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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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INTRODUCTION 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.

New York, Sept. 30, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "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" - https://www.reportlinker.com/p06323411/?utm_source=GNW
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?

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.
Read the full report: https://www.reportlinker.com/p06323411/?utm_source=GNW

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