# Ai In Oncology Market

> AI in Oncology Market Research Report: Size, Share, Trend Analysis By Applications (Diagnosis, Treatment Planning, Drug Discovery, Prognosis), By Technology (Machine Learning, Deep Learning, Computer Vision, Natural Language Processing), By Cancer Type (Lung Cancer, Breast Cancer, Colorectal Cancer, Prostate Cancer, Lymphoma), By Data Type (Imaging Data, Genomic Data, Clinical Data, Patient Reported Data), By Deployment Model (Cloud-based, On-premises, Hybrid) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Growth Outlook & Industry Forecast 2026 To 2035

- **Forecast Period:** 2026-2035
- **CAGR:** 31.9%
- **2025:** USD 2.13 Billion
- **2035:** USD 34.35 Billion
- **Key Players:** Siemens Healthineers AG, GE HealthCare, Koninklijke Philips N.V., Elekta AB, Tempus AI, Inc., NVIDIA Corp., IBM Corp., Flatiron Health

**Report ID:** MRFR/HC/27956-HCR · **Pages:** 128 · **Author:** Rahul Gotadki & Vikita Thakur · **Last Updated:** November 06, 2026

**URL:** https://www.marketresearchfuture.com/reports/ai-in-oncology-market-29682

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## Market Summary

## Ai In Oncology Market Summary

The AI in Oncology Market was valued at USD 2.13 billion in 2025 and is projected to reach USD 2.85 billion in 2026, climbing to USD 34.35 billion by 2035 at a CAGR of 31.9% over 2026–2035. Two catalysts anchor that trajectory. The U.S. National Cancer Institute received about USD 7.22 billion in FY2025 appropriations, a growing share of which funds data commons and imaging informatics [7], while the UK committed GBP 21 million through its AI Diagnostic Fund to deploy imaging algorithms across NHS trusts [6]. Demand underneath is relentless: global new cancer cases reached roughly 20 million in 2022 and are expected to exceed 35 million by 2050 [1].

The rate of change in clinical workflows is the fastest it has been since the transition to digital imaging. Deep-learning triage, auto-segmentation within treatment planning systems, and foundation models trained on millions of pathology slides are replacing rule-based computer-aided detection, manual contouring, and paper tumor boards. The U.S. FDA has authorized over 1,000 AI-enabled devices, with radiology comprising approximately seventy-five percent. The MASAI randomized trial in Sweden demonstrated that AI-supported mammography reading reduced the labor of radiologists by 44% without eliciting false positives [17].

North America is the market leader in 2025, with a 41.2% revenue share, which is based on reimbursement pathways and concentrated cancer-center networks. Driven by national screening programs in China, India, and South Korea, the Asia-Pacific region is the fastest-growing, with a compound annual growth rate (CAGR) of 32.7%. Europe is the second-ranked region, and the EU AI Act provides vendors with a unified compliance framework for 27 member states [5]. Vendors that provide clinical evidence in conjunction with seamless workflow integration will be rewarded by the AI in Oncology Market over the next decade.

## Key Report Takeaways

### • By Component

- Software Solutions held 59.4% of AI in Oncology Market revenue in 2025, reflecting subscription algorithms that run on existing PACS and planning consoles.
- Services is the fastest-expanding component, advancing at a 33.6% CAGR as hospitals outsource data curation and model monitoring.

### • By Cancer Type

- Breast Cancer accounted for 26.2% of 2025 revenue on the back of organised mammography screening.
- Brain Tumor solutions are forecast to grow at a 34.3% CAGR, propelled by intraoperative tumour-detection models.

### • By Treatment Type

- Radiotherapy captured 38.3% of 2025 revenue through auto-contouring and adaptive planning.
- Immunotherapy-focused tools will expand at a 33.4% CAGR as checkpoint and cell therapies demand response prediction.

