AI for Radiology Market by Offering (Software/SaaS, On-Device Software), Function (Screening & Triage, Diagnostic Imaging & Interpretation, Treatment Planning & Intervention Support, Monitoring & Follow-Up, Reporting & Documentation, Workflow Optimization), Modality (Computed Tomography, Magnetic Resonance Imaging, X-Ray, Ultrasound, Mammography), Indication, and End User - Global Forecast to 2036

What is the AI for Radiology Market Size?

The global AI for radiology market was valued at USD 1.41 billion in 2025. The market is expected to reach approximately USD 11.84 billion by 2036 from USD 1.69 billion in 2026, growing at a CAGR of 21.5% from 2026 to 2036. The growth of the overall AI for radiology market is driven by the intensifying global demand for early disease detection and the rapid expansion of automated diagnostic imaging systems across healthcare institutions. As manufacturers seek to integrate more functionality into clinical workflows and address the critical shortage of skilled radiologists, advanced AI-powered imaging solutions have become essential for maintaining diagnostic accuracy and clinical efficiency. The rapid expansion of cloud-based infrastructure and the increasing need for high-performance algorithms capable of analyzing complex imaging data continue to fuel significant growth of this market across all major geographic regions.

Market Highlights: Global AI for Radiology Market

• In terms of revenue, the global AI for radiology market is projected to reach USD 11.84 billion by 2036.
• The market is expected to grow at a CAGR of 21.5% from 2026 to 2036.
• North America dominates the global AI for radiology market with the largest market share in 2026, driven by advanced healthcare infrastructure and the presence of leading technology providers in the United States and Canada.
• Asia-Pacific is expected to witness the fastest growth during the forecast period, supported by aggressive healthcare infrastructure expansion and the rapid adoption of AI-powered diagnostic systems.
• By offering, the software/SaaS segment holds the largest market share in 2026, particularly in supporting scalable deployment and seamless integration with existing healthcare IT infrastructure.
• By modality, the computed tomography segment holds the largest market share in 2026, due to its proven efficacy in supporting diverse diagnostic applications and high imaging volumes.
• By indication, the oncology segment is expected to grow at the fastest CAGR during the forecast period.

Market Overview and Insights

Click here to: Get Free Sample Pages of this Report

AI for radiology systems are critical diagnostic platforms used to provide automated image analysis while allowing for rapid clinical decision-making and enhanced diagnostic precision throughout the imaging workflow. These systems include triage algorithms, detection platforms, and workflow optimization tools, which are designed to withstand high-volume imaging environments and fit into diverse clinical protocols. The market is defined by high-efficiency algorithms such as deep learning models and convolutional neural networks, which significantly enhance diagnostic accuracy and workflow performance in medical imaging applications. These systems are indispensable for healthcare providers seeking to optimize their clinical operations and meet aggressive patient care targets.

The market includes a diverse range of solutions, ranging from simple detection algorithms for basic abnormality screening to complex multimodal AI systems for comprehensive diagnostic support and clinical decision-making. These systems are increasingly integrated with advanced components such as natural language processing and generative AI to provide services such as automated reporting and predictive analytics. The ability to provide accurate, high-speed results while minimizing false positives has made advanced AI for radiology systems the technology of choice for healthcare institutions where diagnostic precision and operational efficiency are paramount.

The global healthcare sector is pushing hard to modernize diagnostic capabilities, aiming to meet radiologist shortage mitigation and enhanced patient outcome targets. This drive has increased the adoption of high-performance AI algorithms, with advanced deep learning techniques helping to stabilize diagnostic yields for ultra-complex imaging interpretation. At the same time, the rapid growth in the imaging volume and chronic disease prevalence is increasing the need for high-reliability, clinically-proven AI-enabled diagnostic solutions.

What are the Key Trends in the AI for Radiology Market?

