Artificial Intelligence in Oncology Market: Distribution by Type of Cancer (Solid Malignancies, Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others), Type of End-Users (Hospitals, Pharmaceutical Companies, Research Institutes and Others) and Key Geographical Regions (North America, Europe, Asia-Pacific and Rest of the World): Industry Trends and Global Forecasts, 2022-2035

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Overview

Cancer is the one of the leading cause of deaths, globally, as per the World Health Organization (WHO). Annual statistics reported by the American Cancer Society (ACR) indicate that, in 2022, around 1.9 million individuals are likely to be diagnosed with various types of cancer in the US. During the same year, around 0.6 million cancer-related deaths are anticipated to be reported in the aforementioned region. In this context, it is important to highlight that, according to the International Agency for Cancer Research, by 2030, the number of cancer-related deaths is likely to rise by 72%. This, in turn, is expected to result in an increase of 70% in the global cancer burden, over the next two decades. Amidst the ever growing cancer burden, a number of strategies are being tested by researchers and industry players to help provide relief to the affected individuals. In recent years, artificial intelligence (AI) has emerged as a key enabler in improving the accuracy and speed of cancer diagnosis. Specifically, AI based cancer screening has resulted in reduced mortality rates of some prevalent malignancies. One of the most successful examples includes the detection of precancerous lesions, where timely treatment was demonstrated to considerably reduce the risk of malignant tumors. Consequently, several players engaged in the healthcare sector have incorporated AI powered technologies into their regular workflow to enable the identification of affected patients, thereby, ensuring timely treatment. 

Given the various advantages offered by AI technology, players engaged in the pharmaceutical domain have developed AI in oncology-based software solutions for the treatment of a myriad of oncological indications. These solutions help in interpretation and integration of huge volumes of complex data. Further, an AI system lowers the diagnostic and treatment related errors that are likely to occur in human clinical practice, thereby, resulting in reduced testing costs. Experts believe that there has been a significant rise in the revenue generation potential within this domain. This is further supported by the significant investments being made in this market. In fact, over the past five years, close to USD 6 billion has been invested in companies engaged in the development of AI in oncology-based software solutions. Further, the global spending on AI is forecasted to grow to more than USD 110 billion by 2024. Considering the rising popularity of such solutions in the healthcare industry and the ongoing efforts of software providers to further improve / expand their respective offerings, we believe that the AI in oncology market is likely to evolve at a steady pace, till 2030.

Scope of the Report

The ‘Artificial Intelligence in Oncology by Type of Cancer (Solid Malignancies, Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others), Type of End-Users (Hospitals, Pharmaceutical Companies, Research Institutes), Key Geographical Regions (North America, Europe, Asia-Pacific and Rest of the World): Industry Trends and Global Forecasts, 2022-2035’ report features an extensive study of the current market landscape and future potential associated with the AI in oncology market, over the next decade. The study also includes an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain. Amongst other elements, the report features: 

  • A detailed overview of the overall market landscape of companies engaged in the development of AI in oncology-based software solutions, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, type of service(s) offered (cancer detection, drug discovery, drug development), type of AI technology used (machine learning, deep learning), type of platform (cloud-based, on-site) and type of end-user (hospitals, pharma companies, research institutes).
  • Elaborate profiles of prominent players (shortlisted on the basis of company competitive analysis score) that specialize in offering AI in oncology-based software solutions. Each profile features a brief overview of the company, along with information on their year of establishment, number of employees, location of headquarters, key executives, proprietary technology platform(s), AI focused service portfolio, recent developments and an informed future outlook.
  • A detailed competitiveness analysis of companies engaged in the development of AI in oncology-based software solutions, based on their supplier strength (in terms of years of experience), portfolio diversity (based on the type of service(s), type of AI technology used, type of platform and type of end-user) and portfolio strength (in terms of number of platforms and target oncological indications).
  • An in-depth analysis of patents related to AI in oncology-based software solutions filed / granted till date, based on several relevant parameters, such as type of patent, publication year, geographical location / patent jurisdiction, legal status, CPC symbols, type of industry, type of applicant and leading players (in terms of number of patents filed / granted). In addition, it features a patent valuation analysis which evaluates the qualitative and quantitative aspects of the patents.
  • A detailed analysis of the partnerships and collaborations inked in the domain, during the period 2017-2022, based on several parameters, such as year of partnership, type of partnership, most active players (analysis by parent company and analysis by partner company), type of partner, type of cancer and region.
  • An analysis of the funding and investments made within the domain, during the period 2017-2022, based on several relevant parameters, such as year of funding, type of funding (seed financing, venture capital financing, debt financing, grants, IPOs and other offerings), leading players (in terms of amount invested) and key investors (in terms of number of funding instances) .
  • A detailed analysis of the current and future market based on blue ocean strategy, covering a strategic plan / guide for emerging players in this domain to help unlock an uncontested market, featuring thirteen strategic tools that can help software developers to shift towards a blue ocean strategic market.

One of the key objectives of the report was to evaluate the current market size and the future growth potential associated with the AI in oncology market, over the coming years. We have provided informed estimates of the likely evolution of the market in the short to mid-term and long term, for the period 2022-2035. Additionally, our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as [A] Type of Cancer (Solid Malignancies, Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others ), [B] Type of End-Users (Hospitals, Pharma Companies, Research Institutes and Others), [C] Key Geographical Regions (North America, Europe, Asia-Pacific and Rest of the World) 

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

  • Jon DeVries (Chief Executive Officer, Mirada Medical)
  • Piotr Krajewski (Chief Executive Officer, CancerCenter.AI)
  • Christian Vestergaard Kaltoft (Chief Executive Officer, Visiopharm)
  • David Wilson (Vice President, Marketing and Communications, Enlitic)
  • Emily Salerno (Commercial Strategy and Operations Lead, 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.

Key Questions Answered

  • Who are the leading players engaged in the development of AI in oncology-based software solutions?
  • Which type of end-users are primarily employing AI in oncology-based software solutions in their regular workflow?
  • What kind of partnership models are most commonly being adopted by stakeholders engaged in this domain?
  • What is the trend for capital investments in this domain?
  • What are the key strategies that can be implemented by emerging players / start-ups to enter into this highly competitive market?
  • What is the focus area of big pharma players in this domain?
  • Which companies are actively filing patents to drive innovation in the field of AI in oncology?
  • What are the key challenges associated within this domain?