### • By Application

- Cancer Detection represented 40.0% of the AI in Oncology Market in 2025, the most mature use case
- Drug Discovery is the fastest-growing application at a 34.6% CAGR

### • By Region

- North America led with a 41.2% share in 2025
- Asia-Pacific is set to post a 32.7% CAGR, the highest of any region
- Europe generated USD 0.61 billion in 2025, supported by harmonised AI regulation

## Market Size and Forecast (2026–2035)

Estimates for the AI in Oncology Market combine vendor revenue mapping across 60+ software, hardware and services providers with installed-base analysis of radiotherapy planning systems, digital-pathology scanners and imaging AI deployments. Historical values were reconciled against company filings, regulatory authorisation counts and hospital procurement data, while forecast values reflect adoption curves by cancer type, reimbursement timelines and regional screening capacity. Primary interviews with oncologists, radiologists, medical physicists and procurement heads validated each assumption.

## Market Drivers

## Driver Impact Analysis

| Driver | ~% Impact on CAGR | Geographic Relevance | Impact Timeline | Ref |
| --- | --- | --- | --- | --- |
| Rising global cancer incidence and screening volumes | +6.8% | Global | Long term (≥4 yrs) | [1] |
| Expanding regulatory authorisations for AI devices | +5.4% | North America, Europe | Medium term (2–4 yrs) |   |
| Radiologist and pathologist workforce shortages | +4.9% | Global, acute in Europe and Asia-Pacific | Short term (≤2 yrs) | [18] |
| Guideline inclusion and reimbursement for AI biomarkers | +4.2% | North America | Medium term (2–4 yrs) | [10] |
| Public funding for cancer data infrastructure | +3.6% | North America, Europe | Long term (≥4 yrs) | [6][7] |
| AI-enabled oncology drug discovery investment | +3.9% | North America, Asia-Pacific | Long term (≥4 yrs) | [16] |
| GPU compute and foundation model availability | +3.1% | Global | Short term (≤2 yrs) | [14] |

### Rising Global Cancer Incidence and Screening Volumes

Cancer volume is outpacing clinical capacity, and that gap is the single largest demand lever for the AI in Oncology Market. WHO's cancer agency counted about 20 million new cases and 9.7 million deaths in 2022, and projects a 77% rise in new cases by 2050 [1]. Each additional screening round multiplies imaging and pathology reads, and health systems cannot hire fast enough to keep pace. AI triage that clears normal studies and flags suspicious ones offers the only scalable way to absorb this load without lengthening diagnostic waits.

### Expanding Regulatory Authorisations for AI Devices

Regulators have built predictable routes to market. The FDA's AI-enabled device list passed 1,000 entries, and its December 2024 final guidance on Predetermined Change Control Plans lets vendors update models without fresh submissions. That certainty shortens commercial timelines and encourages investors to fund oncology-specific algorithms rather than generic imaging tools. Breakthrough designations for prognostic tests further compress review periods for high-impact products.

### Radiologist and Pathologist Workforce Shortages

Staffing gaps convert AI from a nice-to-have into an operational necessity. The Royal College of Radiologists reported a 30% shortfall of clinical radiologists in the UK in 2023, forecast to reach 40% by 2028 [18]. Pathology departments face similar strain as molecular testing grows. Hospitals now justify AI purchases on throughput and turnaround metrics, which shortens procurement cycles and makes multi-year enterprise licences easier to approve.

### Guideline Inclusion and Reimbursement for AI Biomarkers

Clinical guidelines are starting to name specific algorithms. The ArteraAI Prostate Test entered NCCN guidelines in 2024 as a tool to personalise therapy decisions for localised prostate cancer [10]. Guideline inclusion unlocks payer coverage and gives oncologists confidence to order AI-derived results. Each new listing signals to payers that algorithmic prognostics belong in standard care, encouraging similar evidence programmes across breast, lung and colorectal indications.

