Deep Learning Market

Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market: Distribution by Therapeutic Areas and Key Geographical Regions: Industry Trends and Global Forecasts (2nd Edition), 2023-2035

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    March 2023

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Report Description

Since the mid-twentieth century, computing devices have continually been explored for applications beyond mere calculations, to emerge as machines that possess intelligence. These targeted efforts have contributed to the introduction of artificial intelligence, the next-generation simulator that employs programmed machines possessing the ability to comprehend data and execute the instructed tasks. The progress of artificial intelligence can be attributed to machine learning, a field of study imparting computers with the ability to think without being explicitly programmed. Deep learning is a complex machine learning algorithm that uses a neural network of interconnected nodes / neurons in a multi-layered structure, thereby enabling the interpretation of large volumes of unstructured data to generate valuable insights. The mechanism of deep learning technique resembles the interpretation ability of human beings, making it a promising approach for big data analysis. Owing to the distinct characteristic of deep learning algorithm to imitate human brain, it is currently being deployed in the life sciences domain, primarily for the purpose of drug discovery and diagnostics. Considering the challenges associated with drug discovery and development, such as the high attrition rate and increased financial burden, deep learning has been found to improve the overall R&D productivity and enhance diagnosis / prediction accuracy. Recent advancements in the deep learning domain have demonstrated its potential in other healthcare-associated segments, such as medical image analysis, molecular profiling, virtual screening and sequencing data analysis. Driven by the ongoing pace of innovation and the profound impact of implementation of such solutions, deep learning is anticipated to witness substantial growth in the foreseen future.

Key Market Insights

The 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, Ophthalmic 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 report features an extensive study of the current market landscape and the likely future potential of the deep learning solutions market within the healthcare domain. The report highlights the efforts of several stakeholders engaged in this rapidly emerging segment of the pharmaceutical industry. The report answers many key questions related to this domain.

What is the Current Market Landscape of the Deep Learning Market Focused on Drug Discovery and Diagnostics?

Currently, more than 200 industry players are focused on providing deep learning-based services / technologies for drug discovery and diagnostic purposes. The primary focus areas of these companies include big data analysis, medical imaging, medical diagnosis and genetic / molecular data analysis. Further, these players are engaged in offering services across a wide range of therapeutic areas. It is worth highlighting that deep learning-powered diagnostic service providers offer various diagnostic solutions, such as structured analysis reports, image interpretation and biomarker identification solutions, with input data from several compatible devices. 

What is the Market Size of Deep Learning in Drug Discovery?

Lately, the industry has witnessed the development of advanced deep learning technologies / software. These technologies possess the ability to obviate the concerns associated with the conventional drug discovery process. Eventually, such technologies will aid in the reduction of financial burden associated with drug discovery. The global deep learning market focusing on  drug discovery is anticipated to grow at a CAGR of over 22.7% between 2023 and 2035. By 2035, the deep learning in drug discovery market for oncological disorders is expected to capture the majority share. In terms of geography, the market in North America and Europe is anticipated to grow at a relatively faster pace by 2035.

What is the Market Size of Deep Learning in Diagnostics Market?

The adoption of deep learning-powered technologies to assist medical diagnosis, as well as prevention of diseases, has increased in the recent past. The global deep learning market focusing on diagnostics is anticipated to grow at a CAGR of over 15% between 2023 and 2035. By 2035, the deep learning in diagnostics market in North America is expected to capture the majority share. In terms of therapeutic areas, the deep learning in diagnostics market for endocrine and respiratory disorders is anticipated to grow at a relatively faster pace by 2035.

Which Segment held the Largest Share in Deep Learning Market?

The study covers the revenues from deep learning technology for their potential applications in the drug discovery and diagnostics domain. As of 2022, deep learning-based diagnostics held the largest share of the market, owing to the efficiency and precision of applying deep learning-powered diagnostic solutions. Further, the deep learning in drug discovery market is anticipated to grow at a relatively higher growth rate during the given time period with several pharmaceutical companies actively collaborating with solution providers for drug design and development.

What are the Key Advantages offered by Deep Learning in Drug Discovery and Diagnostics?

The use of deep learning in drug discovery has the potential to reduce capital requirements and the failure-to-success ratio, as algorithms are better equipped to analyze large datasets. Similarly, in diagnostics domain, deep learning technology can be used to assist medical professionals in medical imaging and interpretation. This enables quick and efficient diagnosis of disease indications at an early stage.

What are the Key Drivers of Deep Learning in Drug Discovery and Diagnostics Market?

