AI In Oncology Market Size & Share Analysis - Growth Trends And Forecast (2025 - 2030)

Global AI In Oncology Market Trends and Insights

Growing Cancer Burden Worldwide

New cancer diagnoses are projected to swell by more than 12 million cases annually by 2050, a trend most acute in lower-income countries that lack specialist capacity^{[2]International Agency for Research on Cancer, “Global Cancer Statistics 2024,” iarc.who.int}. Rising incidence magnifies the appeal of lightweight AI tools that operate on commodity laptops, enabling radiologists to triage images rapidly and spot tumours that conventionally slip through busy reading rooms. Global spend on oncology therapeutics and supportive care hit USD 223 billion in 2023 and is on course to top USD 409 billion by 2028, prompting payers to reward technologies that can shave costs through earlier detection. Early-stage AI systems such as National Taiwan University Hospital’s PANCREASaver, which detects sub-2 cm pancreatic lesions with 86.4% accuracy, illustrate how algorithmic innovation can redirect care pathways toward prevention.

Expanding Precision Medicine Programs

AI has become the analytical engine of precision oncology, sifting whole-genome sequencing, RNA expression and digital pathology images to craft tailored regimens. The National Comprehensive Cancer Network’s endorsement of the ArteraAI Prostate Test—backed by randomised trials and level 1B evidence—has legitimised algorithmic prognostics in mainstream guidelines^{[3]National Comprehensive Cancer Network, “NCCN Adds ArteraAI Prostate Test,” nccn.org}. In Europe, the €28 million Thera4Care consortium is establishing pan-EU protocols that link imaging, genomics and treatment planning across 29 institutions, demonstrating how concerted funding can speed translational adoption. These programmes heighten demand for interoperable software frameworks that insert AI outputs directly into tumour-board workflows, trimming iteration cycles between sequencing results and treatment initiation.

Integration of AI With Medical Imaging Modalities

Radiology remains the gateway for clinical AI, accounting for more than three quarters of FDA-authorised algorithms. Devices such as CLAIRITY BREAST, the first tool cleared to forecast five-year breast-cancer risk from routine mammograms, show how AI bolsters incumbent hardware without necessitating new scanners. Siemens Healthineers has embedded seventy-plus AI applications—trained on 1.4 billion studies—into its imaging portfolio, giving providers turnkey access to advanced analytics at the console. Meanwhile, ultra-compact models from academic labs now achieve 92% lung-nodule accuracy on ordinary laptops using fewer than 70 training cases, a leap that widens global reach.

Accelerated Approvals of AI-Based Oncology Devices

Regulators are dismantling procedural bottlenecks. The US FDA intends to deploy AI-assisted dossier review systems by mid-2025, compressing clearance times for machine-learning devices. Breakthrough designation has become a fast lane for oncology tools such as Roche’s VENTANA TROP2 companion diagnostic for non-small-cell lung cancer, which marries immunohistochemistry with digital pathology. Europe’s Medical Device Regulation is likewise aligning AI technical dossiers around transparency and post-market surveillance, allowing firms to leverage CE marks as stepping-stones to multi-region launches.

High Implementation Costs and ROI Uncertainty

Deploying enterprise-grade oncology AI often costs mid-sized cancer centres more than USD 1 million once specialised GPUs, data-integration bridges and staff training are tallied. Management teams struggle to balance these outlays against invisible savings from avoided late-stage treatments, especially because only 15 prospective studies between 2013-2023 have provided real-world outcome data for oncology AI. Hardware risk compounds the challenge: rapid algorithmic efficiency gains can make dedicated inference chips obsolete within two equipment cycles. Consequently, smaller providers favour pay-per-use cloud models but still face temporary workflow slowdowns during integration, lengthening their payback horizon.

Stringent Data Privacy and Security Regulations

Frameworks such as Europe’s GDPR oblige developers to embed privacy-by-design functions—data minimisation, encryption at rest and audit trails—into every pipeline. These safeguards extend development timelines by 12-18 months and raise costs for multi-centre studies that must harmonise consent forms across jurisdictions. Only 14.5% of FDA-authorised algorithms report race or ethnicity data, reflecting the difficulty of assembling diverse training sets under privacy constraints. Upcoming explainability mandates will require providers to show clinicians how black-box neural networks arrive at decisions, further stretching development budgets.

Segment Analysis

By Component: Software Dominance Drives Market Evolution

Software Solutions accounted for a 64.34% AI in oncology market share in 2024, underscoring hospitals’ preference for subscription-based algorithms that install on existing PACS viewers and radiation-planning consoles. The Services segment, projected to post a 37.54% CAGR to 2030, shows that many institutions are outsourcing data curation, algorithm tuning and post-deployment monitoring to specialised vendors rather than hiring in-house machine-learning engineers. ConcertAI’s Patient360™ projects illustrate the shift: more than 1,000 projects completed and 72% of revenue sourced from recurring managed services. Hardware, meanwhile, remains a niche purchase linked to ultra-high-resolution digital-pathology scanners or on-premise GPU clusters; buyers hesitate because successive neural-network generations run on ever-cheaper chips, threatening asset obsolescence.

