Global AI in Medical Imaging Market By Modality (CT Scan, MRI, X-rays, Ultrasound and Nuclear Imaging), By Application (Neurology, Respiratory and Pulmonary, Cardiology, Breast Screening, Orthopedics and Other Applications), By Technology (Deep Learning, Natural Language Processing (NLP), Machine Learning and Other Technologies), By End Use (Hospitals, Diagnostic Imaging Centers, and Other End-Users), Region and Companies – Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2025-2034

Report Overview

Global AI in Medical Imaging Market size is expected to be worth around US$ 16.88 Billion by 2034 from US$ 1.70 Billion in 2024, growing at a CAGR of 25.8% during the forecast period 2025 to 2034. In 2024, North America led the market, achieving over 45.2% share with a revenue of US$ 0.77 Billion.

Artificial Intelligence in the medical imaging market uses technologies like artificial Intelligence in medical imaging systems like C T scans, X-rays, MRIs, and ultrasound. AI technology has the potential to improve the accuracy & efficiency of medical imaging by automating routine tasks, identifying anomalies & patterns that human operators may miss, and assisting with diagnosis & treatment planning.

The AI in the medical imaging market is expected to grow steadily in upcoming years due to factors like the rising demand for more efficient & accurate medical imaging solutions, the development of advanced AI technologies, and the growing investment in AI in healthcare. Key players in the AI in the medical imaging market include IBM Watson Health, GE Healthcare, Siemens Healthiness, and Philips Healthcare, among others.

The market is also benefiting from the growing need for efficient healthcare delivery, as AI can automate the time-consuming process of image analysis, thereby reducing the workload on radiologists and healthcare professionals. As a result, hospitals and diagnostic centers are increasingly integrating AI into their operations to enhance productivity and streamline clinical workflows.

Moreover, as the focus shifts toward personalized medicine, AI’s ability to tailor treatment plans based on patient-specific imaging data presents significant opportunities. Government support for AI-driven healthcare innovations, coupled with investments from both public and private sectors, is further fueling market growth.

Key Takeaways

• In 2024, the market for AI in Medical Imaging generated a revenue of US$ 1.70 billion, with a CAGR of 25.8%, and is expected to reach US$ 16.88 billion by the year 2034.
• The Modality segment is divided into CT Scan, MRI, X-rays, Ultrasound, and Nuclear Imaging with CT Scan taking the lead in 2023 with a market share of 37.4%.
• By Application, the market is bifurcated into Neurology, Respiratory and Pulmonary, Cardiology, Breast Screening, Orthopedics, and Other Applications, with Neurology leading the market with 39.8% of market share.
• By Technology, the market is bifurcated into Deep Learning, Natural Language Processing (NLP), Machine Learning, and Other Technologies with Deep Learning leading the market with 57.9% of market share.
• Furthermore, concerning the End Use segment, the market is segregated into Hospitals, Diagnostic Imaging Centers, and Other End-Users. The Hospitals sector stands out as the dominant segment, holding the largest revenue share of 53.7% in the AI in Medical Imaging market.
• North America led the market by securing a market share of 45.2% in 2023.

Modality Analysis

In the AI in Medical Imaging market, the CT Scan modality holds a dominant position with a market share of 37.4% due to its widespread use, high resolution, and ability to provide detailed images of the body’s internal structures. CT scans are commonly used for diagnosing a wide range of conditions, including cancers, cardiovascular diseases, and trauma. AI algorithms applied to CT scans significantly improve diagnostic accuracy by identifying subtle patterns, such as tumors or fractures, that may be difficult for human eyes to detect.

AI-powered solutions in CT scans also enhance speed and efficiency by automating image analysis, allowing healthcare providers to make quicker, more accurate decisions. Additionally, the rise in the demand for precision medicine and early diagnosis has further boosted the adoption of AI in CT scans. As a result, AI-driven CT imaging systems are increasingly used in hospitals and diagnostic centers to provide faster diagnosis, especially in emergency settings, making the CT Scan modality the leading segment in the AI in Medical Imaging market.

Application Analysis

The Neurology application segment dominates the AI in Medical Imaging market with a market share of 39.8% due to the increasing demand for advanced imaging technologies in the diagnosis and treatment of neurological disorders. AI-driven imaging solutions are critical in detecting conditions such as brain tumors, strokes, multiple sclerosis, and neurodegenerative diseases like Alzheimer’s.