In the last decade, the healthcare industry has witnessed an inclination towards the adoption of information services and digital analytical solutions. This can be attributed to the fact that companies have recently shifted towards high-resolution medical images and electronic health and medical records, generating large and complex data, referred to as big data. In order to analyze such large, structured and unstructured datasets, efficient tools and technology, such as deep learning, are required. Thus, these massive datasets are anticipated to be a primary driver of technological advancements in the deep learning and artificial intelligence domain.

What are the Key Trends in the Deep Learning in Drug Discovery and Diagnostics Market?

Many stakeholders have been making consolidated efforts to forge alliances with other industry / non-industry players for research, software licensing and collaborative drug / solution development purposes. It is worth highlighting that over 240 clinical studies are being conducted to evaluate the potential of deep learning in diagnostics, highlighting the continuous pace of innovation in this field. Moreover, the field is evolving continuously, as a number of start-ups have emerged with the aim of developing deep learning technologies / software. In this context, in the past seven years, over 60 companies providing deep learning-based solutions have been established. Given the inclination towards advanced deep learning technologies and their vast applications in the healthcare segment, we believe that the deep learning market is likely to evolve at a rapid pace over the coming years.

Who are the Key Players in the Deep Learning in Drug Discovery Domain?

Examples of players engaged in the deep learning in drug discovery domain (which have also been captured in this report) include (in alphabetic order) Atomwise, Benevolent.ai, Cloud Pharmaceuticals, Deargen, Deep Cure, Exscientia, GNS Healthcare, Insilico Medicine, Isomorphic Labs, Juvena Therapeutics, Merative, Optibrium and Valence Discovery.

Who are the Key Players in the Deep Learning in Diagnostics Domain?

Examples of players engaged in the deep learning in diagnostic domain (which have also been captured in this report) include (in alphabetic order) Avalon AI, Behold.ai, Blueberry Diagnostics, Deep Longevity, Esaote, Enlitic, Flatiron Health, H2O.ai, Huawei, InMed Prognostics, Kheiron Medical, Mediwhale, Nference and Visiopharm.

Scope of the Report

The study presents an in-depth analysis of the various firms / organizations that are engaged in this domain, across different segments as defined in the below table:

Report Attributes Details

Forecast Period

  • 2023 – 2035

Therapeutic Areas

  • Oncological Disorders
  • Oncological Disorders
  • Neurological Disorders
  • Neurological Disorders
  • Endocrine Disorders
  • Cardiovascular Disorders
  • Respiratory Disorders
  • Ophthalmic Disorders
  • Musculoskeletal Disorders
  • Inflammatory Disorders
  • Other Disorders

Key Geographical Regions

  • North America
  • Europe
  • Asia Pacific
  • Rest of the World

Key Companies Profiled

  • Aegicare
  • Aiforia Technologies
  • Ardigen
  • Berg
  • Google
  • Huawei
  • Merative
  • Nference
  • Nvidia
  • Owkin
  • Phenomic AI
  • Pixel AI

Customization Scope

  • 15% Free Customization Option (equivalent to 5 analyst’s working days)

PowerPoint Presentation (Complimentary)

  • Available

Excel Data Packs (Complimentary)

  • Market Landscape Analysis (Drug Discovery)
  • Market Landscape Analysis (Diagnostics)
  • Clinical Trial Analysis
  • Funding Analysis
  • Start-up Health Indexing
  • Company Valuation Analysis
  • Market Sizing and Opportunity Analysis (Drug Discovery)
  • Market Sizing and Opportunity Analysis (Diagnostics)

The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain, across different geographies. Amongst other elements, the report includes:

