세계의 종양학용 인공지능(AI) 시장 : 제품 유형, 기술, 암 유형, 용도, 최종 용도별 - 예측(2025-2030년)

The Artificial Intelligence in Oncology Market was valued at USD 23.25 billion in 2024 and is projected to grow to USD 27.67 billion in 2025, with a CAGR of 20.23%, reaching USD 70.26 billion by 2030.

Artificial Intelligence has emerged as a pivotal force in reshaping the landscape of oncology, facilitating breakthroughs that redefine clinical workflows and patient care. In an era where technology converges with medicine, the integration of advanced algorithms and data analytics paves the way for more precise diagnostics, personalized treatment plans, and improved operational efficiencies in healthcare environments. This report examines how sophisticated AI solutions are not only augmenting diagnostic accuracy but are also playing an instrumental role in accelerating drug discovery and optimizing treatment planning.

The evolution of AI in oncology is marked by significant technological advancements that enhance decision-making and patient outcome predictability. From image analysis to predictive modeling, these developments underscore a trend toward more dynamic and responsive cancer care. Furthermore, the healthcare landscape is witnessing a paradigm shift as interdisciplinary teams, combining the prowess of clinicians and technologists, leverage AI to interpret complex data patterns and deliver actionable insights. The convergence of medical expertise with state-of-the-art technology lays a robust foundation for addressing emerging challenges and unlocking new opportunities within the sector.

Transformative Shifts in the Oncology Landscape Driven by AI

Artificial Intelligence is not just an incremental innovation; it represents a radical transformation in the field of oncology. A series of groundbreaking shifts are redefining how healthcare providers approach cancer diagnosis, treatment planning, and patient management. The integration of machine learning, advanced image processing, and predictive analytics has ushered in a new era where traditional methods are reimagined for greater precision and efficiency.

Innovative applications such as automated diagnostic imaging and real-time surgical assistance are turning theoretical possibilities into everyday clinical realities. This transformative journey is supported by continuous improvements in algorithms, data management technologies, and sensor systems, which collectively bolster the reliability and scalability of AI-driven applications. Furthermore, the trend towards cloud-based and on-premise solutions offers flexibility to healthcare providers, allowing them to adapt seamlessly to evolving regulatory requirements and patient expectations.

The impact of these shifts is evident in the reduction of diagnostic errors, faster decision-making processes, and a marked improvement in the overall patient experience. By seamlessly integrating AI with existing healthcare infrastructures, industry stakeholders are setting new benchmarks that promise to revolutionize patient care and redefine operational standards across the oncology spectrum.

Key Segmentation Insights: A Detailed Examination of Market Dynamics

An in-depth analysis of the market reveals a complex and tiered segmentation structure that is driving innovation and fueling growth in the realm of oncology. One layer of segmentation is based on product type, where the market is dissected into hardware, services, and software solutions. Within hardware, significant emphasis is placed on diagnostic imaging systems and robotic surgical systems, while the services category delves into both consulting and implementation services. The software solutions segment further subdivides into cloud-based solutions and on-premise tools, each offering unique advantages to healthcare providers.

The segmentation based on technology further enriches this perspective by analyzing segments such as computer vision, machine learning, natural language processing, and robotic process automation. Computer vision is intricately studied in relation to 3D reconstruction, image recognition, and video analysis, which are critical for precise imaging diagnostics. Machine learning is explored through its subsets of deep learning, supervised learning, and unsupervised learning, while natural language processing covers data annotation, sentiment analysis, and text mining to facilitate more nuanced insights. Advances in robotic process automation underpin innovations in automated workflows, clinical documentation, and process mapping.

Additionally, the market analysis extends to segmentation by cancer type, which comprehensively covers breast cancer, cervical cancer, colorectal cancer, esophageal cancer, liver cancer, lung cancer, skin cancer, stomach (gastric) cancer, and thyroid cancer, providing a granular view of disease-specific trends. Application segmentation further categorizes the market into diagnostics, drug discovery, outcome prediction, personalized medicine, and treatment planning. Each application category opens up subdomains such as imaging analytics, molecular diagnostics, and pathology under diagnostics; clinical trial design, lead discovery, and target identification in drug discovery; and, similarly, specialized focus areas across outcome prediction, personalized medicine, and treatment planning. Finally, the end-use segmentation distinguishes the market along the use in diagnostic laboratories, hospitals, pharmaceutical companies, and research institutions, with further drilling down into genomic testing facilities, pathology labs, varied hospital types, manufacturer classifications, and research institutions including academic and biotech entities.