Integration of Multimodal AI and Generative Foundation Models

Manufacturers across the healthcare industry are rapidly shifting to advanced AI architectures, moving well beyond traditional single-task algorithms toward high-speed, multimodal foundation setups. Siemens Healthineers' latest AI-Rad Companion platforms deliver significantly higher diagnostic precision for complex imaging cases, while GE HealthCare's recent Edison AI installations have enhanced workflow efficiency in clinical trials. The real game-changer comes with generative AI featuring integrated multimodal capabilities that maintain peak performance even in diverse clinical environments with varying imaging protocols. These advancements make comprehensive AI-powered diagnostics practical and cost-effective for everyone from community hospitals to global academic medical centers chasing diagnostic excellence and lower operational overhead.

Innovation in Cloud-Native and Edge Computing Deployment

Innovation in cloud-native and edge computing deployment is rapidly driving the AI for radiology market, as imaging systems become more distributed and interoperable. Equipment suppliers are now designing platforms that combine the processing power of cloud-based infrastructure with the low-latency benefits of edge computing in a single ecosystem, saving valuable computing resources and simplifying deployment logistics. These systems often involve advanced containerization and federated learning technology capable of handling ultra-large imaging datasets without compromising data security or clinical reliability.

At the same time, growing focus on data governance and privacy is pushing manufacturers to develop AI solutions tailored to HIPAA compliance and regulatory standards. These systems help reduce data exposure risks through on-premises processing capabilities and the use of privacy-preserving learning techniques. By combining high-performance diagnostic delivery with robust security performance, these new architectures support both technological advancement and regulatory compliance, strengthening the resilience of the broader healthcare value chain.

Market Summary:

Market Dynamics

Drivers: Radiologist Shortage and Rising Imaging Volumes

A key driver of the AI for radiology market is the rapid movement of the global healthcare industry toward technology-enabled, highly efficient diagnostic workflows. Global demand for faster turnaround times, accurate diagnoses, and reduced radiologist burnout has created significant incentives for the adoption of AI-powered imaging platforms. The trend toward precision medicine and the integration of AI into clinical decision support systems drive healthcare providers toward scalable solutions that AI for radiology can uniquely provide. It is estimated that as imaging volumes continue to increase and radiologist shortages persist through 2036, the need for robust, automated diagnostic solutions increases significantly; therefore, AI algorithms, with their ability to ensure high-speed image analysis and workflow optimization, are considered a crucial enabler of modern radiology department strategies.

Opportunity: Value-Based Care and Personalized Diagnostics

The rapid shift toward value-based care models and personalized diagnostic technologies provides great opportunities for the AI for radiology market. Indeed, the global emphasis on patient outcomes and cost efficiency has created a compelling demand for systems that can enhance diagnostic accuracy and integrate seamlessly into comprehensive care pathways. These applications require high reliability, clinical validation, and the ability to handle diverse patient populations, all attributes that are met with advanced AI-powered imaging solutions. The personalized medicine market is set to expand significantly through 2036, with AI for radiology systems poised for an expanding share as healthcare providers seek to maximize patient outcomes and minimize diagnostic errors. Furthermore, the increasing demand for predictive analytics and risk stratification tools is stimulating demand for advanced AI solutions that provide actionable insights and clinical flexibility.

Offering Insights

Why Does Software/SaaS Lead the Market?

The software/SaaS segment accounts for a significant portion of the overall AI for radiology market in 2026. This is mainly attributed to the scalable deployment model of this technology in supporting rapid implementation and seamless integration within existing healthcare IT environments, such as in large hospital networks and multi-site imaging centers. These systems offer the most comprehensive way to ensure diagnostic consistency across diverse high-volume clinical applications. The enterprise healthcare and cloud computing sectors alone consume a large share of AI radiology software production, with major installations in North America and Europe demonstrating the technology's capability to handle complex integration requirements. However, the on-device software segment maintains a significant share due to the growing need for low-latency processing in time-critical diagnostic applications, particularly in emergency departments and stroke centers.

