Artificial Intelligence (AI)-assisted Radiology

Global Artificial Intelligence (AI)-assisted Radiology Market to Reach US$13.0 Billion by 2030

The global market for Artificial Intelligence (AI)-assisted Radiology estimated at US$2.3 Billion in the year 2024, is expected to reach US$13.0 Billion by 2030, growing at a CAGR of 33.8% over the analysis period 2024-2030. X-Ray Technique, one of the segments analyzed in the report, is expected to record a 32.4% CAGR and reach US$4.0 Billion by the end of the analysis period. Growth in the Magnetic Resonance Imaging Technique segment is estimated at 37.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$592.4 Million While China is Forecast to Grow at 32.2% CAGR

The Artificial Intelligence (AI)-assisted Radiology market in the U.S. is estimated at US$592.4 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.9 Billion by the year 2030 trailing a CAGR of 32.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 30.7% and 29.4% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 23.7% CAGR.

Global Artificial Intelligence (AI)-Assisted Radiology Market – Key Trends & Drivers Summarized

Is AI Transforming the Role of Radiologists in Diagnostic Medicine?

Artificial Intelligence (AI) is bringing a profound shift to the field of radiology, transitioning it from a purely diagnostic discipline to a data-driven ecosystem that integrates speed, precision, and predictive insights. Unlike traditional radiology, which relies on the interpretation of images by human specialists, AI-assisted radiology leverages machine learning algorithms—especially deep learning networks—to analyze medical images with exceptional accuracy. These algorithms are trained on vast datasets of X-rays, CT scans, MRIs, and ultrasounds, enabling them to detect anomalies such as tumors, fractures, hemorrhages, and infections at an early stage. This capability allows for faster turnaround times and reduces the risk of oversight, which is particularly crucial in high-volume clinical settings. Rather than replacing radiologists, AI augments their capabilities by acting as a second set of eyes, flagging potential issues and prioritizing urgent cases. Some AI systems can even compare current images with prior scans to detect subtle changes that may signal disease progression or recurrence. Additionally, AI tools are now being integrated with natural language processing to help convert image interpretations into structured radiology reports, enhancing clarity and consistency. As these systems become more sophisticated, radiologists are increasingly taking on a supervisory and consultative role, focusing on complex decision-making and patient communication while AI handles the bulk of image review. This shift is redefining the scope of radiological practice, making it more efficient, proactive, and aligned with personalized medicine goals.

How Are Clinical Efficiency and Diagnostic Accuracy Driving AI Adoption in Radiology?

The adoption of AI in radiology is accelerating due to its ability to dramatically enhance diagnostic accuracy and clinical workflow efficiency. In overburdened healthcare systems, radiologists often face the challenge of reviewing thousands of images daily, increasing the risk of diagnostic errors and burnout. AI systems help alleviate this burden by automatically triaging studies based on urgency, flagging abnormalities, and even identifying incidental findings that may otherwise be missed. For instance, AI algorithms can detect lung nodules, brain bleeds, or bone fractures in seconds, helping ensure that critical findings are not delayed in the reading queue. Beyond flagging abnormalities, AI can quantify imaging biomarkers, measure lesion dimensions, and track treatment responses with high precision, adding valuable context to clinical decisions. Workflow optimization is another key benefit—AI tools can pre-populate reports, retrieve relevant patient history, and streamline image retrieval, allowing radiologists to focus more on interpretation and clinical consultation. In multidisciplinary care settings, AI facilitates better communication between radiologists, oncologists, surgeons, and primary care physicians by standardizing image analysis and improving data sharing. Furthermore, as reimbursement models shift toward value-based care, hospitals and clinics are under pressure to demonstrate outcomes. AI supports this by reducing unnecessary imaging, shortening diagnosis timelines, and improving consistency across radiologists. These operational and diagnostic benefits are not only elevating the standard of care but also strengthening the case for AI as a strategic tool in modern radiology departments.

Can AI Widen Access to Radiology Services in Underserved Regions?

A major transformative potential of AI-assisted radiology lies in its ability to bridge the diagnostic gap in regions with limited access to skilled radiologists and imaging infrastructure. In many parts of the world, particularly rural and low-resource settings, there is a severe shortage of radiology professionals, leading to delayed diagnoses and compromised patient outcomes. AI tools can help fill this void by enabling non-specialists to perform preliminary image evaluations and by supporting teleradiology networks that connect local clinics with centralized AI platforms. These systems can pre-screen scans and highlight findings for review by remote radiologists, ensuring that critical cases receive timely attention. Portable imaging devices paired with AI analysis are also making it feasible to deliver basic diagnostic services in the field or at the point of care. In settings where internet connectivity is unreliable, edge AI devices are emerging as a viable solution, allowing offline image processing without the need for constant cloud access. Moreover, AI models are being trained on diverse global datasets to improve their generalizability and reduce bias, ensuring that their performance remains robust across different populations and imaging modalities. International health organizations and governments are beginning to invest in AI radiology initiatives as part of broader efforts to digitize and decentralize healthcare. By automating core diagnostic tasks and enabling remote collaboration, AI is becoming a catalyst for expanding the reach and equity of radiology services, particularly in environments where human expertise is scarce.

What Are the Key Forces Fueling Global Growth in AI-Assisted Radiology?

The growth in the artificial intelligence (AI)-assisted radiology market is driven by a confluence of technological, clinical, and institutional dynamics that reflect the evolving demands of modern healthcare. One of the primary drivers is the rapid advancement of AI models, particularly convolutional neural networks (CNNs) and transformers, which have dramatically improved the accuracy of image recognition and classification tasks. The growing availability of large, annotated medical imaging datasets has been essential in training and validating these models. At the same time, improvements in computational infrastructure—especially in cloud and edge computing—have made it easier to deploy AI tools in clinical settings without the need for extensive IT overhauls. Another major factor is the increasing burden of chronic diseases such as cancer, cardiovascular disorders, and neurological conditions, which require continuous imaging for diagnosis and treatment monitoring. AI enhances the capacity of radiology departments to manage this rising demand without proportionally increasing manpower. Regulatory momentum is also contributing to market growth; with several AI-based radiology tools already approved by agencies such as the FDA and CE, the pathway to clinical adoption is becoming clearer. Hospitals and imaging centers are motivated to adopt AI not only for its diagnostic utility but also to gain a competitive edge, improve patient satisfaction, and align with digital transformation initiatives. Investment from venture capital, health tech companies, and academic institutions is further accelerating innovation in the space. Together, these forces are creating a robust and rapidly expanding global market for AI-assisted radiology that promises to reshape the future of diagnostic imaging.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence (AI)-assisted Radiology market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Technique (X-Ray Technique, Magnetic Resonance Imaging Technique, Computed Tomography Technique, Positron Emission Tomography Technique, Other Techniques); Application (Neurology Application, Mammography Application, Cardiovascular Application, Respiratory & Pulmonary Orthopedics Application, Other Applications); End-Use (Hospitals End-Use, Diagnostic Imaging Centers End-Use, Other End-Uses)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 48 Featured) -

• Aidoc
• Arterys
• Butterfly Network
• Caption Health
• ContextVision
• CureMetrix
• EnvoyAI
• iCAD
• Infervision
• MaxQ AI
• Maximal Tech
• MedyMatch Technology
• Nanox
• Qure.ai
• Rad AI
• Radboudumc (Radboud University Medical Center)
• Riverain Technologies
• Siemens Healthineers
• Zebra Medical Vision
• Zegami

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