  • An executive summary of the insights captured during our research. It offers a high-level view on the current state of deep learning market for drug discovery and diagnostics 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. Further, it concludes with a discussion on various applications of deep learning within the healthcare industry.
  • A detailed assessment of the market landscape of more than 70 companies offering deep learning  technologies / services for the purpose of drug discovery, based on several relevant parameters, such as year of establishment, company size, location of headquarters, application area (drug discovery, and drug discovery and diagnostics), focus area (big data analysis, genomic data analysis, molecular data analysis, medical diagnosis, medical imaging and EMR analysis), therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, inflammatory disorders, metabolic disorders, pulmonary disorders, hepatic disorders, musculoskeletal disorders, dermatological disorders, gastrointestinal disorders and other disorders), operational model (service provider, technology / software developer and in-house developer), along with information on the company’s service and product centric models. 
  • A detailed assessment of the market landscape of more than 130 companies offering deep learning  technologies / services for diagnostics, based on several relevant parameters, such as year of establishment, company size, location of headquarters, application area (diagnostics, and drug discovery and diagnostics), focus area (big data analysis, genomic data analysis, medical screening, medical diagnosis, medical imaging, surgery planning and EMR analysis), therapeutic area (oncological disorders, neurological disorders, cardiovascular disorders, pulmonary disorders, infectious diseases, musculoskeletal disorders, metabolic disorders, ophthalmic disorders, hepatic disorders, gastrointestinal disorders, gynecological disorders, hematological disorders, urological diseases, dermatological disorders and other disorders), type of offering / solution (analysis reports, image processing, cloud based solutions and biomarker identification), along with information on various compatible device (CT, MRI, Ultrasound, X-Ray, Mammography, PET and others).
  • 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.  
  • An analysis of 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, based on several relevant parameters, such as focus area, therapeutic area, operational model, compatible device, type of offering and start-up health indexing.
  • An elaborate valuation analysis of companies that are involved in the deep learning in drug discovery and diagnostics market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.

One of the key objectives of the report was to estimate the current opportunity and future growth potential of deep learning market for drug discovery and diagnostic purposes over the coming years. We have provided informed estimates on the likely evolution of the market in the mid-to-long term, for the period, 2023-2035. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as therapeutic area (oncological disorders, infectious diseases, neurological disorders, immunological disorders, endocrine disorders, cardiovascular disorders, respiratory disorders, ophthalmic disorders, musculoskeletal 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 in the healthcare domain. In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s evolution.

The opinions and insights presented in the report were influenced by discussions held with stakeholders in this domain. The report features detailed transcripts of interviews held with the following individuals:

  • Mausumi Acharya (Chief Executive Officer, Advenio Technosys)
  • Vikas Karade (Founder, Chief Executive Officer, AlgoSurg)
  • Babak Rasolzadeh (Former Vice President of Product and Software Development, Arterys)
  • Carla Leibowitz (Head of Strategy and Marketing, Arterys)
  • Deekshith Marla (Founder, Chief Technology Officer, Arya.ai) and Sanjay Bhadra (Chief Business Officer, Arya.ai)
  • Walter de Back Former Research Scientist, Context Vision)
  • Kevin Choi (Chief Executive Officer, Mediwhale)
  • Avi Veidman (Chief Executive Officer, Nucleai), Yoav Blum (Director of AI, Nucleai) and Ken Bloom (Head of Pathology, Nucleai)

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

Frequently Asked Questions

Question 1: What is deep learning? What are the major factors driving the deep learning market focused on drug discovery and diagnostics?
Answer: The paradigm shift of industry players towards digitization and challenges associated with the drug discovery process have contributed to the overall adoption of deep learning technologies for drug discovery, leading to a reduced economic load. The potential of deep learning technologies in assisting medical personnel in an early-stage diagnosis of various disorders has fueled the adoption of such technologies in the diagnostics segment.

Question 2: Which companies offer deep learning technologies / services for drug discovery and diagnostics?
Answer: Presently, more than 200 players are engaged in the deep learning domain, offering technologies / services, specifically for drug discovery and diagnostics purposes.

Question 3: How much funding has taken place in field of deep learning in drug discovery and diagnostics?
Answer: Since 2019, more than USD 15 billion has been invested in the deep learning in drug discovery and diagnostics domain across multiple funding instances. Of these, the most prominent funding types included venture capital and grants, demonstrating high start-up activity in this domain.

Question 4: How many clinical trials, based on deep learning technologies, are being conducted?
Answer: Currently, more than 420 clinical trials are being conducted to evaluate the potential of deep learning for diagnostic purposes. Of these, 63% of the trials are active.

Question 5: What is the likely cost saving potential associated with the use of deep learning-based technologies in drug discovery?
Answer: Considering the vast potential of artificial intelligence, deep learning technologies are believed to save around 20% of the overall drug discovery costs.

Question 6: Which therapeutic area accounts for the largest share in the deep learning for drug discovery market?
Answer: Presently, oncological disorders capture the largest share (close to 40%) of the deep learning in drug discovery market. However, therapeutic areas, such as cardiovascular and respiratory disorders are likely to witness higher annual growth rates in the upcoming years. This can be attributed to the increasing applications of deep learning technologies across drug discovery.

Question 7: Which region is expected to witness the highest growth rate in the deep learning market for diagnostics?
Answer: The deep learning market for diagnostics in Asia Pacific is likely to grow at the highest CAGR, during the period 2023- 2035.

Contents

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

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