Modality Insights

How Does the Computed Tomography Segment Dominate?

Based on modality, the computed tomography segment holds the largest share of the overall market in 2026. This is primarily due to the massive volume of CT procedures performed globally and the rigorous performance standards required for modern diagnostic imaging. Current large-scale healthcare facilities are increasingly specifying AI-enhanced CT solutions to ensure compliance with clinical protocols and expectations for faster, more accurate disease detection across diverse patient populations.

The magnetic resonance imaging segment is expected to witness rapid growth during the forecast period. The shift toward integrated diagnostic platforms and the complexity of MRI interpretation workflows are pushing the requirement for advanced AI systems that can handle varied imaging sequences and pathological presentations while ensuring absolute reliability for safety-critical diagnostic applications.

Indication Insights

Why Does Oncology Drive the Fastest Growth?

The oncology segment is expected to witness the fastest growth during the forecast period. This rapid expansion stems from the critical need for early cancer detection and the massive global cancer burden requiring advanced diagnostic capabilities. The increasing emphasis on precision oncology and personalized treatment planning is driving demand for AI systems that can identify subtle imaging biomarkers and predict treatment responses with high accuracy.

However, the neurology segment commands substantial market share in 2026, fueled by expanding applications in stroke detection, traumatic brain injury assessment, and neurodegenerative disease monitoring. Manufacturers face mounting pressure to optimize AI algorithms for time-critical neurological emergencies, where AI-enabled triage systems provide life-saving interventions through rapid detection and automated clinical team notification.

Regional Insights

How is North America Maintaining Dominance in the Global AI for Radiology Market?

North America holds the largest share of the global AI for radiology market in 2026. The largest share of this region is primarily attributed to the advanced healthcare infrastructure and the presence of leading AI technology developers, particularly in the United States. The United States alone accounts for a significant portion of global AI radiology adoption, with its position as a hub for healthcare innovation and regulatory approval driving sustained growth. The presence of leading manufacturers like GE HealthCare and Siemens Healthineers, along with specialized AI companies such as Aidoc and Viz.ai, provides a robust market for both diagnostic and workflow optimization solutions.

Which Factors Support Asia-Pacific and Europe Market Growth?

Asia-Pacific is expected to witness the fastest growth during the forecast period, supported by rapidly expanding healthcare infrastructure, increasing medical imaging volumes, and government initiatives promoting AI adoption in healthcare. Countries like China, Japan, South Korea, and India are at the forefront, with significant investments in digital health transformation and smart hospital initiatives. The presence of manufacturers like Shanghai United Imaging Healthcare and the growing number of AI startups in the region are accelerating market penetration.

In Europe, the leadership in regulatory frameworks and the push for healthcare digitalization are driving the adoption of AI-powered radiology systems. Countries like Germany, France, and the United Kingdom are leading implementations, with significant focus on integrating AI solutions into public healthcare systems and university medical centers to ensure the highest levels of diagnostic accuracy and clinical efficiency.

Key Players

The companies such as Siemens Healthineers AG, GE HealthCare, Koninklijke Philips N.V., and Canon Medical Systems Corporation lead the global AI for radiology market with comprehensive imaging equipment portfolios and AI-enabled diagnostic solutions, particularly for large-scale hospital networks and integrated delivery systems. Meanwhile, players including Fujifilm Holdings Corporation, Shanghai United Imaging Healthcare Co., Hologic, Inc., and Merative focus on specialized modality-specific AI applications targeting oncology and women's health diagnostic segments. Emerging AI-native companies and integrated platforms such as Aidoc, Viz.ai, Lunit, RapidAI, Qure.ai, Annalise.ai, Rad AI, DeepHealth, Enlitic, Inc., Subtle Medical, and Cleerly are strengthening the market through innovations in deep learning algorithms and cloud-native deployment architectures.

Key Questions Answered