Contents

Chapter Outlines

Chapter 2 is an executive summary of key insights captured during our research. It offers a high-level view on the current state of the artificial intelligence in oncology market and its likely evolution in the mid to long-term.

Chapter 3 provides a brief overview of artificial intelligence, machine learning and deep learning. Further, it highlights the classification of AI and its applications in the healthcare and oncology domain. The chapter further features various challenges associated with the adoption of AI in oncology-based software solutions and its future perspectives.

Chapter 4 provides a detailed overview of the overall  market landscape of  companies engaged in the development of AI in oncology- based software solutions, based on several relevant parameters, such as year of establishment, company size (in terms of number of employees), location of headquarters, type of service(s) offered (cancer detection, drug discovery, drug development), type of AI technology used (machine learning, deep learning), type of platform (cloud-based, on-site), type of end-user (hospitals, pharma companies, research institutes).

Chapter 5 provides elaborate profiles of prominent players (based on company competitive analysis score) engaged in offering AI in oncology- based software solutions. Each profile features a brief overview of the company along with information on their year of establishment, number of employees, location of headquarters, key executives, its proprietary platform(s), financial information of the company, AI focused service portfolio, recent developments and an informed future outlook. 

Chapter 6 provides an insightful company competitiveness analysis of AI in oncology- based software providers, based on their supplier strength (in terms of years of experience), portfolio diversity (which takes into account type of service(s), type of AI technology used, type of platform and type of end-user) and portfolio strength (which includes number of platform and target oncological indications).

Chapter 7 provides an in-depth analysis of patents related to AI in oncology- based software solutions filed / granted till date, based on several relevant parameters, such as type of patents, publication year, geographical location / patent jurisdiction, legal status, CPC symbols, type of industry, type of applicants and leading players (in terms of number of patents filed / granted), year-wise trend of filed patent applications and granted patents. In addition, it features a patent valuation analysis which evaluates the qualitative and quantitative aspects of the patents.

Chapter 8 provides an in-depth analysis of the various collaborations and partnerships that have been inked by stakeholders engaged in this domain, during the period 2017-2022. It includes a brief description of the partnership models (including acquisitions, commercialization agreements, technology utilization agreement, technology integration agreement, technology licensing agreement, distribution agreement, product development agreements, research development agreements and service alliance) adopted by stakeholders in this domain. Further, the partnership activity in this domain has been analyzed based on various parameters, such as year of partnership, type of partnership, analysis on most active players and most active partners, type of cancer. Further, the chapter includes a world map representation of all the deals inked in this field in the period 2017-2022, highlighting both intercontinental and intracontinental agreements.

Chapter 9 presents details on various investments received by various players engaged in this domain. Based on several relevant parameters, such as year of investment, number of funding instances, amount invested, type of funding (grant, seed, venture capital, initial public offering, secondary offering, other equity, and debt) and type of investor, along with information on the most active players (in terms of number of funding instances and amount raised), type of investors, most active investors (in terms  of number of funding instances), geographical distribution, area of application, type of cancer and focus area. 

Chapter 10 features an elaborate discussion on implementing blue ocean strategy, covering a strategic plan / guide for emerging software providers to help unlock an uncontested market, featuring thirteen strategic tools, modified in context to AI services in oncology, that can help companies to shift towards a blue ocean strategic market. The chapter also includes detailed analysis on buyer utility map, pioneer-migrator-settler map, and strategic canvas.

Chapter 11 presents an insightful market forecast analysis, highlighting the likely growth of AI services in oncology market till 2035. Additionally, our year-wise projections of the current and future opportunity have further been segmented based on several relevant parameters, such as Type of Cancer (Solid Malignancies, Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others), Type of End-Users (Hospitals, Pharmaceutical Companies, Research Institutes and Others), Key Geographical Regions (North America, Europe, Asia-Pacific and Rest of the World).

Chapter 12 is a summary of the entire report. It provides the key takeaways and presents our independent opinion of the AI in oncology market, based on the research and analysis described in the previously mentioned chapters.

Chapter 13 is a collection of interview transcripts of discussions held with various key stakeholders in this market. The chapter provides a brief overview of the companies and details of interviews held with Jon DeVries (Chief Executive Officer, Mirada Medical), Piotr Krajewski (Chief Executive Officer, CancerCenter.AI), Christian Vestergaard Kaltoft (Chief Executive Officer, Visiopharm), David Wilson (Vice President, Marketing and Communications, Enlitic),  Emily Salerno (Commercial Strategy and Operations Lead, Nucleai).

Chapter 14 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.

Chapter 15 is an appendix, which provides the list of companies and organizations mentioned in the report.

Table Of Contents

1. PREFACE
1.1. Overview
1.2. Scope of the Report
1.3. Market Segmentation
1.4. Research Methodology
1.5. Key Questions Answered
1.6. Chapter Outlines

2. EXECUTIVE SUMMARY
2.1 Chapter Overview

3. INTRODUCTION
3.1. Chapter Overview
3.2. Overview of Artificial Intelligence (AI)
3.3. Type Of AI
3.4. Applications of AI
3.5. Key Challenges Associated with Use of AI in Healthcare Sector
3.6. Future Perspectives

4. MARKET OVERVIEW
4.1. Chapter Overview
4.2. AI in Oncology: Market Landscape of Software providers
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters (Region-wise)
4.2.4. Analysis by Location of Headquarters (Country-wise)
4.2.5. Analysis by Type of End-User
4.2.6. Analysis by Year of Establishment, Company size and Location of Headquarters

4.3. AI in Oncology: Market Landscape of Software Solutions
4.3.1. Analysis by Type of Service(s) Offered
4.3.2. Analysis by Type of AI Technology Used
4.3.3. Analysis by Type of Platform
4.3.4. Analysis by Type of Service(s) Offered and Type of End-User
4.3.5. Analysis by Type of Platform and Type of AI Technology Used
4.3.6. Analysis by Type of Service(s) Offered, Location of Headquarters and Type of AI Technology Used

