The global artificial intelligence in cancer diagnostics market was valued at USD 1.74 Billion in 2025, primarily driven by exponential data proliferation coupled with scalable cloud and edge computing infrastructure, and continuous advancements in machine learning/deep learning technologies that enable automation and intelligent decision‑making across the globe. The market is anticipated to grow at a CAGR of 22.40% during the forecast period of 2026-2035 to achieve a value of USD 13.13 Billion by 2035.
Base Year
Historical Period
Forecast Period
AI excels at detecting subtle imaging and genomic patterns, surpassing traditional methods in early cancer detection.
FDA clearances (e.g., Ibex Prostate Detect) and clinical partnerships are accelerating acceptance, thereby, boosting market growth.
Asia–Pacific offers high growth potential; North America leads in adoption due to mature infrastructure and regulatory support.
Compound Annual Growth Rate
22.4%
Value in USD Billion
2026-2035
*this image is indicative*
The global market for artificial intelligence (AI) in cancer diagnostics is experiencing substantial growth, driven by the increasing demand for early detection and improved accuracy in diagnosis. AI is being integrated into various aspects of cancer diagnosis, including medical imaging, pathology, and genomics, to enhance speed, efficiency, and precision. AI algorithms are being used to analyze medical images like mammograms, CT scans, and MRIs to detect and characterize tumors, potentially reducing the workload on radiologists and improving early detection rates.
Rising Cancer Incidence and Focus on Early Detection
AI-driven tools are transforming cancer diagnostics. For instance, in digital pathology, a PubMed meta-analysis of 48 studies (exceeding 152,000 whole-slide images) reported 96.3% sensitivity and 93.3% specificity. This indicates immense accuracy. For hepatocellular carcinoma detection, AI methods have 84–85% sensitivity and 84–92% specificity, far greater than clinicians with around 70% sensitivity. In lung cancer CT screening, pooled sensitivity and specificity were both 87%, with AUC of 0.93. A meta-analysis of 36 studies revealed that AI reduced radiologists’ reading time by about 27.2%, reading volume by 44.5%, and pre-screening workload by 61.7% while improving breast cancer detection by 20% in real-world NHS pilots.
Major trends in the market include rising adoption of AI in medical imaging and diagnostics, application of AI to streamline workflow significantly.
AI Algorithms are being Trained to Analyze Medical Images (like X-rays, MRIs, and mammograms)
AI algorithms have transformed medical imaging and pathology, detecting cancer earlier and more accurately. A PubMed‑based meta‑analysis on lung cancer screening (150,721 scans) reported pooled sensitivity of 94.6% and specificity of 93.6%, vindicating AI’s diagnostic potential. Another review revealed tumor metastasis detection yielded 82% sensitivity and 84% specificity, with AI exceeding the capabilities of clinicians (89% vs. 72% sensitivity). In oral and head‑neck cancers, AI’s sensitivity rose up to 88%–100% with its specificity rising to 80%–94%. AI‑augmented mammogram screenings show a 20% increase in cancer detection, with large NHS trials underway.
Leveraging AI for Efficient Workflows and Accuracy to Boost Market Growth
Routine tasks like image triage, report drafting, and workflow prioritization can be automated with AI, improving the efficiency of oncology treatment. There are 33 PubMed‑indexed studies that show AI reduced clinicians’ image-reading time, with 13 studies noting statistically. At one U.S. health network, AI reduced follow‑up manual workload from 5 hours per week to 15 minutes, an efficiency gain by 95 %. Northwell Health’s iNav AI reduced time from diagnosis to treatment for pancreatic cancer by 50%. Additionally, NHS adoption of Derm AI for melanoma checks (99.9 % rule‑out accuracy) freed dermatologists to deal with severe cases alone.
Breast Cancer to Lead the Market Share by Cancer Type
Breast cancer held the largest market share. This was followed by lung cancer, colorectal cancer, and prostate cancer. Breast cancer led the market due to its high prevalence and the significant demand for early detection, with AI technologies, such as advanced imaging systems and predictive algorithms, playing a crucial role in improving the accuracy of mammograms, biopsies, and risk assessments, thus enhancing patient outcomes.
North America leads the regional segment as it has a well-established healthcare infrastructure, EHR systems, and deep and wide AI integration enabling rapid deployment. High patient volumes and rich datasets make robust AI training and validation possible. Industry collaborates with academia often with strong ties between universities, research institutes, and companies enable fundamental innovation. Proactive FDA approvals and guidelines for AI/ML-based diagnostics have made safe rollouts possible.
The key features of the market report comprise patent analysis, grants analysis, funding and investment analysis, and strategic initiatives by the leading players. The major companies in the market are as follows:
Aidoc (founded in 2016, Tel Aviv) develops the aiOS™ AI Care Platform for medical imaging, with over 30 AI-powered tools (17+ FDA‑cleared). Deployed in 1,500+ hospitals globally, it accelerates critical findings detection, reducing turnaround times by 30–50%. Backed by USD 250 million funding (Series A–D), its solutions analyze millions of scans monthly.
iCAD, founded in 1984 and based in Nashua, New Hampshire, develops AI-driven cancer detection and treatment solutions. Its flagship ProFound Breast Health Suite offers FDA-cleared tools for breast cancer detection, density assessment, and risk evaluation, used in over 50 countries and analyzing 40M+ mammograms. The company also markets the Xoft electronic brachytherapy system. iCAD engages in key partnerships (e.g., RamSoft, Koios), backed by robust clinical validation and global regulatory approvals.
Microsoft is advancing cancer diagnostics through AI-powered initiatives like Paige Alba (built on Azure AI with multimodal foundation models), Virchow2/G (analyzing over 3 million pathology slides), and the Prov‑GigaPath model suite for radiology/pathology integration. Projects like InnerEye also accelerate treatment planning by auto-contouring tumors up to 13× faster. These tools, developed alongside partners like Paige and Providence, aim to enhance diagnostic accuracy, reduce clinician workload, and improve oncology workflows via Azure’s scalable infrastructure.
CancerCenter.ai (founded 2017, Wrocław, Poland) offers a web-based AI platform for digital pathology and radiology, including modules like PathoCam and PathoViewer, enabling image capture, annotation, and AI-powered cancer detection of Gleason score, Ki‑67, metastasis, and more. Backed by GBP 0.5–1M in grants and accelerator support (EIT Health, OVHcloud), it serves pathologists and radiologists across Europe with cloud or on-premises deployment.
*Please note that this is only a partial list; the complete list of key players is available in the full report. Additionally, the list of key players can be customized to better suit your needs.*
The market report offers a detailed analysis of the market based on the following segments:
Market Breakup by Offerings
Market Breakup by Cancer Type
Market Breakup by End-User
Market Breakup by Region
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*While we strive to always give you current and accurate information, the numbers depicted on the website are indicative and may differ from the actual numbers in the main report. At Expert Market Research, we aim to bring you the latest insights and trends in the market. Using our analyses and forecasts, stakeholders can understand the market dynamics, navigate challenges, and capitalize on opportunities to make data-driven strategic decisions.*
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| REPORT FEATURES | DETAILS |
| Base Year | 2025 |
| Historical Period | 2019-2025 |
| Forecast Period | 2026-2035 |
| Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment:
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| Breakup by Offerings |
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| Breakup by Cancer Type |
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| Breakup by End-User |
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| Breakup by Region |
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| Market Dynamics |
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| Supplier Landscape |
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| Companies Covered |
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