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Artificial Intelligence in Oncology Market: Distribution by Type of Cancer, Type of End-Users and Key Geographical Regions: Industry Trends and Global Forecasts, 2022-2035

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    Report

  • 240 Pages
  • August 2022
  • Region: Global
  • Roots Analysis
  • ID: 5659467
Description Jump to:

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?

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 DATA15. 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)

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • 6 Dimensions Capital
  • 83North
  • 8VC
  • Affidea
  • Aidence
  • Aidoc
  • AIRA Matrix
  • Alexandria Venture Investments
  • AllianceBernstein
  • Ally Bridge Group
  • Ambra Health
  • AmCad BioMed
  • aMoon Fund
  • Amplify Partners
  • Ankur Capital
  • Apollo Hospital 
  • Arterys
  • ARUP Laboratories 
  • Asset Management Ventures
  • AstraZeneca
  • Athensmed 
  • Atomico
  • Accelerating Technology Purposefully
  • AXA Venture Partners
  • Axilor Ventures
  • Baheya Foundation 
  • Bain Capital Life Sciences
  • BankInvest
  • BEENEXT
  • Benslie Investment Group
  • BERG
  • BioAdvance
  • Biotheranostics 
  • Blackford 
  • Blue Pool Capital
  • Boehringer Ingelheim Venture Fund
  • Borski Fund
  • b-rayZ
  • Breyer Capital
  • BrightEdge 
  • Bristol-Myers Squibb
  • British Business Bank 
  • BVF Partners
  • C.L. David Foundation
  • Cambridge Capital Group
  • CancerCenter.ai
  • Canon Medical Systems 
  • Capitol Health
  • Casdin Capital
  • Catalio Capital Management
  • Charles River Ventures
  • China Merchant Securities International
  • Colorectal Cancer Alliance
  • ConcertAI
  • Connect Ventures
  • Cormorant Asset Management
  • Cosmo Pharmaceuticals
  • Coutts 
  • CPP Investments
  • Cornerstone Total Return Fund 
  • CureMatch
  • CureMetrix
  • D.E. Shaw Research
  • D1 Capital Partners
  • Dana Farber Cancer Institute 
  • Danhua Capital
  • Data Collective Venture Capital
  • Debiopharm Innovation Fund 
  • Declaration Partners
  • Deep Bio
  • DeepHealth
  • DeepMind
  • Delin Ventures
  • Dell Technologies Capital
  • Densitas®
  • DilenyTech 
  • DocPanel
  • Dream Incubator
  • East Hi-tech
  • EcoR1 Capital
  • Emergent Medical Partners
  • Enlitic
  • Envestors
  • European Innovation Council
  • European Investment Bank
  • European Research Council
  • Exor Seeds
  • Farallon Capital Management
  • Fidelity Management and Research
  • Foresite Capital
  • Formation 8
  • DNA Capital
  • Founders Fund
  • Foxconn
  • Franklin Templeton
  • Freenome
  • FUJIFILM 
  • Fund Twenty8
  • GE Healthcare
  • GE Ventures
  • Gebert Rüf Stiftung
  • General Atlantic
  • General Catalyst
  • Generali
  • GenWorks Health
  • Global Ventures 
  • Goldman Sachs
  • Google
  • GRAIL
  • Greycroft
  • Grove Ventures 
  • Guardant Health
  • Guerbet
  • Hanfor Capital Management
  • Harmonix 
  • Health Innovations
  • HealthCare Konnect
  • HealthQuest Capital
  • henQ
  • Hera-MI
  • HERAN Partners
  • Hillhouse Capital Group
  • Hina Group
  • Holland Capital
  • Hologic 
  • Horizon 2020
  • Horizon Ventures
  • Hoxton Ventures
  • HuangPu River Capital
  • Huiying Medical 
  • Ibex Medical Analytics
  • IBM Watson Health
  • iCAD
  • ICBC International
  • Icebreaker.vc
  • Ikonopedia 
  • Illumina
  • IMM Investment
  • Indica Labs 
  • Information Technology Academia Collaboration
  • INKEF Capital
  • Innovate UK
  • Innovation Endeavors
  • Innovation Quarter
  • Inobe
  • Insight Partners
  • Inspirata