5. COMPANY PROFILES
5.1. Chapter Overview
5.2. Roche Diagnostics
5.2.1. Company Overview
5.2.2. Financial Information
5.2.3. AI Focused Service Portfolio
5.2.4. Recent Developments and Future Outlook

5.3. IBM Watson Health
5.3.1. Company Overview
5.3.2. Financial Information
5.3.3. AI Focused Service Portfolio
5.3.4. Recent Developments and Future Outlook

5.4. CancerCenter.AI
5.4.1. Company Overview
5.4.2. AI Focused Service Portfolio
5.4.3. Recent Development and Future Outlooks

5.5. GE Healthcare
5.5.1. Company Overview
5.5.2. Financial Information
5.5.3. AI Focused Service Portfolio
5.5.4. Recent Development and Future Outlook

5.6. Concert AI
5.6.1. Company Overview
5.6.2. AI Focused Service Portfolio
5.6.3. Recent Developments and Future Outlook

5.7. Path AI
5.7.1. Company Overview
5.7.2. AI Focused Service portfolio
5.7.3. Recent Development and Future Outlook

5.8. Berg
5.8.1. Company Overview
5.8.2. AI Focused Service Portfolio
5.8.3. Recent Development and Future Outlook

5.9. Median Technologies
5.9.1. Company Overview
5.9.2. Financial Information
5.9.3. AI Focused Service Portfolio
5.9.4. Recent Development and Future Outlook

5.10. iCAD
5.10.1. Company Overview
5.10.2. Financial Information
5.10.3. AI Focused Service Portfolio
5.10.4. Recent Developments and Future Outlook

5.11. JLK Inspection
5.11.1. Company Overview
5.11.2. AI Focused Service Portfolio
5.11.3. Recent Development and Future Outlook

6. COMPANY COMPETITIVENESS ANALYSIS
6.1. Chapter Overview
6.2. Assumptions and Key Parameters
6.3. Methodology
6.4. AI in Oncology Software providers: Company Competitiveness
6.4.1. Company Competitiveness: Small Companies in North America (Peer Group I)
6.4.2. Company Competitiveness: Small Companies in Europe (Peer Group II)
6.4.3. Company Competitiveness: Small Companies in Asia Pacific (Peer Group III)
6.4.4. Company Competitiveness: Mid-sized companies in North America (Peer Group IV)
6.4.5. Company Competitiveness: Mid-sized companies in Europe (Peer Group V)
6.4.6. Company Competitiveness: Mid-sized companies in Asia Pacific (Peer Group VI)
6.4.7. Company Competitiveness: Large companies in North America and Europe (Peer Group VII)

7. PATENT ANALYSIS
7.1. Chapter Overview
7.2. Scope and Methodology
7.3. AI in Oncology: Patent Analysis
7.3.1. Analysis by Type of Patent
7.3.2. Analysis by Patent Publication Year
7.3.3. Analysis by Year-wise Trend of Filed Patent Applications and Granted Patents
7.3.4. Analysis by Geography
7.3.5. Analysis by Type of Industry
7.3.6. Analysis by Patent Age
7.3.7. Analysis by Legal Status
7.3.8. Analysis by CPC Symbols
7.3.9. Analysis by Top Applicants
7.3.10. Analysis by Key Inventors

7.4. AI in Oncology: Patent Benchmarking Analysis
7.4.1. Analysis by Patent Characteristics
7.4.2. AI in Oncology: Patent Valuation Analysis

8. PARTNERSHIPS
8.1. Chapter Overview
8.2. Partnership Models
8.3 AI in Oncology: Recent Partnerships and Collaborations
8.3.1. Analysis by Year of Partnership
8.3.2. Analysis by Type of Partnership
8.3.3. Distribution by Year and Type of Agreement
8.3.4. Distribution by Company Size and Type of Agreement
8.3.5. Distribution by Most Active Players and Type of Agreement
8.3.6. Analysis by Type of Cancer
8.3.7. Analysis by Type of Partner
8.3.8. Analysis by Year and Type of Partner
8.3.9. Intercontinental and Intracontinental Agreements
8.3.10. Local and International Agreements
8.3.11. Distribution by Country
8.3.12. Analysis by Region
8.3.13. Most Active Partners: Distribution by Number of Partnerships

9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. AI in Oncology: List of Funding and Investment Analysis
9.3.1. Analysis by Year and Number of Funding Instances
9.3.2. Analysis by Year and Amount Invested
9.3.3 Analysis by Type of Funding and Number of Instances
9.3.4. Analysis by Year, Type of Funding and Amount Invested
9.3.5. Analysis by Type of Funding and Amount Invested
9.3.6. Analysis by Area of Application
9.3.7. Analysis by Focus Area
9.3.8. Analysis by Type of Cancer Indication
9.3.9. Analysis by Geography
9.3.10. Most Active Players by Number of Instances
9.3.11. Most Active Players by Amount Invested
9.3.12. Analysis by Type of Investors
9.3.13. Analysis by Lead Investors

9.4. Summary of Investments

9.5. Concluding Remarks

10. BLUE OCEAN STRATEGY: A STRATEGIC GUIDE FOR START-UPS TO ENTER INTO HIGHLY COMPETITIVE MARKET
10.1. Chapter Overview
10.2. Overview of Blue Ocean Strategy
10.2.1 Red Ocean
10.2.2 Blue Ocean
10.2.3 Comparison of Red Ocean Strategy and Blue Ocean Strategy
10.2.4. AI in Oncology: Blue Ocean Strategy and Shift Tools
10.2.4.1. Value Innovation
10.2.4.2. Strategy Canvas
10.2.4.3. Four Action Framework
10.2.4.4. Eliminate-Raise-Reduce-Create (ERRC) Grid
10.2.4.5. Six Path Framework
10.2.4.6. Pioneer-Migrator-Settler (PMS) Map
10.2.4.7. Three Tiers of Noncustomers
10.2.4.8. Sequence of Blue Ocean Strategy
10.2.4.9. Buyer Utility Map
10.2.4.10. The Price Corridor of the Mass
10.2.4.11. Four Hurdles to Strategy Execution
10.2.4.12. Tipping Point Leadership
10.2.4.13. Fair Process