The precision of AI algorithms, particularly deep learning models, enables better identification of complex patterns and abnormalities in brain scans, such as MRIs or CT scans. AI tools can automate the identification of areas of concern, enhancing diagnostic accuracy and reducing the time required for analysis.

For instance, AI is used to detect early signs of brain tumors or hemorrhages that might not be visible to human eyes. As the global prevalence of neurological disorders rises, AI’s role in neurology becomes increasingly important, offering opportunities for more personalized treatment plans, faster diagnosis, and improved patient outcomes, solidifying neurology as the leading application in this market.

Technology Analysis

The Deep Learning technology segment is the dominant segment within the AI in Medical Imaging market with a market share of 57.9% due to its exceptional capability to analyze large and complex datasets. Deep learning algorithms, particularly convolutional neural networks (CNNs), are highly effective in interpreting medical images and extracting meaningful insights.

These models are trained on vast amounts of data, enabling them to detect intricate patterns, classify abnormalities, and make predictions with remarkable accuracy. In medical imaging, deep learning is applied across modalities like CT, MRI, and X-rays, making it the cornerstone of AI advancements in healthcare diagnostics.

For example, deep learning algorithms can detect cancers, identify fractures, and segment tissues with much greater efficiency than traditional imaging techniques. Its continuous evolution in accuracy, computational power, and data processing capabilities ensures that deep learning remains the dominant technology, driving the adoption of AI across the medical imaging industry.

End Use Analysis

The Hospitals segment dominates the AI in Medical Imaging market with a market share of 53.7% due to their significant need for efficient, high-quality diagnostic imaging to manage patient care. Hospitals are increasingly adopting AI-driven solutions to enhance diagnostic accuracy, reduce clinician workload, and improve patient outcomes.

AI technology assists radiologists by automating routine tasks such as image segmentation, anomaly detection, and report generation, thus allowing them to focus on more complex cases. AI applications in hospitals also help streamline hospital operations, reduce wait times, and improve workflow efficiency. As hospitals treat a large volume of patients and provide a broad spectrum of medical services, the integration of AI in imaging plays a crucial role in enhancing diagnostic capabilities across departments, including emergency, oncology, neurology, and cardiology.

Moreover, with increasing patient data and the complexity of diagnoses, AI’s role in supporting personalized medicine and improving accuracy has become indispensable, making hospitals the dominant end-use segment in the AI in Medical Imaging market.

Key Market Segments

By Modality

• CT Scan
• MRI
• X-rays
• Ultrasound
• Nuclear Imaging

By Application

• Neurology
• Respiratory and Pulmonary
• Cardiology
• Breast Screening
• Orthopedics
• Other Applications

By Technology

• Deep Learning
• Natural Language Processing (NLP)
• Machine Learning
• Other Technologies

By End Use

• Hospitals
• Diagnostic Imaging Centers
• Other End-Users

Drivers

Increasing Adoption of AI for Improved Diagnostic Accuracy

The increasing adoption of AI in medical imaging is driven by its ability to significantly enhance diagnostic accuracy and reduce the risk of human errors, particularly in complex cases. AI, especially deep learning algorithms, has the ability to process vast amounts of medical imaging data quickly and accurately. This allows for better identification of subtle abnormalities such as early-stage tumors, fractures, or tissue degeneration that might be missed by the human eye.

For example, Aidoc uses AI to analyze CT scans for conditions like intracranial hemorrhages, providing results to radiologists in real time. This allows for quicker intervention, improving patient outcomes and potentially saving lives. AI’s ability to analyze large datasets also allows it to identify patterns and correlations that might not be immediately obvious to radiologists, thus providing more precise diagnoses. Furthermore, AI can work tirelessly 24/7, providing consistent and reliable results, especially in busy medical environments.

As healthcare providers focus on improving patient care and operational efficiency, AI-driven diagnostic tools are becoming essential for enhancing diagnostic accuracy, supporting clinical decision-making, and reducing misdiagnoses, all of which directly contribute to the growing adoption of AI in medical imaging. For instance, in May 2025, with the CE-IVDR compliance deadline of May 26th now in effect, Diagnostics.

