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

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 this 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.

SCOPE OF THE REPORT

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 20% 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.

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 (Chief Technical Officer, Arya.ai) and Sanjay Bhadra (Chief Operating Officer, Arya.ai)

Walter de Back (Former Research Scientist, Context Vision)

Kevin Choi (Chief Executive Officer, Mediwhale)

Avi Viedman (Chief Executive Officer, Nucleai) and Emily Salerno (Commercial Strategy and Operations Lead)

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.

RESEARCH METHODOLOGY

The data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews / surveys with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Wherever possible, the available data has been checked for accuracy from multiple sources of information.

Th secondary sources of information include:

Annual reports
Investor presentations
SEC filings
Industry databases
News releases from company websites
Government policy documents
Industry analysts' views

While the focus has been on forecasting the market till 2035, the report also provides our independent views on various technological and non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information.

KEY QUESTIONS ANSWERED

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 tor 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 diagnostics?

Answer: Considering the vast potential of artificial intelligence, deep learning technologies are believed to save around 45% of the overall drug diagnostic 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 North America is likely to grow at the highest CAGR, during the period 2023- 2035.

CHAPTER OUTLINES

Chapter 1 is a preface providing an introduction to the full report, Deep Learning in Drug Discovery and Deep Learning in Diagnostics Market, 2023-2035.

Chapter 2 provides an executive summary of the key insights captured in our report. It offers a high-level view of the current state of deep learning market and its likely evolution in the mid-to-long term.

Chapter 3 provides 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, the chapter concludes with a discussion on various applications of deep learning within the healthcare industry.

Chapter 4 includes detailed assessment of the overall 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.

Chapter 5 includes detailed assessment of the overall market landscape of more than 135 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).

Chapter 6 provides 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.

Chapter 7 features 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.

Chapter 8 provides 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).

Chapter 9 provides 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).

Chapter 10 provides 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.

Chapter 11 presents 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.

Chapter 12 presents 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.

Chapter 13 presents 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.

Chapter 14 presents 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.

Chapter 15 summarizes the overall report. In this chapter, we have provided a list of key takeaways from the report, and expressed our independent opinion related to the research and analysis described in the previous chapters.

Chapter 16 provides the transcripts of interviews conducted with key stakeholders in this industry. The chapter presents the details of our conversation with 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 (Chief Technical Officer, Arya.ai) and Sanjay Bhadra (Chief Operating Officer, Arya.ai), Walter de Back Former Research Scientist, Context Vision), Kevin Choi (Chief Executive Officer, Mediwhale) and Avi Viedman (Chief Executive Officer, Nucleai) and Emily Salerno (Commercial Strategy and Operations Lead)

Chapter 17 is an appendix, which contains tabulated data and numbers for all the figures included in the report.

Chapter 18 is an appendix, which contains a list of companies and organizations mentioned in this report.

Product Code: RA100419

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