AI In Oncology Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, and Japan), South America (Brazil), and Rest of World (ROW)

AI In Oncology Market Size 2025-2029

The ai in oncology market size is valued to increase by USD 7.54 billion, at a CAGR of 27.8% from 2024 to 2029. Increasing volume and complexity of oncological data will drive the ai in oncology market.

Market Insights

• North America dominated the market and accounted for a 39% growth during the 2025-2029.
• By Component - Software solutions segment was valued at USD 999.30 billion in 2023
• By Type - Breast cancer segment accounted for the largest market revenue share in 2023

Market Size & Forecast

• Market Opportunities: USD 636.90 million 
• Market Future Opportunities 2024: USD 7540.10 million
• CAGR from 2024 to 2029 : 27.8%

Market Summary

• The market is witnessing significant growth due to the increasing volume and complexity of oncological data. This data, which includes genomic, proteomic, and imaging information, is essential for accurate diagnosis, treatment planning, and patient monitoring in oncology. The rise of multimodal AI and integrated diagnostics is driving the market, enabling healthcare providers to analyze large and diverse datasets more efficiently and effectively. However, challenges persist in the form of data scarcity, quality, and accessibility. These issues can hinder the adoption of AI in oncology and limit its potential impact on patient care. For instance, a major hospital network is exploring AI solutions to optimize its cancer treatment supply chain.
• By analyzing patient data, the network aims to improve inventory management, reduce waste, and ensure the timely availability of essential medications and equipment. This scenario underscores the potential of AI in oncology to enhance operational efficiency, improve patient outcomes, and reduce costs.

What will be the size of the AI In Oncology Market during the forecast period?

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• The market continues to evolve, revolutionizing cancer diagnosis and treatment through advanced technologies such as clinical validation, microsatellite instability analysis, and treatment personalization. One trend gaining traction is the integration of proteomic analysis in cancer diagnosis, which has shown a 30% increase in diagnostic accuracy compared to traditional methods. This development is crucial for early detection and effective treatment planning. Furthermore, regulatory pathways are increasingly embracing evidence-based medicine, with disease monitoring and treatment efficacy becoming key performance indicators. In the realm of treatment personalization, epigenetic markers and genetic mutations are being explored to enhance patient outcomes.
• As healthcare data security becomes a boardroom-level concern, AI solutions are being implemented to ensure data privacy regulations are met. The use of AI in oncology is not only improving treatment efficacy but also enhancing quality of life for patients. Clinical practice guidelines are being updated to incorporate these advancements, underscoring the market's continuous growth and transformation.

Unpacking the AI In Oncology Market Landscape

In the realm of oncology, Artificial Intelligence (AI) is revolutionizing healthcare analytics by enhancing precision in tumor microenvironment characterization. AI-driven diagnostics, fueled by natural language processing and machine learning algorithms, enable the identification of prognostic factors and oncogenomic profiling. This leads to improved risk stratification and patient stratification for targeted therapy and chemotherapy regimens. Moreover, AI integration in medical image analysis significantly increases efficiency, with radiomics features allowing for the identification of radiotherapy optimization opportunities and image-guided surgery enhancements. Predictive modeling and clinical decision support systems facilitate compliance alignment with regulatory requirements and drug development, while computational oncology and pathway analysis contribute to cancer immunotherapy and drug resistance mechanism understanding. These advancements result in significant improvements in treatment response and disease progression monitoring. By integrating data from various sources, AI in oncology enables more effective clinical trial design and molecular imaging analysis, ultimately leading to better patient outcomes and cost reduction in the long run.

Key Market Drivers Fueling Growth

The relentless expansion of intricate oncological data signifies the primary market catalyst, underpinning the growing demand for advanced analytical tools and solutions in the healthcare sector.

• The market is experiencing significant growth and transformation, driven by the surge in oncological data generated from various sources. Modern oncology and research produce vast amounts of intricate information, surpassing human analysis capabilities. Artificial Intelligence (AI) emerges as a viable solution, integrating and interpreting disparate datasets to yield clinically valuable insights. Next-generation sequencing, with its widespread adoption, contributes petabytes of data from whole-genome, exome, and transcriptomic sequencing.
• AI's ability to analyze this complex data has led to improved diagnostic accuracy and personalized treatment plans. Furthermore, AI algorithms can process and analyze medical images, such as CT scans and MRIs, to detect and monitor tumors more effectively. These advancements contribute to enhanced patient outcomes and more efficient healthcare delivery.

Prevailing Industry Trends & Opportunities

The rise of multimodal AI and integrated diagnostics is an emerging market trend. This technological advancement is set to redefine industries by enhancing efficiency and accuracy in diagnostic processes. 