### Public Funding for Cancer Data Infrastructure

Government money is building the data foundations that algorithms need. NCI's FY2025 budget of roughly USD 7.22 billion supports the Cancer Research Data Commons and imaging archives [7], while the UK's GBP 21 million AI Diagnostic Fund financed deployments across 64 NHS trusts [6]. These investments lower data acquisition costs for developers and create reference sites that accelerate adoption in neighbouring institutions.

### AI-Enabled Oncology Drug Discovery Investment

Pharmaceutical sponsors are treating AI as core R&D infrastructure. A 2024 analysis found AI-discovered molecules achieved 80–90% Phase 1 success rates, well above historical averages of about 40–65% [16]. Oncology dominates these pipelines because tumour genomics provide rich, labelled targets. Licensing deals between drug developers and AI platform companies now routinely carry upfront payments in the tens of millions of dollars.

### GPU Compute and Foundation Model Availability

Cheaper compute and pre-trained models lower the barrier to building oncology tools. NVIDIA's healthcare platforms and cloud partnerships give developers access to GPU clusters without capital outlay [14], and pathology foundation models trained on more than one million slides now underpin multiple tumour-detection products. Smaller vendors can fine-tune these models with modest local datasets, widening the competitive field and speeding product release cycles.

## Restraints

## Restraints Impact Analysis

The negative impact figures below indicate the directional drag each restraint places on AI in Oncology Market growth. Like the driver estimates, they overlap and should not be summed against the headline CAGR.

| Restraint | ~% Impact on CAGR | Geographic Relevance | Impact Timeline | Ref |
| --- | --- | --- | --- | --- |
| Data privacy and cybersecurity risk | −3.4% | Global | Short term (≤2 yrs) | [19] |
| Fragmented reimbursement for AI software | −3.1% | Global, acute outside the U.S. | Medium term (2–4 yrs) | [20] |
| Limited generalisability and algorithmic bias | −2.6% | Global | Medium term (2–4 yrs) | [21] |
| Integration cost with legacy hospital IT | −2.2% | Europe, emerging markets | Short term (≤2 yrs) | [22] |
| Heavier compliance burden under new AI rules | −1.8% | Europe | Long term (≥4 yrs) | [5] |

### Data Privacy and Cybersecurity Risk

Oncology AI depends on large volumes of identifiable imaging, genomic, and clinical data. According to the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR), the 2024 Change Healthcare cyberattack affected approximately 192.7 million individuals, making it the largest U.S. healthcare data breach on record. Consequently, hospital security teams now subject cloud-hosted algorithms to extensive risk reviews, frequently forcing vendors into costly on-premises deployments that delay implementation.

### Fragmented Reimbursement for AI Software

Payment remains uneven. Outside a handful of CPT codes and new-technology add-on payments in the U.S., most health systems fund AI from operating budgets [20]. European payers rarely reimburse algorithm use separately, forcing vendors to prove savings through throughput gains. Without dedicated payment, smaller hospitals delay purchases, keeping adoption concentrated in academic centres.

### Limited Generalisability and Algorithmic Bias

Models trained on narrow datasets can underperform on different scanners, staining protocols or populations. Systematic reviews have found that many published oncology AI studies lack external validation and under-represent non-white patients [21]. Clinicians remain cautious until local validation is complete, which adds months to deployment and forces vendors to fund costly multi-site studies.

### Integration Cost with Legacy Hospital IT

To add value, algorithms must seamlessly stream output directly into existing PACS, LIS, and Electronic Health Record (EHR) platforms. However, U.S. Centers for Medicare & Medicaid Services (CMS) and health IT benchmark data show that average hospital IT expenditures remain low, accounting for only 2.3% of total hospital operating budgets. High custom integration costs for legacy infrastructure often exceed initial AI license fees, hindering adoption across public hospitals.

### Heavier Compliance Burden under New AI Rules

The European Union’s regulatory framework imposes strict requirements on healthcare algorithms. Under the EU AI Act (Regulation 2024/1689), AI tools integrated into medical devices certified under the EU Medical Device Regulation (MDR 2017/745) face high-risk classification mandates. Manufacturers must implement rigorous risk management, technical data governance, and post-market surveillance plans ahead of the phased 2027–2028 high-risk enforcement deadlines, placing heavy financial burdens on early-stage developers.