This layered segmentation not only illustrates the diversity of market dynamics but also signals the potential for tailored AI applications that cater to specific needs across the oncology spectrum. By leveraging these segmented insights, stakeholders can identify niches and opportunities that drive both innovation and operational efficiency while enhancing patient care.

Based on Product Type, market is studied across Hardware, Services, and Software Solutions. The Hardware is further studied across Diagnostic Imaging Systems and Robotic Surgical Systems. The Services is further studied across Consulting Services and Implementation Services. The Software Solutions is further studied across Cloud-Based Solutions and On-Premise Tools.

Based on Technology, market is studied across Computer Vision, Machine Learning, Natural Language Processing, and Robotic Process Automation. The Computer Vision is further studied across 3D Reconstruction, Image Recognition, and Video Analysis. The Machine Learning is further studied across Deep Learning, Supervised Learning, and Unsupervised Learning. The Natural Language Processing is further studied across Data Annotation, Sentiment Analysis, and Text Mining. The Robotic Process Automation is further studied across Automated Workflows, Clinical Documentation, and Process Mapping.

Based on Cancer Type, market is studied across Breast Cancer, Cervical Cancer, Colorectal Cancer, Esophageal Cancer, Liver Cancer, Lung Cancer, Skin Cancer, Stomach (Gastric) Cancer, and Thyroid Cancer.

Based on Application, market is studied across Diagnostics, Drug Discovery, Outcome Prediction, Personalized Medicine, and Treatment Planning. The Diagnostics is further studied across Imaging Analytics, Molecular Diagnostics, Pathology, and Screening. The Drug Discovery is further studied across Clinical Trials Design, Lead Discovery, and Target Identification. The Outcome Prediction is further studied across Complication Prediction, Response Prediction, and Survival Rate Visualization. The Personalized Medicine is further studied across Biomarker Identification, Genomic Data Analysis, and Therapeutic Optimization. The Treatment Planning is further studied across Chemotherapy Planning, Radiation Therapy Planning, and Surgical Planning.

Based on End-Use, market is studied across Diagnostic Laboratories, Hospitals, Pharmaceutical Companies, and Research Institutions. The Diagnostic Laboratories is further studied across Genomic Testing Facilities and Pathology Labs. The Hospitals is further studied across Private Hospitals and Public Hospitals. The Pharmaceutical Companies is further studied across Generic Manufacturers and Innovator Companies. The Research Institutions is further studied across Academic Institutions and Biotech Firms.

Key Regional Insights: Navigating Global Opportunities in Oncology

Regional dynamics play a critical role in shaping the adoption and expansion of AI-driven oncology solutions. The Americas continue to be at the forefront, driven by significant investments in healthcare technology and well-established research ecosystems. In this region, robust regulatory frameworks coupled with a high level of digital infrastructure support the rapid implementation of AI applications, thereby enhancing both diagnostic capabilities and treatment planning.

In contrast, the Europe, Middle East & Africa region presents a diverse mix of opportunities and challenges. European nations have long been pioneers in integrating technological advancements within their healthcare systems, benefiting from collaborative research and stringent regulatory standards. Meanwhile, emerging markets in the Middle East and Africa are rapidly embracing innovative healthcare technologies, leveraging AI to improve access to quality care and bridge the gap in service delivery.