  • Institut Curie
  • Intervest
  • IQ Capital
  • iSeed Ventures
  • Janus Henderson Investors
  • JLK Inspection 
  • Johnson & Johnson
  • Kaiku Health
  • Kaiser Permanente
  • Kakao Ventures
  • Kamet Ventures
  • Kansen voor West
  • KdT Ventures
  • Kheiron Medical Technologies
  • Korea Health Industry Development Institute
  • Kinship Trust 
  • KT Investment
  • Laerdal
  • LDPath
  • Legend Capital
  • Leica Biosystems
  • LEO Pharma
  • Lightpoint Medical
  • Liverpool Heart and Chest Hospital 
  • Lucida Medical
  • Luminous Ventures
  • LungLife AI
  • Lunit
  • M Capital 
  • Mamotest 
  • Marubeni Corporation
  • MassMutual Ventures
  • Maverick Ventures
  • Median Technologies 
  • Medical EarlySign
  • Medicover
  • MediPath
  • Medtronic
  • Memorial Sloan Kettering Cancer Center
  • Merck 
  • Metaplanet
  • Metsola Ventures
  • Mirada Medical
  • Mirae Asset Venture Investment
  • MobileODT
  • Mustard Seed 
  • MVision AI
  • Nanox
  • National Center for Research and Development
  • National Education Association 
  • NESTA
  • New Enterprise Associates
  • NewMargin Ventures
  • NewYork-Presbyterian
  • Nice University Hospital 
  • Niramai
  • Northpond Ventures
  • Northwell Health 
  • Northzone
  • Novarad
  • Novo Ventures
  • NSG Ventures
  • Nuard Ventures
  • Nucleai
  • Octopus Ventures
  • OMERS Ventures
  • OMNES
  • Oncora Medical
  • Onward Assist
  • East Netherlands Development Agency
  • Optellum
  • OrbiMed
  • ORI Capital
  • Oxford Technology
  • Paige
  • PathAI
  • Perceptive Advisors
  • Philips 
  • PHS Fund
  • pi Ventures
  • Pillar Companies
  • Ping An Global Voyager Fund
  • Planven Entrepreneur Ventures
  • Polaris Partners
  • Prodeko Ventures
  • Prognica Labs 
  • Proscia
  • PSP Investments
  • Qingsong Fund
  • Qlarity Imaging
  • Quantib
  • Qure.ai
  • QView Medical
  • RA Capital Management
  • Rabo Ventures
  • RAD-AID International
  • RadLink
  • RadNet
  • RaySearch Laboratories
  • Reaktor Ventures
  • Refactor Capital
  • Regal Funds Management
  • Relay Therapeutics
  • Revelation Partners
  • Revolution Growth
  • Rezolut
  • Riverain Technologies 
  • Roche Diagnostics 
  • Roche Venture Fund
  • Rock Springs Capital
  • Sana Kliniken 
  • Sascan Meditech
  • ScreenPoint Medical 
  • Section 32
  • Seno Medical
  • Sequoia Capital
  • Shinhan Investment
  • Siemens Healthineers
  • SK Telecom
  • Skin Analytics 
  • SkinVision
  • SME Instrument
  • SoftBank Vision Fund
  • Soleria Capital
  • Solis Mammography 
  • S28 Capital 
  • St. John's College
  • Stanford University
  • SymphonyAI 
  • SyndicateRoom
  • T. Rowe Price
  • Tara Health
  • Tavistock Group
  • Telix Pharmaceuticals
  • Temasek 
  • Tempus Labs 
  • Tencent
  • The Jagen Group
  • The Netherlands Cancer Institute 
  • The NorthCap University
  • TheraPanacea
  • Therapixel
  • Think.Health
  • Third Rock Ventures
  • Thirona
  • Thorney Investment 
  • TRAF Intercontinental 
  • Tristel
  • Turbine 
  • Tybourne Capital Management
  • National Cancer Institute
  • Innovate UK 
  • Unilabs
  • University of Oxford
  • University of Pittsburgh Medical Center
  • University Radiology 
  • Vaekstfonden
  • Vanedge Capital
  • Vara
  • Varian Medical Systems
  • VentureFounders
  • Verily Life Sciences
  • Vertex Ventures
  • VieCure
  • Atlantic Labs
  • Visiopharm
  • Voima Ventures
  • Volpara Health
  • VoxelCloud
  • Whiterabbit.ai
  • Wondfo Biotech
  • WuXi NextCODE
  • xCures
  • Xingtai People’s Hospital
  • Yizhun Intelligent

Methodology

 

 

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