Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market (2nd Edition), 2023-2035: Distribution by Therapeutic Area (Oncological Disorders, Infectious Diseases, Neurological Disorders, Immunological Disorders, Endocrine Disorders, Cardiovascular Disorders, Respiratory Disorders, Eye Disorders, Musculoskeletal Disorders, Inflammatory Disorders and Other Disorders) and Key Geographical Regions (North America, Europe, Asia Pacific and Rest of the World): Industry Trends and Global Forecasts, 2023-2035
The deep learning market is expected to reach USD 34.5 billion in 2023 anticipated to grow at a CAGR of 21.9% during the forecast period 2023-2035.
Owing to the evaluation of computing devices from the mid-twentieth century onwards has transcended their initial purpose of basic calculations, leading to the emergence of artificial intelligence (AI). This field has empowered machines to comprehend data and perform tasks beyond traditional programming. At the core of AI lies machine learning, enabling computers to learn and adapt without explicit programming. Within machine learning, deep learning stands out as a sophisticated subset that employs multi-layered neural networks to interpret vast amounts of unstructured data, yielding valuable insights, particularly in big data analysis.
In the life sciences, especially in domains such as drug discovery and diagnostics, deep learning's application has stemmed from its ability to mimic the human brain. Diagnostics, within the healthcare sector, notably benefit from the capabilities of deep learning. Addressing challenges encountered in drug discovery, like high attrition rates and financial burdens, deep learning has significantly boosted productivity in this field. Recent advancements in deep learning techniques have broadened its applications in medical imaging, molecular profiling, virtual screening, and comprehensive data analysis.
Fueled by ongoing innovation, the deep learning market in healthcare and drug discovery is poised for substantial growth. The profound impact of computational medicine, combined with continuous advancements in deep learning techniques, foreshadows a promising future for this field, indicating significant market expansion in the forecast period.
Report Coverage
Owing to the evaluation of computing devices from the mid-twentieth century onwards has transcended their initial purpose of basic calculations, leading to the emergence of artificial intelligence (AI). This field has empowered machines to comprehend data and perform tasks beyond traditional programming. At the core of AI lies machine learning, enabling computers to learn and adapt without explicit programming. Within machine learning, deep learning stands out as a sophisticated subset that employs multi-layered neural networks to interpret vast amounts of unstructured data, yielding valuable insights, particularly in big data analysis.
In the life sciences, especially in domains such as drug discovery and diagnostics, deep learning's application has stemmed from its ability to mimic the human brain. Diagnostics, within the healthcare sector, notably benefit from the capabilities of deep learning. Addressing challenges encountered in drug discovery, like high attrition rates and financial burdens, deep learning has significantly boosted productivity in this field. Recent advancements in deep learning techniques have broadened its applications in medical imaging, molecular profiling, virtual screening, and comprehensive data analysis.
Fueled by ongoing innovation, the deep learning market in healthcare and drug discovery is poised for substantial growth. The profound impact of computational medicine, combined with continuous advancements in deep learning techniques, foreshadows a promising future for this field, indicating significant market expansion in the forecast period.
Report Coverage
- An executive summary of the key insights captured in our research. It offers a high-level view of the current state of deep learning market and its likely evolution in the mid-to-long term.
- A general overview of big data revolution in the medical industry. It also presents information on artificial intelligence, machine learning, and deep learning algorithms in the healthcare sector. Further, the chapter concludes with a discussion on various applications of deep learning within the healthcare sector.
- Detailed assessment of the overall market landscape of more than 70 companies offering deep learning technologies and services for the purpose of drug discovery, based on several relevant parameters, such as year of establishment, company size, location of headquarters, application area, focus area, therapeutic area, operational model, along with information on the company’s service and product centric models.
- Elaborate profiles of key players developing technologies and offering services related to deep learning, specifically for drug discovery and diagnostics, located across North America, Europe and Asia Pacific (shortlisted based on a proprietary criterion). Each profile includes a brief overview of the company, along with details related to its financial information (wherever available), service portfolio, recent developments and an informed future outlook.
- A qualitative analysis, highlighting the five competitive forces prevalent in this domain, including threats for new entrants, bargaining power of companies using deep learning-based drug discovery and diagnostics, bargaining power of drug developers, threats of substitute technologies and rivalry among existing competitors.
- A detailed analysis of over 420 completed and ongoing clinical trials, based on several relevant parameters, such as trial registration year, trial status, patient enrollment, type of sponsor / collaborator, therapeutic area, trial focus area, study design, and geography. In addition, the chapter highlights the most active industry and non-industry players (in terms of number of clinical trials conducted).
- A detailed analysis of various investments made by players engaged in this domain, during the period 2019-2022, based on several relevant parameters, such as year of funding, amount invested, type of funding (seed financing, venture capital financing, IPOs, secondary offerings, debt financing, grants, and other offerings), focus area, therapeutic area, and geography. In addition, the chapter highlights the most active players (in terms of number of funding instances and amount invested) and key investors (in terms of number of funding instances).
- An analysis of the start-ups / small players (established post 2015, with less than 50 employees) engaged in the deep learning market focused on drug discovery and diagnostics. The chapter includes information on several relevant parameters, such as focus area, therapeutic area, operational model, compatible device, type of offering and start-up health indexing.
- A valuation analysis of companies that are involved in the deep learning-based drug discovery and diagnostics market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
- An insightful market forecast and opportunity analysis, highlighting the future growth potential of the deep learning in drug discovery market till the year 2035. In order to provide details on the future opportunity, our projections have been segmented based on therapeutic area (oncological disorders, infectious diseases, neurological disorders, immunological disorders, endocrine disorders, cardiovascular disorders, respiratory disorders and other disorders) and key geographical regions (North America, Europe, Asia Pacific and Rest of the World). Further, the chapter includes estimates of the likely cost saving potential of deploying deep learning technologies for drug discovery.
- An insightful market forecast and opportunity analysis, highlighting the future growth of the deep learning in diagnostics market till the year 2035. In order to provide details on the future opportunity, our projections have been segmented based on therapeutic area (oncological disorders, cardiovascular disorders, neurological disorders, endocrine disorders, respiratory disorders, ophthalmic disorders, infectious diseases, musculoskeletal disorders, inflammatory disorders and other disorders) and key geographical regions (North America, Europe, Asia Pacific and Rest of the World). Further, the chapter includes estimates of the likely cost saving potential of deploying deep learning technologies for diagnostics.
- The opinions expressed by selected key opinion leaders on the applications and challenges associated with deep learning in the healthcare sector. The chapter provides key takeaways from presentations and videos of these experts, highlighting the future opportunity for these models within the healthcare industry.
- Aegicare
- Aiforia Technologies
- Ardigen
- Berg
- Huawei
- Merative
- Nference
- Nvidia
- Owkin
- Phenomic AI
- Pixel AI
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
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