The Asia-Pacific region is marked by dynamic growth, with substantial investments in research and development fueling advancements in oncology. The expanding digital ecosystem, combined with a large patient base and government initiatives towards healthcare modernization, makes Asia-Pacific a fertile ground for implementing advanced AI solutions. These regional insights underscore the importance of tailoring strategies to local market needs while exploiting global opportunities presented by the transformative capabilities of AI in oncology.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Key Company Insights: Industry Leaders Driving Innovation in Oncology

Leading companies are at the heart of the AI revolution in oncology, each contributing to the rapid evolution of technology and clinical practice. The involvement of industry giants and innovative startups is reshaping the competitive landscape. Amazon Web Services, Inc. stands out as a prominent force, offering cloud computing power that underpins many AI solutions. Arterys, Inc. and Berg LLC are making significant strides with advanced analytical tools that integrate seamlessly into clinical workflows.

Bristol-Myers Squibb Company plays a vital role in bridging the gap between pharmaceuticals and AI, while Butterfly Network, Inc. is celebrated for its breakthrough innovations in portable imaging devices. The contributions of F. Hoffmann-La Roche Ltd. and Flatiron Health, Inc. further highlight a commitment to leveraging data for enhanced diagnostic accuracy and treatment personalization. Freenome Holdings, Inc. is at the forefront of early cancer detection, using AI to analyze complex datasets for early intervention.

Major technology providers such as GE Healthcare, Google LLC by Alphabet Inc., and IBM Corporation bring exceptional expertise in data processing and machine learning. Intel Corporation and Koninklijke Philips N.V. are innovating in the hardware space with advanced imaging and diagnostic instruments. Medial EarlySign Ltd., Microsoft Corporation, Nvidia Corporation, and Oncora Medical, Inc. are collectively advancing the field through integrated solutions that combine clinical data with AI insights. Emerging leaders like Paige.AI Inc., PathAI, Inc., Siemens Healthineers AG, Tempus Labs, Inc., Ultromics Limited, Viz.ai, Inc., and Zebra Medical Vision Ltd. also contribute significantly by harnessing novel technologies that promise to transform every aspect of oncology care.

This varied collection of industry players not only underscores the global commitment to advancing cancer care but also hints at the synergies that will continue to drive innovation in AI-driven oncology solutions.

The report delves into recent significant developments in the Artificial Intelligence in Oncology Market, highlighting leading vendors and their innovative profiles. These include Amazon Web Services, Inc., Arterys, Inc., Berg LLC, Bristol-Myers Squibb Company, Butterfly Network, Inc., F. Hoffmann-La Roche Ltd., Flatiron Health, Inc., Freenome Holdings, Inc., GE Healthcare, Google LLC by Alphabet Inc., IBM Corporation, Intel Corporation, Koninklijke Philips N.V., Medial EarlySign Ltd., Microsoft Corporation, Nvidia Corporation, Oncora Medical, Inc., Paige.AI Inc., PathAI, Inc., Siemens Healthineers AG, Tempus Labs, Inc., Ultromics Limited, Viz.ai, Inc., and Zebra Medical Vision Ltd.. Actionable Recommendations for Strategic Leadership in AI-Driven Oncology

Industry leaders are encouraged to adopt a forward-thinking approach that capitalizes on the transformative potential of AI in oncology. It is crucial to align investment strategies with emerging trends in technology, segmentation, and regional market dynamics. Decision-makers should prioritize the integration of robust AI systems that enhance diagnostic accuracy and streamline treatment processes, ensuring that clinical innovations translate into tangible patient benefits.

To secure a competitive edge, organizations must focus on fostering collaborative environments where cross-disciplinary teams can innovate effectively. By investing in strong data infrastructure and scalable AI solutions, healthcare providers can better manage the complexities of modern oncology care. Additionally, tailored strategies that consider regional regulatory frameworks and local market needs are imperative. Leaders must harness insights derived from detailed segmentation analysis-ranging from product type and specific technological advancements to cancer types and end-user scenarios-to ensure that strategic initiatives are both data-driven and contextually relevant.

Moreover, partnerships with leading technology providers and academic institutions should be prioritized to facilitate research and development. This collaboration could drive the refinement of algorithms and promote the standardization of AI applications in clinical settings. Lastly, continuous monitoring of key performance indicators and regular updates to strategic plans will ensure that organizations remain agile in the face of evolving industry trends, thereby transforming challenges into growth opportunities.