10.3. Conclusion

11. MARKET SIZING AND OPPORTUNITY ANALYSIS
11.1. Chapter Overview
11.2. Forecast Methodology and Key Assumptions
11.3. Global Artificial Intelligence in Oncology Market, 2022-2035
11.4. Artificial Intelligence in Oncology Market: Analysis by Type of Cancer, 2022- 2035
11.4.1. Artificial Intelligence in Oncology Market for Solid Malignancies, 2022-2035
11.4.2. Artificial Intelligence in Oncology Market for Breast Cancer, 2022-2035
11.4.3. Artificial Intelligence in Oncology Market for Lung Cancer, 2022-2035
11.4.4. Artificial Intelligence in Oncology Market for Prostate Cancer, 2022-2035
11.4.5. Artificial Intelligence in Oncology Market for Colorectal Cancer, 2022-2035
11.4.6. Artificial Intelligence in Oncology Market for Brain Tumor, 2022-2035
11.4.7. Artificial Intelligence in Oncology Market for Others, 2022-2035
11.5. Artificial Intelligence in Oncology Market: Analysis by Type of End-User, 2022-2035
11.5.1. Artificial Intelligence in Oncology Market for Hospitals, 2022-2035
11.5.2. Artificial Intelligence in Oncology Market for Pharma Companies, 2022-2035
11.5.3. Artificial Intelligence in Oncology Market for Research Institutes, 2022-2035
11.5.4. Artificial Intelligence in Oncology Market for Others, 2022-2035
11.6. Artificial Intelligence in Oncology Market: Analysis by Key Geographical Regions, 2022-2035
11.6.1. Artificial Intelligence in Oncology Market for North America, 2022-2035
11.6.2. Artificial Intelligence in Oncology Market for Europe, 2022-2035
11.6.3. Artificial Intelligence in Oncology Market for Asia Pacific, 2022-2035
11.6.4. Artificial Intelligence in Oncology Market for Rest of the World, 2022-2035

12. CONCLUSION
12.1. Chapter Overview
12.2. Key Takeaways

13. EXECUTIVE INSIGHTS
13.1. Chapter Overview
13.2. Enlitic
13.2.1. Company Snapshot
13.2.2. Interview Transcript: David Wilson (Vice President, Marketing and Communications)

13.3. Nucleai
13.3.1. Company Snapshot
13.3.2. Interview Transcript: Emily Salerno (Commercial Strategy and Operations Lead)

13.4. Mirada Medical
13.4.1. Company Snapshot
13.4.2. Interview Transcript: Jon DeVries (Chief Executive Officer)

13.5. CancerCenter.AI
13.5.1. Company Snapshot
13.5.2. Interview Transcript: Piotr Krajewski (Chief Executive Officer)

13.6. Visiopharm
13.6.1 Company Snapshot
13.6.2 Interview Transcript: Christian Vestergaard Kaltoft (Chief Executive Officer)