## Opportunities

## Ai In Oncology Market Opportunities

### National Screening Programmes in Emerging Economies

High disease burdens in developing regions highlight the urgent need for scalable diagnostic tools. According to the Indian Council of Medical Research (ICMR) National Cancer Registry Programme, India recorded an estimated 1,461,427 new cancer cases in 2022. AI tools deployed on mobile units and cloud infrastructure provide a scalable model to overcome clinical shortages across district hospitals. Vendors operating on a pay-per-study basis alongside public health missions can secure high volume.

### Data Monetisation and Real-World Evidence Platforms

De-identified oncology datasets represent valuable assets for healthcare innovation. According to its SEC disclosures, Tempus AI reported total revenues of $693 million in 2024, backed by $200.7 million in Q4 2024 alone, generated through multimodal data licensing and discovery services provided to pharmaceutical sponsors. Health systems structuring linked imaging and genomic datasets can generate recurring licensing revenue to fund clinical algorithm development.

### AI-Driven Clinical Trial Matching and Design

Fewer than 10% of adult cancer patients enrol in clinical trials, and recruitment delays add months to development timelines [8]. Language-model tools that read unstructured records and match patients to protocols can raise enrolment and diversify cohorts. FDA encouragement of decentralised and pragmatic trial designs further opens the door for algorithmic recruitment services.

### Intraoperative and Real-Time Surgical Guidance

FastGlioma, described in Nature in 2024, identifies residual brain tumour tissue in about 10 seconds during surgery with high accuracy. Similar tools for margin assessment in breast and head-and-neck surgery could reduce re-operation rates, which run as high as 20–30% in breast-conserving surgery. Vendors bundling these tools with optical imaging hardware can create a defensible niche.

### Adaptive Radiotherapy Automation

Adaptive radiotherapy requires rapid real-time re-contouring to ensure treatment accuracy while the patient is on the table. Automated AI segmentation reduces contouring time from hours to minutes, enabling adaptive workflows beyond specialized academic centers. Equipment manufacturers integrating these software models directly into linear accelerator consoles access service revenue streams across an installed base of more than 15,000 radiotherapy machines registered worldwide in the International Atomic Energy Agency (IAEA) DIRAC database.

## Future Outlook

## Ai In Oncology Market Future Outlook

### Multimodal and Agentic Clinical Decision Support

Decision support will move from single-image reads to models that combine radiology, pathology, genomics and clinical notes. The AI in Oncology Market will see tumour-board assistants that summarise patient history and suggest guideline-concordant options, with clinicians retaining sign-off. Physician acceptance is rising: 66% of U.S. physicians reported using AI in 2024, up from 38% a year earlier [15]. Oncology, with its dense data and complex decisions, is a natural early adopter.

### Platform Economics and Algorithm Marketplaces

Hospitals do not want 30 separate contracts for 30 algorithms, and the AI in Oncology Market is reorganising around that preference. Value will shift toward platforms that bundle data management, validation tooling and third-party algorithms under one licence. Imaging OEMs and cloud providers are already building marketplaces, and the GE HealthCare acquisition of MIM Software in 2024 shows how planning and imaging software is consolidating [13]. Independent vendors will increasingly sell through these channels.

### AI-Native Oncology Drug Pipelines

Drug discovery will be the fastest-moving frontier. AI-designed molecules have shown higher early-stage success rates [16], and oncology accounts for a large share of AI-originated candidates. By the early 2030s, several AI-discovered cancer drugs are expected to reach pivotal trials, shifting value toward platforms that link target discovery, patient stratification and trial design in one workflow.

### Regulation, Trust and Post-Market Monitoring

Rules will tighten and mature together. The FDA's January 2025 draft guidance on AI-enabled device software sets lifecycle expectations covering bias, transparency and performance monitoring, and EU AI Act obligations for high-risk systems take effect by 2027 [5]. Vendors that invest in real-world performance monitoring and explainability will win procurement decisions as hospitals demand evidence that models stay accurate after deployment.

