AI in Oncology Market Size is valued at 1.2 Bn in 2023 and is predicted to reach 4.5 Bn by the year 2031 at an 18.8% CAGR during the forecast period for 2024-2031.
The field of oncology is experiencing rapid advancements with the integration of artificial intelligence (AI), which employs advanced algorithms and machine learning techniques to enhance cancer diagnosis, treatment, and patient care. AI's significance in oncology lies in its capacity to sift through extensive medical data, discern intricate patterns, and deliver tailored treatment suggestions. This technological innovation holds promise in revolutionizing cancer management by enhancing diagnostic accuracy, treatment efficacy, and overall healthcare efficiency.
One of the primary driving factors in the AI in oncology market is the rising global incidence of cancer. According to the World Health Organization, nearly 20 million new cancer cases are documented worldwide annually, with projections indicating a steep rise in the future. The surge in demand for precise and prompt cancer detection, alongside the necessity for personalized treatment strategies, is fueling the uptake of AI in oncology. This transformative technology stands poised to streamline clinical processes, curtail healthcare expenditures, and elevate patient outcomes, thereby serving as a pivotal tool in combating cancer.
The AI in the Oncology market is segmented based on type of cancer and by type of end users. By type of cancer the market is segmented into solid malignancies, breast cancer, lung cancer, prostate cancer, colorectal cancer, brain tumor, others. By type of end users market is categorized into hospitals, pharmaceutical companies, research institutes, others
The breast cancer segment stands at the forefront of driving the AI in oncology market, given its rank as the most diagnosed cancer among U.S. women and its substantial global incidence, with over 2.3 million new cases reported in 2020 alone. This high prevalence underscores the urgent need for accurate and timely diagnosis and treatment, a demand effectively met by AI technologies. Through leveraging AI algorithms for early detection, personalized treatment planning, and analysis of medical imaging data, the breast cancer segment serves as a key driver in propelling the growth and innovation within the AI in oncology market.
The hospital segment in India is witnessing substantial growth due to rising demand for medical services, particularly elective surgeries and specialized care. Hospitals are focusing on specialty areas like orthopedics and oncology, attracting patients seeking tailored treatment. Strategic expansions into Tier-II and Tier-III cities are enabling partnerships with smaller hospitals, providing wider access to advanced care. Despite price adjustments, smaller hospitals remain cost-effective options. Government initiatives to improve healthcare infrastructure further bolster this growth trajectory, making the hospital segment the primary driver of the healthcare industry in India.
North America's high incidence of cancer, notably in the United States, underscores the critical need for accurate diagnosis and treatment, a demand that AI technologies are well-equipped to address. Government support through reimbursement policies democratizes healthcare access, fostering the growth of the AI in oncology market. Leveraging a well-established digital infrastructure, particularly in the United States, enables seamless integration of AI solutions into clinical workflows, thereby enhancing patient care and outcomes in the region.
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Report Attribute |
Specifications |
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Market Size Value In 2022 |
USD 1.2 Bn |
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Revenue Forecast In 2031 |
USD 4.5 Bn |
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Growth Rate CAGR |
CAGR of 18.8 % from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type of Cancer, By Type of End Users and By Region |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; Southeast Asia |
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Competitive Landscape |
Berg, CancerCenter.AI, Concert AI, GE Healthcare, IBM Watson Health, iCAD, JLK Inspection, Median Technologies, Path AI, Roche Diagnostics, Other Prominent Players |
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Customization Scope |
Free customization report with the procurement of the report, Modifications to the regional and segment scope. Geographic competitive landscape. |
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Pricing and Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Oncology Market Snapshot
Chapter 4. Global AI in Oncology Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type of Cancer Estimates & Trend Analysis
5.1. by Type of Cancer & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type of Cancer:
5.2.1. Solid Malignancies
5.2.2. Breast Cancer
5.2.3. Lung Cancer
5.2.4. Prostate Cancer
5.2.5. Colorectal Cancer
5.2.6. Brain Tumor
5.2.7. Others
Chapter 6. Market Segmentation 2: by End-users Estimates & Trend Analysis
6.1. by End-users & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End-users:
6.2.1. Hospitals
6.2.2. Pharmaceutical Companies
6.2.3. Research Institutes
6.2.4. Others
Chapter 7. AI in Oncology Market Segmentation 3: Regional Estimates & Trend Analysis
7.1. North America
7.1.1. North America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by Type of Cancer, 2024-2031
7.1.2. North America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2024-2031
7.1.3. North America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
7.2. Europe
7.2.1. Europe AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by Type of Cancer, 2024-2031
7.2.2. Europe AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2024-2031
7.2.3. Europe AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
7.3. Asia Pacific
7.3.1. Asia Pacific AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by Type of Cancer, 2024-2031
7.3.2. Asia Pacific AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2024-2031
7.3.3. Asia Pacific AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
7.4. Latin America
7.4.1. Latin America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by Type of Cancer, 2024-2031
7.4.2. Latin America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2024-2031
7.4.3. Latin America AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
7.5. Middle East & Africa
7.5.1. Middle East & Africa AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by Type of Cancer, 2024-2031
7.5.2. Middle East & Africa AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by End-users, 2024-2031
7.5.3. Middle East & Africa AI in Oncology Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
Chapter 8. Competitive Landscape
8.1. Major Mergers and Acquisitions/Strategic Alliances
8.2. Company Profiles
8.2.1. Berg
8.2.2. CancerCenter.AI
8.2.3. Concert AI
8.2.4. GE Healthcare
8.2.5. IBM Watson Health
8.2.6. iCAD
8.2.7. JLK Inspection
8.2.8. Median Technologies
8.2.9. Path AI
8.2.10. Roche Diagnostic
8.2.11. Other Prominent Players
AI in Oncology Market by Type of Cancer -
AI in Oncology Market by Type of End Users -
AI in Oncology Market by Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