14. APPENDIX 1: TABULATED DATA

15. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

Figure 2.1 Executive Summary: Overall Market Landscape
Figure 2.2 Executive Summary: Patent Analysis
Figure 2.3 Executive Summary: Partnerships and Collaboration Analysis
Figure 2.4 Executive Summary: Funding and Investment Analysis
Figure 2.5 Executive Summary: Market Forecast and Opportunity Analysis
Figure 3.1 Historical Evolution of AI
Figure 3.2 Relationship between AI, ML and DL
Figure 3.3 Type of AI
Figure 3.4 Artificial Intelligence Software Solutions: Distribution by Oncology-related Field
Figure 3.5 Various Types of Cancers
Figure 4.1 AI in Oncology Software providers: Distribution by Year of Establishment
Figure 4.2 AI in Oncology Software providers: Distribution by Company Size
Figure 4.3 AI in Oncology Software providers: Distribution by Location of Headquarters (Region-wise)
Figure 4.4 AI in Oncology Software providers: Distribution by Location of Headquarters (Country-wise)
Figure 4.5 AI in Oncology Software providers: Distribution by Type of End-User
Figure 4.6 AI in Oncology Software providers: Distribution by Year of Establishment, Company Size and Location of Headquarters
Figure 4.7 AI in Oncology- based Software Solutions: Distribution by Type of Service(s) Offered
Figure 4.8 AI in Oncology- based Software Solutions: Distribution by Type of AI Technology Used
Figure 4.9 AI in Oncology- based Software Solutions: Distribution by Type of Platform
Figure 4.10 AI in Oncology- based Software Solutions: Distribution by Type of Service(s) Offered and Type of end-user
Figure 4.11 AI in Oncology Software Solutions: Distribution by Type of Platform and Type of AI Technology Used
Figure 5.1 Roche Diagnostics: Annual Revenues, 2017-2021 (CHF Billion)
Figure 5.2 Roche Diagnostics: Service Portfolio
Figure 5.3 IBM Watson Health: Annual Revenues, 2017-2021 (USD Billion)
Figure 5.4 IBM Watson Health: Service Portfolio
Figure 5.5 CancerCenter.ai: Service Portfolio
Figure 5.6 GE Healthcare: Annual Revenues, 2017-2021 (USD Billion)
Figure 5.7 PathAI: Service Portfolio
Figure 5.8 BERG: Service Portfolio
Figure 5.9 Median Technologies: Annual Revenues, 2017-2021 (EUR Million)
Figure 5.10 iCAD: Annual Revenues, 2017-2021 (USD Million)
Figure 5.11 iCAD: Distribution of Revenues by Business Units, FY2021 (USD Million)
Figure 6.1 Company Competitiveness Analysis: Small Companies in North America (Peer Group I)
Figure 6.2 Company Competitiveness Analysis: Small Companies in Europe (Peer Group II)
Figure 6.3 Company Competitiveness Analysis: Small Companies in Asia Pacific (Peer Group III)
Figure 6.4 Company Competitiveness Analysis: Mid-sized companies in North America (Peer Group IV)
Figure 6.5 Company Competitiveness Analysis: Mid-sized companies in Europe (Peer Group V)
Figure 6.6 Company Competitiveness Analysis: Mid-sized companies in Asia Pacific (Peer Group VI)
Figure 6.7 Company Competitiveness Analysis: Large Companies in North America and Europe (Peer Group VII)
Figure 7.1 Patent Analysis: Distribution by Type of Patents
Figure 7.2 Patent Analysis: Cumulative Distribution by Publication Year
Figure 7.3 Patent Analysis: Year-wise Distribution of Filed Patent Applications and Granted Patents
Figure 7.4 Patent Analysis: Distribution by Jurisdiction
Figure 7.5 Patent Analysis: Cumulative Distribution by Type of Industry
Figure 7.6 Patent Analysis: Distribution by Patent Age
Figure 7.7 Patent Analysis: Distribution by Legal Status
Figure 7.8 Patent Analysis: Distribution by CPC Symbols
Figure 7.9 Leading Industry Players: Distribution by Number of Patents
Figure 7.10 Leading Non-Industry Players: Distribution by Number of Patents
Figure 7.11 Patent Analysis: Distribution by Key Inventors
Figure 7.12 Patent Analysis (Top 10 CPC Symbols): Benchmarking by Leading Industry Players
Figure 7.13 AI in Oncology: Patent Valuation Analysis
Figure 8.1 Partnerships and Collaborations: Distribution by Year of Partnership, 2017- 2022
Figure 8.2 Partnerships and Collaborations: Distribution by Type of Partnership
Figure 8.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Figure 8.4 Partnerships and Collaborations: Distribution by Company Size and Type of Partnership
Figure 8.5 Most Active Players: Distribution by Type of Partnership
Figure 8.6 Partnerships and Collaborations: Distribution by Type of Cancer
Figure 8.7 Partnerships and Collaborations: Distribution by Type of Partner
Figure 8.8 Partnerships and Collaborations: Distribution by Year and Type of Partner
Figure 8.9 Partnerships and Collaborations: Intercontinental and Intracontinental Agreement
Figure 8.10 Partnerships and Collaborations: Local and International Agreement