## Segment Insights

## Ai In Oncology Market Segmentation

### By Component

| Segment | Key Metric (2025) | Primary Demand Driver |
| --- | --- | --- |
| Software Solutions | 59.4% share | Subscription algorithms integrated with PACS and planning systems |
| Hardware | USD 0.24 Billion | Digital-pathology scanners and on-premises GPU servers |
| Services | 33.6% CAGR (2026–2035) | Outsourced data curation, validation and model monitoring |

Within the AI in Oncology Market, Software Solutions lead because hospitals prefer subscription models that install on existing imaging viewers and radiotherapy consoles without new capital spend. Services grow fastest as institutions outsource data labelling, local validation and post-deployment monitoring rather than hire scarce machine-learning engineers. Hardware remains a narrower purchase tied to whole-slide scanners and on-premise GPU servers, where buyers worry about rapid obsolescence as each new chip generation lowers inference costs.

### By Cancer Type

| Segment | Key Metric (2025) | Primary Demand Driver |
| --- | --- | --- |
| Breast Cancer | 26.2% share | Organised mammography screening and risk prediction |
| Lung Cancer | USD 0.38 Billion | Low-dose CT screening and nodule triage |
| Prostate Cancer | 13.6% share | AI prognostic tests and MRI-guided biopsy |
| Colorectal Cancer | 31.2% CAGR (2026–2035) | AI-assisted colonoscopy polyp detection |
| Brain Tumor | 34.3% CAGR (2026–2035) | Intraoperative tumour-detection models |
| Other Cancer Types | 22.4% share | Skin, cervical and haematological applications |

For the AI in Oncology Market, Breast Cancer leads on the strength of national mammography programmes and regulatory acceptance of image-based risk models. Brain Tumour tools grow fastest as real-time surgical guidance, such as FastGlioma, shows it can identify residual tumour within seconds. Lung Cancer benefits from expanding low-dose CT screening, while Prostate Cancer gains from guideline-listed prognostic tests. Colorectal Cancer adoption rises with computer-aided polyp detection, and Other Cancer Types include skin and cervical screening tools.

### By Treatment Type

| Segment | Key Metric (2025) | Primary Demand Driver |
| --- | --- | --- |
| Radiotherapy | 38.3% share | Auto-contouring, dose planning and adaptive treatment |
| Chemotherapy | 24.7% share | Toxicity prediction and dosing optimisation |
| Immunotherapy | 33.4% CAGR (2026–2035) | Response prediction and biomarker selection |
| Other Treatment Types | USD 0.31 Billion | Surgery, targeted therapy and hormone therapy support |

Across the AI in Oncology Market, Radiotherapy holds the largest share because auto-contouring and automated planning cut preparation time from hours to minutes and fit directly into vendor consoles. Immunotherapy tools post the fastest growth as checkpoint inhibitors and CAR-T therapies require accurate responder selection and toxicity forecasting. Chemotherapy applications focus on dose optimisation and adverse-event prediction, and Other Treatment Types cover surgical planning and targeted therapy matching.

### By Application

| Segment | Key Metric (2025) | Primary Demand Driver |
| --- | --- | --- |
| Cancer Detection | 40.0% share | Screening triage across imaging and pathology |
| Drug Discovery | 34.6% CAGR (2026–2035) | In-silico target identification and molecule design |
| Drug Development | USD 0.45 Billion | Trial matching, patient stratification and biomarker discovery |
| Other Applications | 17.1% share | Treatment planning support, prognosis and survivorship care |

In the AI in Oncology Market, Cancer Detection dominates because imaging and pathology triage tools have the longest regulatory track record and clearest return on investment. Drug Discovery grows fastest as pharmaceutical sponsors use generative models to screen vast chemical libraries against tumour targets. Drug Development applications, including language-model trial matching and patient stratification, shorten recruitment and improve cohort diversity. Other Applications cover prognosis, treatment planning support and survivorship monitoring.