Ai introduced the industry’s first fully-transparent machine learning platform, the CE-IVDR Strategic Advantage Platform, for clinical real-time PCR diagnostics. This groundbreaking platform reveals the exact process behind each result, setting a new standard for molecular-testing machine learning. Backed by over 15 years of expertise and millions of successfully processed samples, the technology boasts an accuracy rate exceeding 99.9%.

Restraints

High Costs of AI in Medical Imaging Measures

A significant restraint for the widespread adoption of AI in medical imaging is the high initial and ongoing cost of implementation. The integration of AI technologies into medical imaging systems requires substantial investment in hardware, software, and infrastructure. AI algorithms typically require powerful computing systems, including advanced graphics processing units (GPUs), to process and analyze large datasets of medical images.

Additionally, these systems often require substantial storage capacity to handle the vast amounts of data generated by medical imaging processes. For example, while AI can drastically improve the speed and accuracy of diagnoses, the upfront cost of purchasing and installing these systems can be prohibitive for smaller healthcare facilities or diagnostic centers.

Along with the cost of AI hardware, there are expenses related to system maintenance, software updates, and training healthcare professionals to use the technology effectively. Despite these challenges, larger institutions with more significant budgets are increasingly integrating AI solutions into their workflows, yet this creates a barrier for smaller practices that may not have the financial resources to adopt AI technology, limiting the market growth in certain regions.

Opportunities

Expanding AI Applications in Personalized Medicine

AI in medical imaging presents a tremendous opportunity in the rapidly expanding field of personalized medicine. Personalized medicine aims to provide tailored treatment plans based on the individual characteristics of a patient, such as their genetic makeup, lifestyle, and medical history. AI, when combined with medical imaging, can assist in creating more accurate and customized treatment plans by analyzing imaging data alongside other patient information.

For example, AI can help determine how a patient might respond to a particular cancer treatment by analyzing radiological images in combination with genetic and clinical data. HeartFlow, a leader in cardiac AI, offers technology that assesses coronary artery disease severity from CT scans and helps physicians design personalized heart disease treatments. This integration of AI and imaging is particularly promising for oncology, cardiology, and neurology, where understanding individual variations in disease progression can significantly improve treatment efficacy.

In April 2025, Biostate AI, a prominent innovator in artificial intelligence for RNA sequencing, partnered with Weill Cornell Medicine in a strategic collaboration aimed at developing AI-driven personalized assessments for patient disease prognosis and progression. The initial phase of the collaboration will concentrate on leukemia, utilizing the extensive biorepository of bone marrow and blood samples from the Weill Cornell Leukemia Program.

As the healthcare industry shifts towards more patient-centric care, AI’s ability to provide personalized treatment recommendations presents a significant growth opportunity, enabling more precise, effective, and individualized healthcare solutions.

Impact of Macroeconomic / Geopolitical Factors

Macroeconomic and geopolitical factors significantly influence the AI in Medical Imaging market, affecting growth, adoption, and market dynamics. On a macroeconomic scale, economic conditions such as GDP growth, healthcare spending, and technological investments play a pivotal role in driving the market. In regions where economies are expanding, there is a higher likelihood of increased healthcare budgets, investment in advanced technologies, and the adoption of AI-driven solutions in medical imaging.

For instance, during periods of economic growth, hospitals and diagnostic centers are more likely to invest in AI-based tools to enhance diagnostic accuracy, speed, and operational efficiency. Conversely, during economic downturns or recessions, healthcare institutions may face budget constraints, slowing the adoption of AI technologies in medical imaging due to high initial costs.

Geopolitical factors, such as government policies, healthcare regulations, and international trade agreements, also affect the market. For instance, countries with favorable healthcare policies and regulations that encourage innovation and investment in AI healthcare solutions see more rapid adoption.

The United States and European Union, with their strong regulatory frameworks and supportive initiatives, have become leaders in AI adoption in medical imaging. Conversely, regions with unstable political environments or stringent regulations may experience slower AI integration in medical imaging due to delays in regulatory approval or lack of investment incentives.

Latest Trends

Integration of AI with Radiology Workflow

A growing trend in the AI in medical imaging market is the seamless integration of AI technology into existing radiology workflows. AI is increasingly being embedded into clinical operations to enhance efficiency, reduce the workload on radiologists, and improve diagnostic accuracy. Rather than replacing human radiologists, AI acts as a supportive tool that helps prioritize cases, flag potential abnormalities, and suggest diagnoses, all in real-time. This integration significantly streamlines workflows and enables radiologists to focus on more complex and critical cases.