Figure 8.11 Partnerships and Collaborations: Distribution by Country
Figure 8.12 Partnerships and Collaborations: Distribution by Region
Figure 8.13 Most Active Partners: Distribution by Number of Partnerships
Figure 9.1 Funding and Investment Analysis: Cumulative Year-wise Distribution by Number of Instances, 2017-2022
Figure 9.2 Funding and Investment Analysis: Cumulative Year-wise Distribution by Amount Invested, 2017-2022 (USD Billion)
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 Year and Type of Funding, 2017-2022 (USD Million)
Figure 9.5 Funding and Investment Analysis: Distribution by Amount Invested and Type of Funding (USD Billion)
Figure 9.6 Funding and Investment Analysis: Distribution of Funding Instances by Area of Application
Figure 9.7 Funding and Investment Analysis: Distribution of Amount Invested by Area of Application (USD Million)
Figure 9.8 Funding and Investment Analysis: Distribution of Instances by Focus Area
Figure 9.9 Funding and Investment Analysis: Distribution of Instances by Type of Cancer
Figure 9.10 Funding and Investment Analysis: Distribution by Geography
Figure 9.11 Most Active Players: Distribution by Number of Instances
Figure 9.12 Most Active Players: Distribution by Amount Invested (USD Million)
Figure 9.13 Funding and Investment Analysis: Distribution by Type of Investors
Figure 9.14 Most Active Investors: Distribution by Number of Instances
Figure 9.15 Funding and Investment Analysis: Summary of Amount Invested, 2017-2022 (USD Million)
Figure 9.16 Funding and Investment Analysis: Concluding Remarks
Figure 10.1 Red Ocean Strategy vs Blue Ocean Strategy
Figure 10.2 Blue Ocean Strategy: Value Innovation
Figure 10.3 Blue Ocean Strategy: Strategy Canvas
Figure 10.4 Blue Ocean Strategy: Four Action Framework
Figure 10.5 Blue Ocean Strategy: Eliminate-Raise-Reduce-Create (ERRC) Grid
Figure 10.6 Blue Ocean Strategy: Six Path Framework
Figure 10.7 Blue Ocean Strategy: Pioneer-Migrator-Settler (PMS) Map
Figure 10.8 Blue Ocean Strategy: Three Tiers of Noncustomers
Figure 10.9 Blue Ocean Strategy: Sequence of Blue Ocean Strategy
Figure 10.10 Blue Ocean Strategy: Buyer Utility Map
Figure 10.11 Blue Ocean Strategy: The Price Corridor of the Mass
Figure 10.12 Blue Ocean Strategy: Four Hurdles to Strategy Execution
Figure 10.13 Blue Ocean Strategy: Tipping Point Leadership
Figure 10.14 Blue Ocean Strategy: Fair Processes
Figure 11.1 Global Artificial Intelligence in Oncology Market, 2022-2035 (USD Billion)
Figure 11.2 Artificial Intelligence in Oncology Market: Distribution by Type of Cancer, 2022-2035 (USD Billion)
Figure 11.3 Artificial Intelligence in Oncology Market for Solid Malignancies, 2022-2035 (USD Billion)
Figure 11.4 Artificial Intelligence in Oncology Market for Breast Cancer, 2022-2035 (USD Billion)
Figure 11.5 Artificial Intelligence in Oncology Market for Lung Cancer, 2022-2035 (USD Billion)
Figure 11.6 Artificial Intelligence in Oncology Market for Prostate Cancer, 2022-2035 (USD Billion)
Figure 11.7 Artificial Intelligence in Oncology Market for Colorectal Cancer, 2022-2035 (USD Billion)
Figure 11.8 Artificial Intelligence in Oncology Market for Brain Tumor, 2022-2035 (USD Billion)
Figure 11.9 Artificial Intelligence in Oncology Market for Others, 2022-2035 (USD Billion)
Figure 11.10 Artificial Intelligence in Oncology Market: Distribution by Type of End-User, 2022-2035 (USD Billion)
Figure 11.11 Artificial Intelligence in Oncology Market for Hospitals, 2022-2035 (USD Billion)
Figure 11.12 Artificial Intelligence in Oncology Market for Pharmaceutical Companies, 2022- 2035 (USD Billion)
Figure 11.13 Artificial Intelligence in Oncology Market for Research Institutes, 2022-2035 (USD Billion)
Figure 11.14 Artificial Intelligence in Oncology Market for Others, 2022-2035 (USD Billion)
Figure 11.15 Artificial Intelligence in Oncology Market: Distribution by Geography, 2022-2035 (USD Billion)
Figure 11.16 Artificial Intelligence in Oncology Market for North America, 2022-2035 (USD Billion)
Figure 11.17 Artificial Intelligence in Oncology Market for Europe, 2022-2035 (USD Billion)
Figure 11.18 Artificial Intelligence in Oncology Market for Asia Pacific, 2022-2035 (USD Billion)
Figure 11.19 Artificial Intelligence in Oncology Market for Rest of the World, 2022-2035 (USD Billion)
Figure 12.1 Concluding Remarks: Overall Market Landscape
Figure 12.2 Concluding Remarks: Company Competitiveness Analysis
Figure 12.3 Concluding Remarks: Patent Analysis
Figure 12.4 Concluding Remarks: Partnerships and Collaboration Analysis
Figure 12.5 Concluding Remarks: Funding and Investment Analysis
Figure 12.6 Concluding Remarks: Blue Ocean Strategy
Figure 12.7 Concluding Remarks: Market Forecast and Opportunity Analysis