## Regional Market Share Analysis

## Regional Market Share Analysis

| Region | Key Metric (2025) | Primary Investment Themes |
| --- | --- | --- |
| North America | 41.2% share | Reimbursed AI biomarkers, radiotherapy automation, pharma R&D |
| Europe | USD 0.61 Billion | Breast screening AI, EU AI Act compliance, national data platforms |
| Asia-Pacific | 32.7% CAGR (2026–2035) | Population screening, domestic AI vendors, hospital digitisation |
| South America | 4.6% share | Private oncology networks, cloud-based imaging AI |
| Middle East & Africa | USD 0.08 Billion | Sovereign health digitisation, new cancer centres |
| Total | USD 2.13 Billion | – |

North America remains the anchor of the AI in Oncology Market, while Asia-Pacific records the fastest growth on the strength of government screening programmes and domestic AI champions. Europe's position is shaped by harmonised regulation, and South America and the Middle East & Africa are early-stage but investing.

### North America

| Country | Key Metric (2025) | Key Driver |
| --- | --- | --- |
| United States | 86.5% share of region | FDA authorisations and guideline-linked reimbursement |
| Canada | USD 0.08 Billion | Provincial screening programmes and data platforms |
| Mexico | 33.9% CAGR (2026–2035) | Private hospital investment in imaging AI |

The United States accounts for most North American spending in the AI in Oncology Market thanks to FDA clearances, NCCN guideline listings and large integrated delivery networks that buy enterprise AI licences. NCI's Cancer Moonshot programmes and ARPA-H funding continue to finance imaging and data projects [7][8]. Canada benefits from provincial cancer agencies running centralised screening programmes, while Mexico's growth reflects private hospital chains adopting cloud-based mammography and lung-screening tools.

### Europe

| Country | Key Metric (2025) | Key Driver |
| --- | --- | --- |
| Germany | 24.8% share of region | Hospital digitisation funding |
| United Kingdom | USD 0.13 Billion | NHS AI Diagnostic Fund and screening trials |
| France | 15.2% share of region | National health data hub and cancer plan |
| Italy | 31.4% CAGR (2026–2035) | Regional imaging network upgrades |
| Spain | USD 0.05 Billion | Regional lung and breast screening pilots |
| Rest of Europe | 20.1% share of region | Nordic registries and Benelux digital pathology |

Germany leads regional spending through its Hospital Future Act digitisation funds, which allocated about EUR 4.3 billion to hospital IT. The United Kingdom stands out for public deployment: the NHS AI Diagnostic Fund and the EDITH breast screening trial, involving roughly 700,000 women, are testing AI at national scale [6]. France and Italy are expanding regional imaging networks, while Spain's regional health services pilot lung screening AI. The EU AI Act sets common rules for every member state [5].

### Asia-Pacific

| Country | Key Metric (2025) | Key Driver |
| --- | --- | --- |
| China | 38.6% share of region | NMPA Class III approvals and hospital scale |
| Japan | USD 0.11 Billion | Ageing population and PMDA pathway |
| India | 36.2% CAGR (2026–2035) | Public-private screening partnerships |
| Australia | USD 0.04 Billion | National lung cancer screening programme |
| South Korea | 9.8% share of region | Domestic AI vendors and digital hospitals |
| Rest of Asia-Pacific | 31.9% CAGR (2026–2035) | Singapore and ASEAN digital health strategies |

China leads regional spending as the NMPA has approved dozens of Class III AI imaging products and tertiary hospitals deploy lung-nodule and pathology tools at scale. Japan's ageing population and PMDA approval pathway support steady adoption. India posts the fastest country growth as public-private screening partnerships expand [9]. South Korea hosts globally competitive vendors such as Lunit, and Australia's national lung cancer screening programme launched in July 2025.

