AI in Oncology Market Size, Share and Growth Analysis 2026 to 2035
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Global AI in Oncology Market Size is valued at USD 1.91 Bn in 2025 and is predicted to reach USD 7.31 Bn by the year 2035 at an 14.5% CAGR during the forecast period for 2026 to 2035.
AI in Oncology Market Size, Share & Trends Analysis Distribution by type of cancer (Solid Malignancies, Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor), Type of end users (Hospitals, Pharmaceutical Companies, Research Institutes) and Segment Forecasts, 2026 to 2035
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.
| Report Attribute | Specifications |
| Market Size Value In 2025 | USD 1.91 Bn |
| Revenue Forecast In 2035 | USD 7.31 Bn |
| Growth Rate CAGR | CAGR of 14.5% from 2026 to 2035 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2026 to 2035 |
| Historic Year | 2022 to 2024 |
| Forecast Year | 2026 to 2035 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type of Cancer, By Type of End Users and By Region |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| 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 |
| Competitive Landscape | Berg, CancerCenter.AI, Concert AI, GE Healthcare, IBM Watson Health, iCAD, JLK Inspection, Median Technologies, Path AI, Roche Diagnostics, Other Prominent Players |
| Customization Scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Geographic competitive landscape. |
| Pricing and Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.