cloud
24. Alpha Intelligence Capital
25. Alwin Capital
26. Amadeus Capital Partners
27. Amazon Web Services
28. AmCad BioMed
29. BrightEdge
30. aMoon
31. Amyloid Solution
32. Andreessen Horowitz
33. Anumana
34. Aomics
35. Apposite Capital
36. Ardigen
37. ARK Investment Management
38. Artelus
39. Arterys
40. Arya.ai
41. Atomico
42. Atomwise
43. Avalon AI
44. Avicenna.AI
45. Avidity Partners
46. AZmed
47. Bain Capital Life Sciences
48. BankInvest
49. Behold.ai
50. BenevolentAI
51. Benslie Investment Group
52. BERG
53. Bessemer Venture Partners
54. BHR Partners
55. Bill & Melinda Gates Foundation
56. BioNTech
57. Biotechnology Industry Research Assistance Council
58. BlackRock
59. Blueberry Diagnostics
60. Boehringer Ingelheim
61. Borski Fund
62. Bpifrance
63. Brainomix
64. Bristol Myers Squibb
65. Butterfly Network
66. California Institute of Regenerative Medicine
67. CancerCenter.ai
68. Canon Medical Systems
69. Capital Cell
70. Capital Group
71. CapitalG
72. Capricorn Venture Partners
73. Caption Health
74. Casdin Capital
75. Catalace
76. Cathay Innovation
77. Celgene Corporation
78. Cemag Invest
79. ChemPass
80. China International Capital Corporation (CICC)
81. Cigna Ventures
82. Cisco
83. Cleveland Avenue
84. Cloud Pharmaceuticals
85. Coatue
86. Collaborations Pharmaceuticals
87. ContextVision
88. Cortechs.ai
89. Cortex Discovery
90. Cosmo Pharmaceuticals
91. CTI Life Sciences Fund
92. CureMetrix
93. Commonwealth Bank of Australia (CBA)
94. Cyclica
95. D1 Capital Partners
96. Daishin Private Equity
97. Databricks
98. DCVC
99. Deargen
100. Debiopharm
101. Deep AI Lab
102. Deep Bio
103. Deep Genomics
104. Deep Longevity
105. DeepCure
106. deepPath
107. Deepscopy
108. DeepTek
109. DeepWise
110. Densitas
111. DiA Imaging Analysis
112. Diagnoly
113. Downing Ventures
114. Drive Capital
115. Eastern Bell Capital
116. EchoNous
117. EcoR1 Capital
118. Eight Roads Ventures
119. Eldridge
120. Enlitic
121. ENSEM Therapeutics
122. Envision Healthcare
123. Epredia
124. Esaote
125. ETP Ventures
126. European Innovation Council
127. European Investment Bank
128. European Regional Development Fund (ERDF)
129. Eurostars
130. Exo
131. Exscientia
132. Exxora
133. FedDev Ontario
134. Fennaio
135. Fidelity Management & Research Company
136. Fifty Years
137. FinLab
138. FinLab EOS VC Fund
139. Fiscus Ventures
140. Flatiron Health
141. Forestay Capital
142. Foxconn
143. F-Prime
144. Franklin Templeton
145. Frazier Life Sciences
146. Freenome
147. FUJIFILM Sonosite
148. Future Ventures
149. G3 Therapeutics
150. GE Healthcare
151. Genedata
152. General Atlantic
153. General Catalyst
154. Genesis Therapeutics
155. Genuity Science
156. GGV Capital
157. Glenview Capital Management
158. GNS Healthcare
159. Goldman Sachs
160. Goldman Sachs Asset Management
161. Google
162. GHV Accelerator
163. Greenoaks Capital Partners
164. Gritstone bio
165. Growth FOF
166. GT Healthcare Capital Partners
167. Guardant Health
168. Guofang Investment
169. H2O.ai
170. HALO Diagnostics
171. Hana Ventures
172. HealthQuad
173. HealthQuest Capital
174. Healx
175. HeartFlow
176. HERAN Partners
177. Hercules Capital
178. Hewlett Packard Enterprise
179. Hologic
180. HOPU
181. Horizons Ventures
182. Hoxton Ventures
183. Huawei
184. HY Medical
185. Human Longevity and Performance Impact Venture Fund
186. Huons
187. Ibex Medical Analytics
188. iCAD
189. iCarbonX
190. icometrix
191. ICON Fund
192. IDG Capital
193. Iktos
194. Imaging Biometrics
195. Imbio
196. ImFusion
197. Immunocure Discovery Solutions
198. IN CAPITAL
199. India Edison Accelerator (a subsidiary of GE Healthcare)
200. iA Financial Group
201. Infervision
202. INKEF Capital
203. InMed Prognostics
204. Innophore
205. Innoplexus
206. Innovacom
207. Innovate UK
208. Insight Partners
209. inSili.com
210. Insilico Medicine
211. Intel Capital
212. Intellegens
213. Intelligent Ultrasound
214. Interprotein
215. Intoolab
216. Intrasense
217. InveniAI
218. IQVIA
219. Isomorphic Labs
220. iSono Health
221. Israel Innovation Authority
222. JLK Inspection
223. JSR Life Sciences