### South America

| Country | Key Metric (2025) | Key Driver |
| --- | --- | --- |
| Brazil | 58.4% share of region | Private oncology networks and ANVISA SaMD rules |
| Argentina | USD 0.02 Billion | Private hospital imaging upgrades |
| Rest of South America | 28.7% CAGR (2026–2035) | Telehealth and donor-funded screening |

Brazil dominates regional demand through large private oncology networks and diagnostic chains that deploy cloud-hosted imaging AI. ANVISA's software-as-a-medical-device rules have given vendors a clearer registration path. Argentina's private hospitals are adopting AI for mammography and radiotherapy planning, while the rest of the region depends on donor-supported and cross-border telehealth projects.

### Middle East & Africa

| Country | Key Metric (2025) | Key Driver |
| --- | --- | --- |
| GCC | 34.1% CAGR (2026–2035) | Vision 2030 programmes and new cancer centres |
| South Africa | USD 0.02 Billion | Private hospital group investment |
| Rest of Middle East & Africa | 31.5% share of region | Development-partner screening pilots |

GCC states drive regional growth through sovereign health digitisation strategies, including Saudi Arabia's Health Sector Transformation Program under Vision 2030 and the UAE's national AI strategy. New comprehensive cancer centres are being designed with AI-ready infrastructure. South Africa leads sub-Saharan adoption through private hospital groups, while the rest of the region relies on pilot projects funded by development partners.

## Competitive Benchmarking

## Competitive Benchmarking

The AI in Oncology Market is moderately concentrated, with an estimated Herfindahl-Hirschman Index of about 1,100 and the top five vendors holding roughly 38–44% of revenue. Imaging and radiotherapy OEMs dominate through installed bases and bundled software, while specialist AI firms compete on clinical evidence in pathology, prognostics and real-world data. Acquisitions continue to consolidate the field as OEMs buy proven algorithms rather than build them.

| Company | Est. Revenue Share Range | Key Offerings | Strategic Positioning |
| --- | --- | --- | --- |
| Siemens Healthineers AG | ~10–13% | AI-Rad Companion, AI-based organ contouring, syngo.via | Imaging installed base and cross-modality AI platform |
| Varian Medical Systems (a Siemens Healthineers company) | ~8–11% | Ethos adaptive therapy, AI-assisted planning | Radiotherapy leader embedding AI in treatment delivery |
| GE HealthCare | ~7–10% | MIM Software, Edison platform, imaging AI | Consolidating planning and imaging software |
| Koninklijke Philips N.V. | ~5–8% | IntelliSpace Precision Medicine, digital pathology | Enterprise informatics and tumour-board tools |
| Elekta AB | ~4–7% | ProKnow, AI auto-contouring, Unity MR-Linac | Adaptive radiotherapy with AI planning |
| Tempus AI, Inc. | ~4–7% | Genomic sequencing, data licensing, AI diagnostics | Multimodal data platform for clinicians and pharma |
| NVIDIA Corp. | ~3–6% | Clara, MONAI, BioNeMo | Compute and model infrastructure for developers |
| IBM Corp. | ~2–4% | watsonx models, consulting and data services | Enterprise AI and life-sciences partnerships |
| Flatiron Health | ~2–4% | Oncology EHR and real-world evidence | Real-world data for regulatory and pharma use |
| PathAI | ~1–3% | AISight, AI-powered pathology algorithms | Pathology workflow and biopharma services |
| ConcertAI | ~1–3% | Patient360, trial design and real-world data | Managed oncology data services for pharma |
| Lunit Inc. | ~1–3% | Lunit INSIGHT, Lunit SCOPE | Screening and biomarker AI with global reach |