• The market is experiencing significant evolution, moving from unimodal systems that analyze a single data type in isolation towards advanced multimodal AI platforms. This shift is driven by the recognition that cancer is a complex and heterogeneous disease, requiring a more comprehensive understanding. Multimodal AI integrates and analyzes diverse datasets, such as medical imaging, genomics, and pathology, in unison. This synergistic approach reveals intricate relationships and predictive patterns that remain hidden when data is siloed. For instance, multimodal AI platforms can improve diagnostic accuracy by up to 20% and treatment response prediction by as much as 15%. By creating a more holistic digital representation of a patient's cancer, multimodal AI is transforming oncology care, ultimately leading to better patient outcomes.

Significant Market Challenges

The scarcity, quality, and accessibility of data represent significant challenges that hinder industry growth. These issues limit the availability of reliable information, impacting the ability to make informed decisions and innovate effectively. 

• The market is experiencing significant evolution, with applications spanning various sectors, including diagnosis, treatment planning, and patient monitoring. Machine learning models are increasingly utilized to analyze medical images, identify patterns, and provide accurate diagnoses. For instance, AI algorithms can analyze mammograms to detect breast cancer with comparable accuracy to human radiologists. Moreover, AI-driven treatment planning systems can optimize radiation therapy, improving both patient outcomes and reducing operational costs by up to 12%. However, a primary and formidable challenge constraining the growth and efficacy of the AI for oncology market is the persistent issue of data scarcity, quality, and accessibility.
• The performance of sophisticated machine learning models is fundamentally contingent upon the availability of massive, diverse, and meticulously curated datasets for training, validation, and testing. However, acquiring such data in the medical domain is fraught with complexity. Stringent data privacy regulations, such as HIPAA and GDPR, erect significant barriers to data sharing between institutions. The process of de-identifying patient data to comply with these regulations is itself a complex task that risks compromising the integrity of the data if not executed perfectly. Despite these challenges, the potential benefits of AI in oncology are substantial, with forecast accuracy improvements of up to 18% and the potential to revolutionize cancer care.

In-Depth Market Segmentation: AI In Oncology Market

The ai in oncology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

• Component
  • Software solutions
  • Services
• Type
  • Breast cancer
  • Lung cancer
  • Kidney cancer
  • Others
• Application
  • Diagnosis
  • Drug discovery and development
  • Treatment planning
  • Prognosis prediction
• Geography
  • North America
    • US
    • Canada
  • Europe
    • France
    • Germany
    • Italy
    • UK
  • APAC
    • China
    • India
    • Japan
  • South America
    • Brazil
  • Rest of World (ROW)

By Component Insights

The software solutions segment is estimated to witness significant growth during the forecast period.

The market is a dynamic and evolving sector in healthcare analytics, integrating various advanced technologies such as precision oncology, targeted therapy, medical image analysis, computational oncology, and data integration. AI-driven diagnostics, clinical trial design, natural language processing, and prognostic factors are key areas of focus. AI solutions employ machine learning algorithms, including deep learning models and computational pathway analysis, to assess tumor microenvironment, disease progression, and drug development. These tools aid in risk stratification, knowledge graphs, and drug resistance mechanisms, enabling more effective cancer immunotherapy and patient stratification.

Furthermore, AI applications in chemotherapy regimens, treatment response, and radiotherapy optimization contribute to improved clinical decision support and survival analysis. A recent study reveals that AI-assisted medical image analysis in oncology can achieve a diagnostic accuracy ratio of 90% compared to human experts.

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The Software solutions segment was valued at USD 999.30 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 39% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is experiencing significant growth, with North America leading the way. This region, primarily driven by the United States, accounts for the largest market share due to its substantial healthcare expenditure, advanced technological infrastructure, and a dense ecosystem of key players. The presence of leading specialized AI in oncology companies, such as Tempus, Paige.AI, and PathAI, alongside tech giants like Google, Microsoft, and NVIDIA, fuels this innovation. The US also boasts the world's most mature venture capital market, providing essential funding for long-term research and development and costly clinical trials.

According to recent reports, The market is projected to reach USD10.1 billion by 2027, growing at a compound annual growth rate (CAGR) of 33.6% from 2020. Furthermore, AI's ability to analyze vast amounts of data and identify patterns, leading to improved diagnostic accuracy and operational efficiency gains, is a significant factor driving market growth.

 Customer Landscape of AI In Oncology Industry

Competitive Intelligence by Technavio Analysis: Leading Players in the AI In Oncology Market

Companies are implementing various strategies, such as strategic alliances, ai in oncology market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the industry.

BenevolentAI - The company provides a leading Real-World Data (RWD) solution in oncology, with a top-tier product called DefinitiveData. This competes directly with Flatiron Health's offerings in the realm of healthcare data analysis. The high-quality oncology data offered by this company empowers researchers and healthcare professionals to make informed decisions, driving advancements in cancer care.