List Of Tables

Table 4.1 AI in Oncology: List of Software providers
Table 4.2 AI in Oncology Software providers: Information on Type of Service(s) Offered
Table 4.3 AI in Oncology Software providers: Information on the Type of AI Technology Used
Table 4.4 AI in Oncology Software providers: Information on Type of Platform
Table 4.5 AI in Oncology Software providers: Information on the Type of End-User
Table 4.6 AI in Oncology Software providers: Information on Type of Additional Service(s) Offered
Table 5.1 Roche Diagnostics: Key Highlights
Table 5.2 Roche Diagnostics: Recent Developments and Future Outlook
Table 5.3 IBM Watson Health: Key Highlights
Table 5.4 IBM Watson Health: Recent Developments and Future Outlook
Table 5.5 CancerCenter.ai: Key Highlights
Table 5.6 CancerCenter.ai: Recent Developments and Future Outlook
Table 5.7 GE Healthcare: Key Highlights
Table 5.8 GE Healthcare: Recent Developments and Future Outlook
Table 5.9 Concert AI: Key Highlights
Table 5.10 Path AI: Key Highlights
Table 5.11 PathAI: Recent Developments and Future Outlook
Table 5.12 BERG: Key Highlights
Table 5.13 BERG: Recent Developments and Future Outlook
Table 5.14 Median Technologies: Key Highlights
Table 5.15 Median Technologies: Recent Developments and Future Outlook
Table 5.16 iCAD: Key Highlights
Table 5.17 iCAD: Recent Developments and Future Outlook
Table 5.18 JLK Inspection: Key Highlights
Table 5.19 JLK Inspection: Recent Developments and Future Outlook
Table 7.1 Patent Analysis: CPC Symbols
Table 7.2 Patent Analysis: Most Popular CPC Symbols
Table 7.3 Patent Analysis: List of Top CPC Symbols
Table 7.4 Patent Analysis: Categorization based on Weighted Valuation Scores
Table 7.5 Patent Analysis: List of Relatively High Value Patents
Table 8.1 Partnerships and Collaborations: List of Partnerships and Collaborations, 2017-2022
Table 9.1 AI in Oncology: List of Funding and Investments, 2017-2022
Table 9.2 Funding and Investment Analysis: Summary of Investments (Number of Instances)
Table 9.3 Funding and Investment Analysis: Summary of Investments (Total Amount Invested)
Table 9.4 Funding and Investment Analysis: Summary of Venture Capital Funding
Table 14.1 AI in Oncology Software providers: Distribution by Year of Establishment
Table 14.2 AI in Oncology Software providers: Distribution by Company Size
Table 14.3 AI in Oncology Software providers: Distribution by Location of Headquarters (Region-wise)
Table 14.4 AI in Oncology Software providers: Distribution by Location of Headquarters (Country-wise)
Table 14.5 AI in Oncology Software providers: Distribution by Type of End-User
Table 14.6 AI in Oncology Software providers: Distribution by Year of Establishment, Company Size and Location of Headquarters
Table 14.7 AI in Oncology- based Software Solutions: Distribution by Type of Service(s) Offered
Table 14.8 AI in Oncology- based Software Solutions: Distribution by Type of AI Technology Used
Table 14.9 AI in Oncology- based Software Solutions: Distribution by Type of Platform
Table 14.10 AI in Oncology- based Software Solutions: Distribution by Type of Service(s) Offered and Type of End User
Table 14.11 AI in Oncology-based Software Solutions: Distribution by Type of Platform and Type of AI Technology Used
Table 14.12 Roche Diagnostics: Annual Revenues (CHF Billion)
Table 14.13 IBM Watson Health: Annual Revenues (USD Billion)
Table 14.14 GE Healthcare: Annual Revenues (USD Billion)
Table 14.15 Median Technologies: Annual Revenues (EUR Million)
Table 14.16 iCAD: Annual Revenues (USD Million)
Table 14.17 Patent Analysis: Distribution by Type of Patents
Table 14.18 Patent Analysis: Cumulative Distribution by Publication Year
Table 14.19 Patent Analysis: Year-Wise Distribution of Filed Patent Applications and Granted Patents
Table 14.20 Patent Analysis: Distribution by Jurisdiction
Table 14.21 Patent Analysis: Cumulative Distribution by Type of Industry
Table 14.22 Patent Analysis: Distribution by Patent Age
Table 14.23 Patent Analysis: Distribution by Legal Status
Table 14.24 Leading Industry Players: Distribution by Number of Patents
Table 14.25 Leading Non-Industry Players: Distribution by Number of Patents
Table 14.26 Patent Analysis: Distribution by Key Inventors
Table 14.27 AI in Oncology: Patent Valuation Analysis
Table 14.28 Partnerships and Collaborations: Distribution by Year of Partnership, 2017- 2022
Table 14.29 Partnerships and Collaborations: Distribution by Type of Partnership
Table 14.30 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Table 14.31 Partnerships and Collaborations: Distribution by Company Size and Type of Partnership
Table 14.32 Partnerships and Collaborations: Distribution by Most Active Partners and Type of Partnership
Table 14.33 Partnerships and Collaborations: Distribution by Type of Cancer
Table 14.34 Partnerships and Collaborations: Distribution by Type of Partner
Table 14.35 Partnerships and Collaborations: Distribution by Year and Type of Partner
Table 14.36 Partnerships and Collaborations: Intercontinental and Intracontinental Agreement
Table 14.37 Partnerships and Collaborations: Local and International Agreement
Table 14.38 Partnerships and Collaborations: Distribution by Country
Table 14.39 Partnerships and Collaborations: Distribution by Region
Table 14.40 Most Active Players: Distribution by number of Partnerships
Table 14.41 Funding and Investment Analysis: Cumulative Year-wise Distribution by Number of Instances, 2017-2022
Table 14.42 Funding and Investment Analysis: Cumulative Year-wise Distribution by Amount Invested, 2017-2022 (USD Billion)
Table 14.43 Funding and Investment Analysis: Distribution of Instances by Type of Funding
Table 14.44 Funding and Investment Analysis: Distribution of Amount Invested and Type of Funding (USD Million)
Table 14.45 Funding and Investment Analysis: Most Active Players: Distribution by Number of Instances
Table 14.46 Funding and Investment Analysis: Most Active Players: Distribution by Amount Invested (USD Million)
Table 14.47 Funding and Investment Analysis: Distribution of Funding Instances by Area of Application
Table 14.48 Funding and Investment Analysis: Distribution of Instances by Focus Area
Table 14.49 Funding and Investment Analysis: Distribution by Geography
Table 14.50 Funding and Investment Analysis: Distribution of Instances by Type of Cancer
Table 14.51 Funding and Investment Analysis: Most Active Investors: Distribution by Number of Instances
Table 14.52 Funding and Investment Analysis: Distribution by Type of Lead Investors
Table 14.53 Funding and Investment Analysis: Summary of Amount Invested, 2017-2022 (USD Million)
Table 14.54 Global Artificial Intelligence in Oncology Market 2022-2035, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.55 Artificial Intelligence in Oncology Market: Distribution by Type of Cancer, 2022-2035
Table 14.56 Artificial Intelligence in Oncology Market for Solid Malignancies, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.57 Artificial Intelligence in Oncology Market for Breast Cancer, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.58 Artificial Intelligence in Oncology Market for Lung Cancer, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.59 Artificial Intelligence in Oncology Market for Prostate Cancer, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.60 Artificial Intelligence in Oncology Market for Colorectal Cancer, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.61 Artificial Intelligence in Oncology Market for Brain Tumor, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.62 Artificial Intelligence in Oncology Market for Others, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.63 Artificial Intelligence in Oncology Market: Distribution by Type of End-Users, 2022-2035
Table 14.64 Artificial Intelligence in Oncology Market for Hospitals, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.65 Artificial Intelligence in Oncology Market for Pharmaceutical Companies, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.66 Artificial Intelligence in Oncology Market for Research Institutes, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.67 Artificial Intelligence in Oncology Market for Others, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.68 Artificial Intelligence in Oncology Market: Distribution by Key Geographical Regions, 2022-2035
Table 14.69 Artificial Intelligence in Oncology Market for North America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.70 Artificial Intelligence in Oncology Market for Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.71 Artificial Intelligence in Oncology Market for Asia Pacific, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 14.72 Artificial Intelligence in Oncology Market for Rest of the World, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)