## Recent News & Developments

## Recent News & Developments

- European Union (August 2024): The EU AI Act entered into force, classifying most medical AI as high-risk and setting phased obligations through 2027 that shape product roadmaps for European launches [5]
- GE HealthCare (April 2024): Completed its acquisition of MIM Software, adding radiation oncology planning and imaging analytics to its portfolio [13]
- Lunit (May 2024): Completed its acquisition of Volpara Health, combining AI cancer detection with breast density and risk software across mammography workflows [23]
- Tempus AI (June 2024): Listed on Nasdaq, raising capital to expand its AI diagnostics and data licensing businesses [12]
- NCCN (2024): Added the ArteraAI Prostate Test to prostate cancer guidelines, creating a template for guideline inclusion of AI prognostics [10]
- University of Michigan and UCSF researchers (November 2024): Published FastGlioma in Nature, showing near-real-time intraoperative detection of residual brain tumour
- U.S. FDA (December 2024): Issued final guidance on Predetermined Change Control Plans for AI-enabled devices, allowing pre-approved model updates
- U.S. FDA (January 2025): Released draft guidance on lifecycle management of AI-enabled device software functions, covering bias, transparency and monitoring

## Report Scope

| Parameter | Details |
| --- | --- |
| Market Scope | Covers AI software solutions, hardware and services used for cancer detection, drug discovery, drug development and treatment support across hospitals, diagnostic laboratories, cancer centres and life-sciences companies |
| Study Period | 2021–2035 |
| Base Year | 2025 |
| Historical Data | 2021–2024 |
| Forecast Period | 2026–2035 |
| Market Size in 2025 | USD 2.13 Billion |
| Market Size in 2035 | USD 34.35 Billion |
| CAGR (2026–2035) | 31.9% |
| Fastest Growing Segments | Drug Discovery (34.6% CAGR); Brain Tumor (34.3% CAGR); Services (33.6% CAGR); Immunotherapy (33.4% CAGR) |
| Regions Covered | North America, Europe, South America, Asia Pacific, Middle East and Africa and ROW |
| Companies Profiled | Siemens Healthineers AG, Varian Medical Systems, GE HealthCare, Koninklijke Philips N.V., Elekta AB, Tempus AI Inc., NVIDIA Corp., IBM Corp., Flatiron Health, PathAI, ConcertAI, Lunit Inc. |
| Valuation Currency | USD Billion |
| Methodology | 350+ primary expert interviews; bottom-up market sizing; top-down validation; data triangulation; scenario-based forecasting; real-time project tracking |

## Frequently Asked Questions

**Q: How should hospitals evaluate vendors in the AI in Oncology Market?**
A: Prioritise external validation on data resembling your own scanners and patients. Ask for peer-reviewed multi-site results and a documented post-market monitoring plan before signing a multi-year licence [21].

**Q: Is cloud or on-premise deployment better for oncology AI?**
A: Cloud suits AI in Oncology Market buyers without GPU infrastructure and speeds model updates. On-premise deployment fits institutions with strict data-residency rules, though it raises upfront hardware and maintenance costs [19].

**Q: What pricing models are common in the AI in Oncology Market?**
A: Annual subscriptions and per-study fees dominate. Enterprise platform licences covering multiple algorithms are growing because they simplify procurement and lower the cost per tool [13].

**Q: How do AI prognostic tests gain payer coverage?**
A: AI in Oncology Market coverage decisions usually follow guideline inclusion backed by validation in completed randomised trial cohorts. Once a test is listed by a body such as NCCN, payers have a clear clinical basis for reimbursement [10].

**Q: Which emerging use cases could reshape the AI in Oncology Market?**
A: Intraoperative tumour detection and AI-guided adaptive radiotherapy are the most promising. Both act during treatment itself, creating value beyond diagnosis and opening new hardware-software bundles [11].

**Q: What should investors watch when assessing oncology AI start-ups?**
A: Look for regulatory clearances, guideline listings and recurring contracts rather than pilot counts. Start-ups with proprietary linked datasets hold stronger defensibility than those relying on public data [12].

**Q: How does the EU AI Act affect non-European vendors?**
A: Any vendor selling medical AI in the EU must meet high-risk obligations, including data governance and human oversight. Compliance applies regardless of where the company is headquartered [5].


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