Conclusion: Embracing the Future of Oncology with AI Innovation

The synthesis of advanced AI technologies with the intricacies of oncology care represents a landmark shift in the healthcare industry. As this analysis has demonstrated, the evolution of AI in oncology is characterized by transformative shifts in clinical practice, multifaceted market segmentation, dynamic regional influences, and the significant participation of key industry players. These elements together form the backbone of a future where predictive analytics, automated diagnostic systems, and personalized treatment planning are not merely aspirational goals but integral components of everyday clinical practice.

Central to this paradigm shift is the recognition that the convergence of technology, data science, and clinical expertise heralds significant improvements in patient outcomes, operational efficiencies, and overall healthcare delivery. By moving beyond traditional methodologies and embracing innovative AI-driven solutions, the industry is set to redefine standards of care in a rapidly evolving medical landscape.

This comprehensive overview reaffirms the importance of adopting an integrated approach that not only recognizes the current industry capabilities but also anticipates future challenges and opportunities. The continued collaboration between technology innovators and healthcare providers will be critical in steering the oncology sector toward a new era of precision, efficiency, and patient-centric care.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Rising prevalence of cancer cases globally necessitates innovative AI solutions to enhance care
    • 5.1.1.2. Government initiatives and funding for AI research in healthcare drive innovation and adoption in oncology
  • 5.1.2. Restraints
    • 5.1.2.1. High costs associated with AI development and deployment along with interoperability issues
  • 5.1.3. Opportunities
    • 5.1.3.1. Significant partnerships between oncology care provider and AI technology providers
    • 5.1.3.2. Surge in telemedicine and remote monitoring bolsters AI-enabled remote oncology services and care
  • 5.1.4. Challenges
    • 5.1.4.1. Challenges in ensuring fairness and mitigating biases in AI algorithms to provide equitable oncology care
• 5.2. Market Segmentation Analysis
  • 5.2.1. Product Type: Significant development of the software solutions for data management and targeted decision-making
  • 5.2.2. End-Use: Advancements in AI solutions to enhance production efficiencies and quality control measures for generic manufacturers
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Artificial Intelligence in Oncology Market, by Product Type

• 6.1. Introduction
• 6.2. Hardware
  • 6.2.1. Diagnostic Imaging Systems
  • 6.2.2. Robotic Surgical Systems
• 6.3. Services
  • 6.3.1. Consulting Services
  • 6.3.2. Implementation Services
• 6.4. Software Solutions
  • 6.4.1. Cloud-Based Solutions
  • 6.4.2. On-Premise Tools

7. Artificial Intelligence in Oncology Market, by Technology

• 7.1. Introduction
• 7.2. Computer Vision
  • 7.2.1. 3D Reconstruction
  • 7.2.2. Image Recognition
  • 7.2.3. Video Analysis
• 7.3. Machine Learning
  • 7.3.1. Deep Learning
  • 7.3.2. Supervised Learning
  • 7.3.3. Unsupervised Learning
• 7.4. Natural Language Processing
  • 7.4.1. Data Annotation
  • 7.4.2. Sentiment Analysis
  • 7.4.3. Text Mining
• 7.5. Robotic Process Automation
  • 7.5.1. Automated Workflows
  • 7.5.2. Clinical Documentation
  • 7.5.3. Process Mapping

8. Artificial Intelligence in Oncology Market, by Cancer Type

• 8.1. Introduction
• 8.2. Breast Cancer
• 8.3. Cervical Cancer
• 8.4. Colorectal Cancer
• 8.5. Esophageal Cancer
• 8.6. Liver Cancer
• 8.7. Lung Cancer
• 8.8. Skin Cancer
• 8.9. Stomach (Gastric) Cancer
• 8.10. Thyroid Cancer

