Artificial Intelligence In Oncology Market 2025-2034: Key Highlights, Growth Dynamics, and Emerging Trends
Discover trends, market shifts, and competitive outlooks for the artificial intelligence in oncology industry through 2025-2034 with The Business Research Company’s reliable data and in-depth research
What Is the Current and Projected Market Size of the Artificial Intelligence In Oncology Market Through 2034?
The market size of artificial intelligence in oncology has seen significant expansion in the past few years. There is an expected increase from $1.78 billion in 2024 to $2.36 billion in 2025, demonstrating a compound annual growth rate (CAGR) of 32.4%. The notable growth during the historical timespan can be credited to the surge in cancer cases, the abundance of data in healthcare, the optimization of clinical trials, the need for individualized medicine, and increased efficiency in radiology and pathology.
The market for artificial intelligence in oncology is predicted to expand exponentially in the coming years, with its size estimated to reach $7.6 billion by 2029, reflecting at a compound annual growth rate (CAGR) of 33.9%. This substantial growth in the forecast period is due to the integration of artificial intelligence with electronic health records (EHR), its increasing use in treatment decision support, patient engagement and education, regulatory support, and standardization. The forecast period will also see major trends like the incorporation of natural language processing (NLP) into healthcare, the application of AI in biomarker discovery, a focus on making AI explanations understandable for clinical adoption, the utilization of AI in managing population health, partnerships between AI developers and healthcare providers, and advancements in predictive analytics.
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Which Primay Drivers Are Accelerating Growth in the Artificial Intelligence In Oncology Market?
The escalating occurrence of cancer is projected to drive the AI in oncology market advancement. Characterized by uncontrolled multiplication and division of abnormal cells, cancer is a complex cluster of diseases. The incorporation of AI into oncology can potentially transform cancer care into a more precise, individualized, and streamlined process. For example, a report by the American Cancer Society Inc., a US-based health non-profit organization, showed that the projected quantity of cancer cases rose from 1.8 million to 1.9 million in 2023. Therefore, the escalating incidence of cancer acts as a growth catalyst for the AI in oncology market. As pharmaceutical companies increasingly adopt cloud-based applications and services, it could further boost the AI in oncology market. The uptake of these cloud technologies is on a steady climb due to their immense advantages. By accessing and analyzing vast amounts of data like genomics, proteomics, metabolomics, transcriptomics, and clinical and epidemiological data using cloud computing, researchers can significantly speed up cancer research for precision medicine. For example, as per AAG IT Services, a UK-based non-government organization, in a 2022 survey, 89% of organizations reportedly employed multi-cloud solutions for their storage and workload purposes. Only 9% relied on a single public cloud, while 2% used a single private cloud. Such a growing reliance on cloud-based applications and services by pharmaceutical firms thus fuels the AI in oncology market.
Which Primary Segments of the Artificial Intelligence In Oncology Market Are Driving Growth and Industry Transformations?
The artificial intelligence in oncology market covered in this report is segmented –
1) By Component: Software Solutions, Hardware, Services
2) By Treatment Type: Chemotherapy, Radiotherapy, Immunotherapy, Other Treatment Types
3) By Cancer Type: Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Other Cancer Types
4) By Application: Cancer Detection, Drug Discovery, Drug Development, Other Applications
5) By End-Users: Hospitals, Diagnostic Centers, Pharmaceutical Companies, Research Institutes, Other End Users
Subsegments:
1) By Software Solutions: Diagnostic Software, Treatment Planning Software, Patient Management Software, Data Analytics Platforms
2) By Hardware: AI-Enabled Imaging Devices, High-Performance Computing Systems, Wearable Health Monitors
3) By Services: Consulting Services, Integration Services, Maintenance And Support Services
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Which Regions Are Key Players in the Growth of the Artificial Intelligence In Oncology Market?
North America was the largest region in the AI in oncology market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the artificial intelligence in oncology market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa
Which Technological Trends Are Reshaping the Artificial Intelligence In Oncology Industry Dynamics?
Key businesses in the AI in oncology market are concentrating on the creation of innovative software, including cloud-based software, to augment their potency and deliver superior solutions. Cloud-based software is pivotal in AI-use in oncology, offering a scalable, flexible, and readily available platform for the processing and interpretation of massive amounts of medical data. For example, Illumina Inc., a biotechnology company based in the U.S., unveiled “Connected Insights,” a novel cloud-based software designed for tertiary analysis in oncology applications in March 2023. This software, aided by the integration of external knowledge bases, simplifies the interpretation and publication of results from various assay kinds, enabling laboratories to broaden their utilization of NGS (Next-Generation Sequencing) and accelerate clinical reporting turnaround times.
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What Parameters Are Used to Define the Artificial Intelligence In Oncology Market?
Artificial intelligence in oncology refers to applying artificial intelligence (AI) technologies and techniques in cancer diagnosis, treatment, research, and patient care. The goal of AI in oncology is to improve outcomes for cancer patients by leveraging computational methods to identify patterns, predict outcomes, and support evidence-based decision-making across the continuum of cancer care.
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