List Of Companies

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

  1. 500 Startups
  2. 6 Dimensions Capital
  3. 83North
  4. 8VC
  5. Affidea
  6. Aidence
  7. Aidoc
  8. AIRA Matrix
  9. Alexandria Venture Investments
  10. AllianceBernstein
  11. Ally Bridge Group
  12. Ambra Health
  13. AmCad BioMed
  14. aMoon Fund
  15. Amplify Partners
  16. Ankur Capital
  17. Apollo Hospital 
  18. Arterys
  19. ARUP Laboratories 
  20. Asset Management Ventures
  21. AstraZeneca
  22. Athensmed 
  23. Atomico
  24. Accelerating Technology Purposefully
  25. AXA Venture Partners
  26. Axilor Ventures
  27. Baheya Foundation 
  28. Bain Capital Life Sciences
  29. BankInvest
  30. BEENEXT
  31. Benslie Investment Group
  32. BERG
  33. BioAdvance
  34. Biotheranostics 
  35. Blackford 
  36. Blue Pool Capital
  37. Boehringer Ingelheim Venture Fund
  38. Borski Fund
  39. b-rayZ
  40. Breyer Capital
  41. BrightEdge 
  42. Bristol-Myers Squibb
  43. British Business Bank 
  44. BVF Partners
  45. C.L. David Foundation
  46. Cambridge Capital Group
  47. CancerCenter.ai
  48. Canon Medical Systems 
  49. Capitol Health
  50. Casdin Capital
  51. Catalio Capital Management
  52. Charles River Ventures
  53. China Merchant Securities International
  54. Colorectal Cancer Alliance
  55. ConcertAI
  56. Connect Ventures
  57. Cormorant Asset Management
  58. Cosmo Pharmaceuticals
  59. Coutts 
  60. CPP Investments
  61. Cornerstone Total Return Fund 
  62. CureMatch
  63. CureMetrix
  64. D.E. Shaw Research
  65. D1 Capital Partners
  66. Dana Farber Cancer Institute 
  67. Danhua Capital
  68. Data Collective Venture Capital
  69. Debiopharm Innovation Fund 
  70. Declaration Partners
  71. Deep Bio
  72. DeepHealth
  73. DeepMind
  74. Delin Ventures
  75. Dell Technologies Capital
  76. Densitas®
  77. DilenyTech 
  78. DocPanel
  79. Dream Incubator
  80. East Hi-tech
  81. EcoR1 Capital
  82. Emergent Medical Partners
  83. Enlitic
  84. Envestors
  85. European Innovation Council
  86. European Investment Bank
  87. European Research Council
  88. Exor Seeds
  89. Farallon Capital Management
  90. Fidelity Management and Research
  91. Foresite Capital
  92. Formation 8
  93. DNA Capital
  94. Founders Fund
  95. Foxconn
  96. Franklin Templeton
  97. Freenome
  98. FUJIFILM 
  99. Fund Twenty8
  100. GE Healthcare
  101. GE Ventures
  102. Gebert Rüf Stiftung
  103. General Atlantic
  104. General Catalyst
  105. Generali
  106. GenWorks Health
  107. Global Ventures 
  108. Goldman Sachs
  109. Google
  110. GRAIL
  111. Greycroft
  112. Grove Ventures 
  113. Guardant Health
  114. Guerbet
  115. Hanfor Capital Management
  116. Harmonix 
  117. Health Innovations
  118. HealthCare Konnect
  119. HealthQuest Capital
  120. henQ
  121. Hera-MI
  122. HERAN Partners
  123. Hillhouse Capital Group
  124. Hina Group
  125. Holland Capital
  126. Hologic 
  127. Horizon 2020
  128. Horizon Ventures
  129. Hoxton Ventures
  130. HuangPu River Capital
  131. Huiying Medical 
  132. Ibex Medical Analytics
  133. IBM Watson Health
  134. iCAD
  135. ICBC International
  136. Icebreaker.vc
  137. Ikonopedia 
  138. Illumina
  139. IMM Investment
  140. Indica Labs 
  141. Information Technology Academia Collaboration
  142. INKEF Capital
  143. Innovate UK
  144. Innovation Endeavors
  145. Innovation Quarter
  146. Inobe
  147. Insight Partners
  148. Inspirata
  149. Institut Curie
  150. Intervest
  151. IQ Capital
  152. iSeed Ventures
  153. Janus Henderson Investors
  154. JLK Inspection 
  155. Johnson & Johnson
  156. Kaiku Health
  157. Kaiser Permanente
  158. Kakao Ventures
  159. Kamet Ventures
  160. Kansen voor West
  161. KdT Ventures
  162. Kheiron Medical Technologies
  163. Korea Health Industry Development Institute
  164. Kinship Trust 
  165. KT Investment
  166. Laerdal
  167. LDPath
  168. Legend Capital
  169. Leica Biosystems
  170. LEO Pharma
  171. Lightpoint Medical
  172. Liverpool Heart and Chest Hospital 
  173. Lucida Medical
  174. Luminous Ventures
  175. LungLife AI
  176. Lunit
  177. M Capital 
  178. Mamotest 
  179. Marubeni Corporation
  180. MassMutual Ventures
  181. Maverick Ventures
  182. Median Technologies 
  183. Medical EarlySign
  184. Medicover
  185. MediPath
  186. Medtronic
  187. Memorial Sloan Kettering Cancer Center
  188. Merck 
  189. Metaplanet
  190. Metsola Ventures
  191. Mirada Medical
  192. Mirae Asset Venture Investment
  193. MobileODT
  194. Mustard Seed 
  195. MVision AI
  196. Nanox
  197. National Center for Research and Development
  198. National Education Association 
  199. NESTA
  200. New Enterprise Associates
  201. NewMargin Ventures
  202. NewYork-Presbyterian
  203. Nice University Hospital 
  204. Niramai
  205. Northpond Ventures
  206. Northwell Health 
  207. Northzone
  208. Novarad
  209. Novo Ventures
  210. NSG Ventures
  211. Nuard Ventures
  212. Nucleai
  213. Octopus Ventures
  214. OMERS Ventures
  215. OMNES
  216. Oncora Medical
  217. Onward Assist
  218. East Netherlands Development Agency
  219. Optellum
  220. OrbiMed
  221. ORI Capital
  222. Oxford Technology
  223. Paige
  224. PathAI
  225. Perceptive Advisors
  226. Philips 
  227. PHS Fund
  228. pi Ventures
  229. Pillar Companies
  230. Ping An Global Voyager Fund
  231. Planven Entrepreneur Ventures
  232. Polaris Partners
  233. Prodeko Ventures
  234. Prognica Labs 
  235. Proscia
  236. PSP Investments
  237. Qingsong Fund
  238. Qlarity Imaging
  239. Quantib
  240. Qure.ai
  241. QView Medical
  242. RA Capital Management
  243. Rabo Ventures
  244. RAD-AID International
  245. RadLink
  246. RadNet
  247. RaySearch Laboratories
  248. Reaktor Ventures
  249. Refactor Capital
  250. Regal Funds Management
  251. Relay Therapeutics
  252. Revelation Partners
  253. Revolution Growth
  254. Rezolut
  255. Riverain Technologies 
  256. Roche Diagnostics 
  257. Roche Venture Fund
  258. Rock Springs Capital
  259. Sana Kliniken 
  260. Sascan Meditech
  261. ScreenPoint Medical 
  262. Section 32
  263. Seno Medical
  264. Sequoia Capital
  265. Shinhan Investment
  266. Siemens Healthineers
  267. SK Telecom
  268. Skin Analytics 
  269. SkinVision
  270. SME Instrument
  271. SoftBank Vision Fund
  272. Soleria Capital
  273. Solis Mammography 
  274. S28 Capital 
  275. St. John's College
  276. Stanford University
  277. SymphonyAI 
  278. SyndicateRoom
  279. T. Rowe Price
  280. Tara Health
  281. Tavistock Group
  282. Telix Pharmaceuticals
  283. Temasek 
  284. Tempus Labs 
  285. Tencent
  286. The Jagen Group
  287. The Netherlands Cancer Institute 
  288. The NorthCap University
  289. TheraPanacea
  290. Therapixel
  291. Think.Health
  292. Third Rock Ventures
  293. Thirona
  294. Thorney Investment 
  295. TRAF Intercontinental 
  296. Tristel
  297. Turbine 
  298. Tybourne Capital Management
  299. National Cancer Institute
  300. Innovate UK 
  301. Unilabs
  302. University of Oxford
  303. University of Pittsburgh Medical Center
  304. University Radiology 
  305. Vaekstfonden
  306. Vanedge Capital
  307. Vara
  308. Varian Medical Systems
  309. VentureFounders
  310. Verily Life Sciences
  311. Vertex Ventures
  312. VieCure
  313. Atlantic Labs
  314. Visiopharm
  315. Voima Ventures
  316. Volpara Health
  317. VoxelCloud
  318. Whiterabbit.ai
  319. Wondfo Biotech
  320. WuXi NextCODE
  321. xCures
  322. Xingtai People’s Hospital
  323. Yizhun Intelligent

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