In the medium term, value will migrate toward “platform” vendors that bundle data-management middleware, algorithm marketplaces and regulatory documentation templates. Such ecosystems lower integration friction and compress validation timelines, making them attractive to community hospitals that lack specialised IT departments. By 2030, software subscription fees and managed-service retainers together are forecast to out-earn hardware sales by more than 4:1, cementing software’s structural primacy in the AI in oncology market.

Note: Segment shares of all individual segments available upon report purchase

By Cancer Type: Brain Tumor Innovation Accelerates Growth

Breast Cancer retained the largest slice of 2024 revenue at 28.23%, buoyed by nationwide mammography programmes and regulatory acceptance of risk-stratification AI such as CLAIRITY BREAST. Yet Brain Tumor solutions are clocking the sector’s fastest CAGR at 38.01%, propelled by real-time surgical guidance algorithms like FastGlioma that identify residual tumour tissue within 10 seconds. Paediatric glioma recurrence models now reach 89% predictive accuracy through spatio-temporal learning, exemplifying the clinical depth of next-gen algorithms. Lung and Prostate Cancer applications are advancing too: the ArteraAI Prostate Test’s guideline inclusion demonstrates how rigorous evidence unlocks reimbursement, and lightweight lung-nodule classifiers make CT screening feasible in mobile clinics.

Collectively, emerging brain, prostate and lung applications will push the AI in oncology market size for under-served tumour groups from less than USD 400 million in 2025 to more than USD 2 billion in 2030, giving vendors an incentive to widen disease coverage. Vendors that master small-data techniques, such as CURATE.AI’s single-patient dose-optimisation engine, could secure first-mover advantage in rare malignancies where traditional big-data methods stall.

Note: Segment shares of all individual segments available upon report purchase

By Treatment Type: Immunotherapy AI Applications Surge

Radiotherapy dominated 2024 revenue, securing 41.45% of the AI in oncology market size through automated contouring, dose planning and respiratory-motion tracking that together cut planning time from hours to minutes. Yet Immunotherapy-focused AI shows a 37.76% CAGR to 2030 as checkpoint inhibitors and CAR-T treatments demand precise biomarker selection and toxicity forecasting. PERception, for instance, analyses single-cell RNA-seq to predict responses to 44 FDA-approved drugs within minutes, slashing trial screening costs. Chemotherapy and targeted-therapy tools are also evolving: AI-generated molecules entering Phase 1 trials exhibit 80-90% success, double historical norms, suggesting a coming wave of algorithm-designed drugs.

As reimbursement agencies increasingly link payment to biomarker-validated outcomes, software that pinpoints responders will capture a premium. Integration with electronic health records will allow real-time adjustment of dosing schedules, enhancing tolerability and extending progression-free survival.

By Application: Drug Discovery Innovation Drives Expansion

Cancer Detection applications supplied 43.23% of 2024 revenues, reflecting two decades of imaging-AI refinement. Drug Discovery, though, will be the headline growth story, racing at a 38.65% CAGR as hyperscale compute and quantum-accelerated pipelines screen hundreds of millions of molecules against in-silico tumour targets. The Stargate programme’s first USD 100 billion tranche to build AI-optimised data centres signals unprecedented capital commitments to in-silico oncology research. Large-language-model-driven clinical-trial matching already achieves 93.3% protocol-level and 88% patient-level accuracy, hinting at future fully digital trial recruitment that shrinks timelines and costs.

Beyond discovery and development, decision-support dashboards that integrate radiology, lab results and genomics onto a single timeline will shift AI value toward longitudinal patient management. Vendors able to wrap explainable-AI layers around complex recommendations will differentiate as regulators tighten transparency rules.

Geography Analysis

North America held 44.73% of 2024 revenue, underpinned by the world’s most mature approval environment, broad reimbursement of digital pathology and a dense network of AI-first oncology start-ups. The FDA’s rolling guidance updates give US vendors clarity on real-time learning systems, encouraging continuous-update algorithms that improve post-market. Mega-deals such as GE HealthCare’s seven-year pact with Sutter Health to blanket 300 facilities with AI-enabled imaging reinforce a virtuous cycle of clinical data generation and product refinement.

Asia-Pacific is the velocity leader with a 36.54% regional CAGR expected between 2025-2030. South Korea’s national AI-health strategy, China’s Healthy China 2030 plan and Singapore’s secure-data-sandbox regulations collectively speed clinical pilots. Nearly 600 health-AI start-ups now operate across Australia, China, Japan and Singapore, funnelling local datasets into disease-specific models attuned to regional genetics and care protocols. National Taiwan University Hospital’s PANCREASaver underscores how indigenous innovation can secure both domestic deployment and US regulatory recognition.

Europe continues to prioritise cross-border research networks and ethical AI. The €28 million Thera4Care project, running across 29 sites, typifies the continent’s collaborative template that pairs algorithm trials with standard-setting for explainability and data stewardship. While GDPR adds compliance overhead, the unified Medical Device Regulation shortens multinational launch sequencing once CE approval is obtained. Emerging regions—Middle East & Africa and South America—are still nascent but show accelerating interest as cloud connectivity widens. Pilot programmes with the World Health Organization that port ultra-compact lung-cancer detectors onto mobile X-ray vans illustrate the adaptability of contemporary AI stacks to infrastructure-light settings.