For example, Viz.ai, a company leveraging AI in medical imaging, has developed a platform that automatically detects conditions such as strokes in CT scans and alerts healthcare professionals within minutes. This real-time decision support can speed up diagnosis and treatment initiation, which is crucial in time-sensitive conditions like stroke or trauma. The trend of embedding AI within radiology workflows is also reducing the chances of missed diagnoses and minimizing delays in care, ultimately leading to faster, more accurate decisions.

Regional Analysis

North America is leading the AI in Medical Imaging Market

North America is the dominant region in the global AI medical imaging market. It is expected that North America accounted for the highest revenue share, 45.2%. Owing to North America has strong research & development infrastructure, especially in the field of medical imaging. Many of the world’s major research institutions & universities are situated in North America. North America has a large healthcare industry, which provides a large market for medical imaging technology.

The region also has a high level of healthcare expenditure, which allows healthcare providers to invest in advanced medical imaging technologies. Many countries in the APAC are investing heavily in healthcare infrastructure & technology, which includes AI in medical imaging. Additionally, the region has a huge number of skilled workforces in the field of artificial intelligence & medical imaging, which has contributed to the growth of new and innovative technologies in the market.

Key Regions and Countries

North America

• US
• Canada

Europe

• Germany
• France
• The UK
• Spain
• Italy
• Russia
• Netherland
• Rest of Europe

Asia Pacific

• China
• Japan
• South Korea
• India
• Australia
• New Zealand
• Singapore
• Thailand
• Vietnam
• Rest of APAC

Latin America

• Brazil
• Mexico
• Rest of Latin America

Middle East & Africa

• South Africa
• Saudi Arabia
• UAE
• Rest of MEA

Key Players Analysis

Key players in the AI in Medical Imaging market includes GE Healthcare, Siemens Healthineers, Philips Healthcare, IBM Watson Health, Canon Medical Systems Corporation, Zebra Medical Vision, Aidoc, Viz.ai, Arterys, Qure.ai, Butterfly Network, Freeno, HeartFlow, Radiology Partners, and Lunit.

GE Healthcare integrates AI-driven solutions into its medical imaging technologies to enhance diagnostic precision and workflow efficiency. With advancements in deep learning algorithms, GE Healthcare’s AI tools aid in the early detection of diseases like cancer and cardiovascular conditions, improving patient outcomes and streamlining healthcare operations. Siemens Healthineers leverages AI across its medical imaging portfolio to improve diagnostic accuracy and operational efficiency. The company focuses on deep learning technologies to provide innovative imaging solutions, such as AI-based automatic segmentation, which enhances radiology workflows, accelerates image interpretation, and supports personalized patient care.

Top Key Players

• GE Healthcare
• Siemens Healthineers
• Philips Healthcare
• IBM Watson Health
• Canon Medical Systems Corporation
• Zebra Medical Vision
• Aidoc
• ai
• Arterys
• ai
• Butterfly Network
• Freeno
• HeartFlow
• Radiology Partners
• Lunit
• Other key players

Recent Developments

• In May 2025, Philips collaborated with NVIDIA to enhance patient care in MRI through the latest AI advancements. The collaboration will involve developing a foundational model for MRI, powered by NVIDIA’s cutting-edge AI computing platform. This large deep learning neural network, trained on vast datasets, will serve as the foundation for a new generation of applications. These applications are expected to significantly improve MR image quality, reduce scan times, and enhance diagnostic workflow and accuracy across a variety of clinical applications.
• In March 2025, NVIDIA announced a partnership with GE HealthCare to drive innovation in autonomous imaging, specifically focusing on the development of autonomous X-ray technologies and ultrasound applications. By incorporating autonomy into systems like X-ray and ultrasound, medical imaging technologies will be able to comprehend and function within the physical world. This advancement allows for the automation of intricate workflows, including patient placement, image scanning, and quality control.
• In October 2024, GE HealthCare announced that it has integrated a third-party artificial intelligence (AI)-enabled application orchestration feature into True PACS and Centricity PACS. In partnership with Blackford, these new AI-powered solutions assist radiologists in managing their workload, potentially leading to faster diagnosis and treatment for patients.

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