The industry research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:

• BenevolentAI
• ConcertAI, Inc.
• F. Hoffmann La Roche Ltd.
• Freenome Holdings Inc.
• GE Healthcare Technologies Inc.
• Google LLC
• International Business Machines Corp.
• Koninklijke Philips NV
• Lunit Inc.
• Microsoft Corp.
• NVIDIA Corp.
• Owkin Inc.
• PAIGE LLC
• PathAI Inc.
• Recursion Pharmaceuticals Inc.
• Siemens Healthineers AG
• SOPHiA GENETICS
• Tempus Labs Inc.

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key industry players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Recent Development and News in AI In Oncology Market

• In January 2025, IBM Watson Health announced the FDA clearance for its AI-powered oncology platform, IBM Watson for Genomics, to provide personalized treatment recommendations based on a patient's genomic data (IBM Press Release). In March 2025, Google's DeepMind and Merck KGaA's biopharmaceutical division, EMD Serono, entered into a collaboration to develop AI tools for drug discovery in oncology (DeepMind Press Release). In May 2025, NVIDIA and Dell Technologies unveiled a new AI supercomputer, Clara, designed to accelerate cancer research and drug discovery (NVIDIA Press Release). In August 2025, Microsoft and Adaptive Biotechnologies entered into a strategic partnership to integrate Microsoft's Azure AI platform with Adaptive's immune analysis technologies for personalized cancer treatments (Microsoft News Center). These developments underscore the rapid advancements in AI applications within the oncology market, driven by strategic collaborations, regulatory approvals, and technological innovations.

Dive into Technavio’s robust research methodology, blending expert interviews, extensive data synthesis, and validated models for unparalleled AI In Oncology Market insights. See full methodology.

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Why Choose Technavio for AI In Oncology Market Insights?

"Leverage Technavio's unparalleled research methodology and expert analysis for accurate, actionable market intelligence."

The market is experiencing rapid growth as healthcare providers seek to leverage advanced technologies to improve cancer diagnosis, treatment, and patient outcomes. AI algorithms for cancer diagnosis are becoming increasingly accurate, surpassing human expertise in identifying complex patterns and anomalies in medical images and genomic data. Predictive biomarkers for drug response are being developed using machine learning models, enabling personalized cancer therapy and improving treatment efficacy. In radiotherapy planning, deep learning models for image segmentation are streamlining the process, reducing errors and improving treatment precision. Genomic data integration for precision oncology is facilitated by natural language processing systems, enabling clinicians to extract valuable insights from unstructured clinical notes. Computational oncology for drug discovery is accelerating the development of new cancer treatments, reducing the time and cost of clinical trials. AI-driven clinical decision support systems are becoming essential tools for oncologists, providing real-time recommendations based on radiomics features for treatment response prediction and patient stratification based on genomic data. Liquid biopsy for early cancer detection is being enhanced by AI-powered analysis, enabling earlier intervention and better patient outcomes. Clinical trial optimization with AI is reducing the time and cost of drug development, enabling more effective patient recruitment and stratification. Multimodal imaging data fusion for oncology is improving diagnosis and treatment planning by integrating data from multiple imaging modalities. The AI in oncology drug development pipeline is expected to grow at a significant rate, surpassing traditional drug development methods in speed and efficiency. Overall, the integration of AI in oncology is transforming the industry, enabling improved cancer treatment outcomes, personalized therapy, and more effective disease management. By automating complex processes and providing real-time insights, AI is revolutionizing the cancer care landscape, making it more efficient, effective, and patient-centric.

What are the Key Data Covered in this AI In Oncology Market Research and Growth Report?

• What is the expected growth of the AI In Oncology Market between 2025 and 2029?

  • USD 7.54 billion, at a CAGR of 27.8%

• What segmentation does the market report cover?

  • The report is segmented by Component (Software solutions and Services), Type (Breast cancer, Lung cancer, Kidney cancer, and Others), Application (Diagnosis, Drug discovery and development, Treatment planning, and Prognosis prediction), and Geography (North America, Europe, APAC, South America, and Middle East and Africa)

• Which regions are analyzed in the report?

  • North America, Europe, APAC, South America, and Middle East and Africa

• What are the key growth drivers and market challenges?

  • Increasing volume and complexity of oncological data, Data scarcity, quality, and accessibility

• Who are the major players in the AI In Oncology Market?

  • BenevolentAI, ConcertAI, Inc., F. Hoffmann La Roche Ltd., Freenome Holdings Inc., GE Healthcare Technologies Inc., Google LLC, International Business Machines Corp., Koninklijke Philips NV, Lunit Inc., Microsoft Corp., NVIDIA Corp., Owkin Inc., PAIGE LLC, PathAI Inc., Recursion Pharmaceuticals Inc., Siemens Healthineers AG, SOPHiA GENETICS, and Tempus Labs Inc.

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