224. Juvena Therapeutics
225. Kaiser Permanente
226. Kansen voor West
227. KBI Biopharma
228. Keen Eye
229. Kennedy Lewis Investment Management
230. Kester Capital
231. Keya Medical
232. Kheiron Medical Technologies
233. Kinnevik
234. Koinvesticinis Fondas
235. Koios Medical
236. Korea Development Bank
237. Kreos Capital
238. ksilink
239. Kunlun Internet Smart Fund
240. Kunlun Worldwide
241. LabCorp
242. Leaps by Bayer
243. Lennertz & Co.
244. Lightbeam Health Solutions
245. Luminous Ventures
246. Lunit
247. MAbSilico
248. MACSF
249. Marubeni
250. MassChallenge Switzerland
251. Matrix Capital Management
252. Mayo Clinic
253. Mechanomind
254. Median Technologies
255. Mediwhale
256. MedTeq
257. Merative
258. Mercia
259. Merck Global Health Innovation Fund
260. Meridian Street Capital
261. Methinks
262. Microsoft
263. Mirada Medical
264. Mirae Asset Capital
265. Mirae Asset Venture Investment
266. moretho
267. Morningside
268. Morningside Ventures
269. Mubadala Capital
270. Mvision AI
271. Naiad Lab
272. Nanox
273. National Center for Research and Development
274. National Institutes of Health (NIH)
275. National Science Foundation
276. Nemedis
277. NeuralSeg
278. nference
279. Novo Holdings
280. NSG Ventures
281. Nucleai
282. Numedii
283. Nuritas
284. NVESTOR
285. NVIDIA
286. OccamzRazor
287. Octopus Ventures
288. OMERS Ventures
289. OneThree Biotech
290. Ontario Together Fund
291. Optellum
292. Optibrium
293. Optum Ventures
294. Oracle
295. OrbiMed
296. OrganAI
297. Owkin
298. Oxipit
299. ParityBit
300. Parkwalk Advisors
301. PathAI
302. Pavilion Capital
303. PEACCEL
304. Peptone
305. Peptris Technologies
306. PercayAI
307. Perceptive Advisors
308. PharmCADD
309. Pharos iBio
310. PHC Holdings
311. Phenomic AI
312. Philips
313. PICC Capital
314. Ping An Global Voyager Fund
315. Pixel AI
316. Polaris Partners
317. Practica Capital
318. Primary Venture Partners
319. Primavera Venture Partners
320. ProdIntelligence
321. Prognica Labs
322. Prosperity7 Ventures
323. Qatar Investment Authority
324. Qianhai Gaozu Asset Management Fund
325. Qiming Venture Partners
326. QMENTA
327. Quantib
328. Quibim
329. Qure.ai
330. QView Medical
331. RA Capital Management
332. Rabo Frontier Ventures
333. RADLogics
334. RadNet
335. RapidAI
336. Recursion Pharmaceuticals
337. Redmile Group
338. Reimagined Ventures
339. Research and Innovation Circle of Hyderabad
340. RevealDx
341. Reverie Labs
342. Ridgeback Capital
343. rises.io
344. Riverain Technologies
345. Roche Diagnostics
346. Rock Springs Capital
347. RSIP Vision
348. Samsung
349. Samyang Holdings
350. Sanabil Investments
351. Sanofi
352. Sanofi Ventures
353. Saverna Therapeutics
354. Scale Venture Partners
355. Scottish Mortgage Investment Trust
356. ScreenPoint Medical
357. SD Biosensor
358. Section 32
359. Seeai
360. Sequoia India
361. Shanghai Artificial Intelligence Investment Fund
362. Shinhan Investment
363. Siemens Healthineers
364. SigTuple
365. SIHA AI
366. Silicon Valley Bank
367. SK Holdings
368. SK Telecom
369. SKS Private Equity
370. SoftBank Vision Fund
371. Square Peg Capital
372. Sravathi AI
373. Standigm
374. Subtle Medical
375. Sunshine Insurance Group
376. Synsight
377. Tata Capital Healthcare Fund
378. TCV
379. Techcyte
380. Temasek
381. Temasek Holdings
382. Tenet Healthcare 
383. TeselaGen
384. Alliance of Families Fighting Pancreatic Cancer
385. The Wells Investment
386. Thirona
387. Thorney Investment Group
388. Three Springs Technology
389. Tiger Global Management
390. Tomocube
391. Trusted Insight
392. TS Investment
393. Tybourne Capital Management
394. UPMC Enterprises
395. UtopiaCompression
396. UVJ Technologies
397. Valence Discovery
398. Vara
399. Verge Genomics
400. VI Partners
401. Vingyani
402. Visiopharm
403. Viz.ai
404. Voima Ventures
405. VoxelCloud
406. vRad
407. VUNO
408. Warburg Pincus
409. Wellington Management
410. Whiterabbit.ai
411. Wingspan
412. Wisecube
413. XtalPi
414. XTX Ventures
415. Yahui Precise Medical Care Fund
416. Yozma Group Korea
417. Yunfeng Capital
418. Zaya AI
419. Zoetis
420. Zola Global Investors