9. Artificial Intelligence in Oncology Market, by Application

• 9.1. Introduction
• 9.2. Diagnostics
  • 9.2.1. Imaging Analytics
  • 9.2.2. Molecular Diagnostics
  • 9.2.3. Pathology
  • 9.2.4. Screening
• 9.3. Drug Discovery
  • 9.3.1. Clinical Trials Design
  • 9.3.2. Lead Discovery
  • 9.3.3. Target Identification
• 9.4. Outcome Prediction
  • 9.4.1. Complication Prediction
  • 9.4.2. Response Prediction
  • 9.4.3. Survival Rate Visualization
• 9.5. Personalized Medicine
  • 9.5.1. Biomarker Identification
  • 9.5.2. Genomic Data Analysis
  • 9.5.3. Therapeutic Optimization
• 9.6. Treatment Planning
  • 9.6.1. Chemotherapy Planning
  • 9.6.2. Radiation Therapy Planning
  • 9.6.3. Surgical Planning

10. Artificial Intelligence in Oncology Market, by End-Use

• 10.1. Introduction
• 10.2. Diagnostic Laboratories
  • 10.2.1. Genomic Testing Facilities
  • 10.2.2. Pathology Labs
• 10.3. Hospitals
  • 10.3.1. Private Hospitals
  • 10.3.2. Public Hospitals
• 10.4. Pharmaceutical Companies
  • 10.4.1. Generic Manufacturers
  • 10.4.2. Innovator Companies
• 10.5. Research Institutions
  • 10.5.1. Academic Institutions
  • 10.5.2. Biotech Firms

11. Americas Artificial Intelligence in Oncology Market

• 11.1. Introduction
• 11.2. Argentina
• 11.3. Brazil
• 11.4. Canada
• 11.5. Mexico
• 11.6. United States

12. Asia-Pacific Artificial Intelligence in Oncology Market

• 12.1. Introduction
• 12.2. Australia
• 12.3. China
• 12.4. India
• 12.5. Indonesia
• 12.6. Japan
• 12.7. Malaysia
• 12.8. Philippines
• 12.9. Singapore
• 12.10. South Korea
• 12.11. Taiwan
• 12.12. Thailand
• 12.13. Vietnam

13. Europe, Middle East & Africa Artificial Intelligence in Oncology Market

• 13.1. Introduction
• 13.2. Denmark
• 13.3. Egypt
• 13.4. Finland
• 13.5. France
• 13.6. Germany
• 13.7. Israel
• 13.8. Italy
• 13.9. Netherlands
• 13.10. Nigeria
• 13.11. Norway
• 13.12. Poland
• 13.13. Qatar
• 13.14. Russia
• 13.15. Saudi Arabia
• 13.16. South Africa
• 13.17. Spain
• 13.18. Sweden
• 13.19. Switzerland
• 13.20. Turkey
• 13.21. United Arab Emirates
• 13.22. United Kingdom

14. Competitive Landscape

• 14.1. Market Share Analysis, 2024
• 14.2. FPNV Positioning Matrix, 2024
• 14.3. Competitive Scenario Analysis
  • 14.3.1. Health Care Global Enterprises (HCG) collaborates with Accenture to revolutionize AI-driven cancer research and treatment
  • 14.3.2. Strategic partnership between BioAI and Arbele aims to revolutionize cancer treatment with AI-driven biomarker models
  • 14.3.3. Apollo's AI-powered Precision Oncology Centre in Bengaluru revolutionizes cancer diagnosis and care
• 14.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Amazon Web Services, Inc.
• 2. Arterys, Inc.
• 3. Berg LLC
• 4. Bristol-Myers Squibb Company
• 5. Butterfly Network, Inc.
• 6. F. Hoffmann-La Roche Ltd.
• 7. Flatiron Health, Inc.
• 8. Freenome Holdings, Inc.
• 9. GE Healthcare
• 10. Google LLC by Alphabet Inc.
• 11. IBM Corporation
• 12. Intel Corporation
• 13. Koninklijke Philips N.V.
• 14. Medial EarlySign Ltd.
• 15. Microsoft Corporation
• 16. Nvidia Corporation
• 17. Oncora Medical, Inc.
• 18. Paige.AI Inc.
• 19. PathAI, Inc.
• 20. Siemens Healthineers AG
• 21. Tempus Labs, Inc.
• 22. Ultromics Limited
• 23. Viz.ai, Inc.
• 24. Zebra Medical Vision Ltd.