Table of Content

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. Forecast Methodology
12.3. Key Assumptions
12.4. Overall Deep Learning in Drug Discovery Market, 2023-2035
12.4.1. Deep Learning in Drug Discovery Market: Analysis by Target Therapeutic Area, 2023-2035
12.4.1.1. Deep Learning in Drug Discovery Market for Oncological Disorders, 2023-2035
12.4.1.2. Deep Learning in Drug Discovery Market for Infectious Diseases, 2023-2035
12.4.1.3. Deep Learning in Drug Discovery Market for Neurological Disorders, 2023-2035
12.4.1.4. Deep Learning in Drug Discovery Market for Immunological Disorders, 2023-2035
12.4.1.5. Deep Learning in Drug Discovery Market for Endocrine Disorders, 2023-2035
12.4.1.6. Deep Learning in Drug Discovery Market for Cardiovascular Disorders, 2023-2035
12.4.1.7. Deep Learning in Drug Discovery Market for Respiratory Disorders, 2023-2035
12.4.1.8. Deep Learning in Drug Discovery Market for Other Disorders, 2023-2035
12.4.2. Deep Learning in Drug Discovery Market: Analysis by Geography, 2023-2035
12.4.2.1. Deep Learning in Drug Discovery Market in North America, 2023-2035
12.4.2.1.1. Deep Learning in Drug Discovery Market in the US, 2023-2035
12.4.2.1.2. Deep Learning in Drug Discovery Market in Canada, 2023-2035
12.4.2.2. Deep Learning in Drug Discovery Market in Europe, 2023-2035
12.4.2.2.1. Deep Learning in Drug Discovery Market in the UK, 2023-2035
12.4.2.2.2. Deep Learning in Drug Discovery Market in France, 2023-2035
12.4.2.2.3. Deep Learning in Drug Discovery Market in Germany, 2023-2035
12.4.2.2.4. Deep Learning in Drug Discovery Market in Spain, 2023-2035
12.4.2.2.5. Deep Learning in Drug Discovery Market in Italy, 2023-2035
12.4.2.2.6. Deep Learning in Drug Discovery Market in Rest of Europe, 2023-2035
12.4.2.3. Deep Learning in Drug Discovery Market in Asia Pacific, 2023-2035
12.4.2.3.1. Deep Learning in Drug Discovery Market in China, 2023-2035
12.4.2.3.2. Deep Learning in Drug Discovery Market in India, 2023-2035
12.4.2.3.3. Deep Learning in Drug Discovery Market in Japan, 2023-2035
12.4.2.3.4. Deep Learning in Drug Discovery Market in Australia, 2023-2035
12.4.2.3.5. Deep Learning in Drug Discovery Market in South Korea, 2023-2035
12.4.2.4. Deep Learning in Drug Discovery Market in Rest of the World, 2023-2035
12.5. Deep Learning in Drug Discovery Market: Cost Saving Potential
12.5.1. Key Assumptions and Methodology
12.5.2. Deep Learning in Drug Discovery Market: Overall Cost Saving Potential, 2023-2035

13. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DIAGNOSTICS
13.1. Chapter Overview
13.2. Forecast Methodology
13.3. Key Assumptions
13.4. Overall Deep Learning in Diagnostics Market, 2023-2035
13.4.1. Deep Learning in Diagnostics Market: Analysis by Target Therapeutic Area, 2023-2035
13.4.1.1. Deep Learning in Diagnostics Market for Oncological Disorders, 2023-2035
13.4.1.2. Deep Learning in Diagnostics Market for Cardiovascular Disorders, 2023-2035
13.4.1.3. Deep Learning in Diagnostics Market for Neurological Disorders, 2023-2035
13.4.1.4. Deep Learning in Diagnostics Market for Endocrine Disorders, 2023-2035
13.4.1.5. Deep Learning in Diagnostics Market for Respiratory Disorders, 2023-2035
13.4.1.6. Deep Learning in Diagnostics Market for Ophthalmic Disorders, 2023-2035
13.4.1.7. Deep Learning in Diagnostics Market for Infectious Diseases, 2023-2035
13.4.1.8. Deep Learning in Diagnostics Market for Musculoskeletal Disorders, 2023-2035
13.4.1.9. Deep Learning in Diagnostics Market for Inflammatory Disorders, 2023-2035
13.4.1.10. Deep Learning in Diagnostics Market for Other Disorders, 2023-2035
13.4.2. Deep Learning in Diagnostics Market: Analysis by Geography, 2023-2035
13.4.2.1. Deep Learning in Diagnostics Market in North America, 2023-2035
13.4.2.2. Deep Learning in Diagnostics Market in Europe, 2023-2035
13.4.2.3. Deep Learning in Diagnostics Market in Asia Pacific, 2023-2035
13.4.2.4. Deep Learning in Diagnostics Market in Rest of the World, 2023-2035
13.5. Deep Learning in Diagnostics Market: Cost Saving Potential
13.5.1. Key Assumptions and Methodology
13.5.2. Deep Learning in Diagnostics Market: Overall Cost Saving Potential, 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 and Emily Salerno, Commercial Strategy and Operations Lead
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, Chief Technical Officer and Sanjay Bhadra, Chief Operational Officer

17. APPENDIX 1: TABULATED DATA

18. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS