The Global Artificial Intelligence (AI) in Medical Imaging Market is estimated to be valued at
The Global Artificial Intelligence (AI) in Medical Imaging Market refers to the industry that leverages artificial intelligence technologies to enhance the interpretation, processing, and analysis of medical images. It encompasses the integration of AI algorithms into imaging modalities such as CT scans, MRIs, X-rays, ultrasounds, and nuclear imaging to support healthcare professionals in diagnosing and managing diseases.
The market includes various AI tools, including deep learning, machine learning, and natural language processing, designed to improve the precision and efficiency of radiology and medical imaging workflows, as it plays a vital role in modernizing healthcare practices by reducing the workload of radiologists and improving patient outcomes.
The AI in the medical imaging market is driven by the need to address challenges associated with traditional diagnostic methods, which can often be time-consuming and prone to human error. AI algorithms are trained on large datasets of medical images to identify patterns and detect anomalies that may be missed by the human eye. This capability is particularly beneficial in complex and critical applications such as oncology, neurology, cardiology, and pulmonology, where early and accurate diagnosis is crucial.
The growing adoption of AI solutions in medical imaging is also fueled by the rising occurrence of chronic diseases, an aging population, and the global demand for more advanced and reliable healthcare services. The market is composed of various components, including AI-based software platforms, dedicated hardware such as high-performance GPUs and processors, and services like consulting and training to support AI deployment in medical institutions.
These components work together to optimize image acquisition, processing, and interpretation, thereby facilitating faster clinical decision-making. The adoption of cloud-based AI platforms is also on the rise, enabling healthcare providers to access advanced imaging tools remotely and collaborate across different geographies, further enhancing the reach and impact of AI technologies in medical diagnostics.
The US AI in Medical Imaging Market
The US AI in Medical Imaging Market is projected to be valued at USD 0.5 billion in 2025. It is further expected to witness subsequent growth in the upcoming period, holding USD 3.9 billion in 2034 at a CAGR of 24.3%.
The AI in the medical imaging market in the US is advancing at a rapid pace, driven by the country’s strong healthcare infrastructure and significant investment in artificial intelligence technologies. The US has always been an early adopter of cutting-edge innovations in healthcare, and the incorporation of AI into diagnostic imaging is no exception.
The rising demand for improved diagnostic accuracy, faster image analysis, and efficient radiology workflows has made AI-based imaging solutions a priority for hospitals, diagnostic centers, and outpatient clinics across the nation. This momentum is further fueled by the country’s ongoing healthcare digitalization initiatives, aimed at addressing inefficiencies and enhancing patient care outcomes.
One of the key factors driving AI adoption in the US medical imaging market is the rising number of chronic diseases such as cancer, cardiovascular conditions, and neurological disorders. These health challenges require timely and precise diagnostic imaging, and AI algorithms are proving invaluable in supporting radiologists to detect subtle abnormalities at earlier stages.
The growing elderly population in the US has further spurred the need for efficient diagnostic services, putting additional pressure on radiology departments to manage large volumes of imaging data. AI solutions help automate repetitive tasks, minimize human error, and improve turnaround times, which is critical in busy healthcare settings.
Global Artificial Intelligence (AI) in Medical Imaging Market: Key Takeaways
- Market Value: The Global Artificial Intelligence (AI) in medical imaging market size is expected to reach a value of USD 12.1 billion by 2034 from a base value of USD 1.5 billion in 2025 at a CAGR of 25.9%.
- By Component Type Segment Analysis: Software components are poised to consolidate their dominance in the component type segment capturing 62.3% of the total market share in 2025.
- By Technology Type Segment Analysis: Deep Learning Learning technologies are anticipated to maintain their dominance in the technology type segment capturing 57.3% of the total market share in 2025.
- By Application Type Segment Analysis: Neurological applications are expected to maintain their dominance in the application type segment capturing 33.9% of the total market share in 2025.
- By Modalities Type Segment Analysis: CT Scans are poised to consolidate their market position in the modalities type segment capturing 67.9% of the total market share in 2025.
- By End-Use Type Segment Analysis: Hospitals are anticipated to consolidate their market position in the end-use type segment capturing 57.1% of the total market share in 2025.
- Regional Analysis: North America is anticipated to lead the Global Artificial Intelligence (AI) in the medical imaging market landscape with 43.5% of total global market revenue in 2025.
- Key Players: Some key players in the Global Artificial Intelligence (AI) in medical imaging market are Siemens Healthineers, GE HealthCare, Philips Healthcare, IBM Watson Health, Canon Medical Systems, Fujifilm Holdings Corporation, Aidoc, Zebra Medical Vision, Arterys, Qure.ai, Lunit, Vuno Inc., HeartFlow, EnvoyAI, iCAD Inc., Bay Labs (now Caption Health), Butterfly Network, RadNet Inc., ScreenPoint Medical, DeepHealth (acquired by RadNet), and Other Key Players.
Global Artificial Intelligence (AI) in Medical Imaging Market: Use Cases
- Early Detection and Diagnosis of Cancer: AI in medical imaging is revolutionizing cancer detection by enabling earlier and more accurate diagnoses. AI-powered algorithms are extensively used in analyzing mammograms, CT scans, and MRIs to detect tumors and lesions that may otherwise go unnoticed. In breast cancer screening, AI can identify small abnormalities such as microcalcifications, allowing for earlier intervention and improved patient outcomes. These tools assist radiologists by flagging high-risk cases and reducing diagnostic errors, leading to faster decision-making and potentially lowering the need for invasive diagnostic procedures.
- Neurological Imaging for Stroke and Brain Disorders: AI plays a crucial role in supporting neurological imaging, particularly in diagnosing strokes, brain tumors, and neurodegenerative diseases. AI models trained on brain scans can quickly identify subtle signs of hemorrhage, ischemia, or abnormal tissue growth, which are vital in emergency stroke care or tumor detection. Additionally, AI aids in differentiating between various neurological disorders, such as Alzheimer’s or Parkinson’s disease, by analyzing patterns in brain imaging.
- Cardiovascular Imaging and Risk Assessment: In cardiology, AI-driven imaging tools are transforming how cardiovascular diseases are diagnosed and managed. AI is used to automate the analysis of echocardiograms, cardiac CTs, and MRIs, providing cardiologists with essential data on heart function, arterial blockages, and anatomical irregularities. Moreover, AI can combine imaging results with patient health records to predict future cardiac events such as heart attacks, enabling preventive strategies. These AI applications not only enhance diagnostic accuracy but also optimize workflow efficiency and support personalized treatment plans for cardiac patients.
- Musculoskeletal Imaging and Orthopedic Diagnostics: AI applications in musculoskeletal imaging are growing rapidly, especially in the field of orthopedics. AI-powered software assists in interpreting X-rays and MRIs to diagnose bone fractures, joint conditions, ligament injuries, and early-stage arthritis. By automating the measurement of bone density, cartilage degradation, and joint space narrowing, AI helps orthopedic specialists detect degenerative diseases and trauma-related injuries with greater precision. This use case is highly valuable in both emergency care for trauma patients and sports medicine, where AI aids in tracking recovery progress and refining rehabilitation programs.
Global Artificial Intelligence (AI) in Medical Imaging Market: Stats & Facts
- According to the Medical Imaging and Data Resource Center (MIDRC), over 300,000 imaging studies have been collected and more than 135,000 released to the public, facilitating AI research in medical imaging. Moreover, researchers trained over 3,000 AI models across various configurations and clinical tasks to assess fairness and performance in medical imaging AI.
- Additionally, NIBIB highlights that AI systems must perform equally across diverse patient groups, emphasizing the importance of fairness in medical imaging AI. Furthermore, as AI systems are deployed across clinical centers, ensuring equitable performance and reducing bias in medical imaging remains a critical focus.
- According to the National Institutes of Health (NIH), national experts have charted a foundational research roadmap to integrate AI into clinical radiology workflows. Additionally, the AI medical imaging market is growing rapidly due to technological advancements and increasing demand for clinical imaging. Furthermore, the NIH stresses that translational research is essential to bridge the gap between AI development and routine clinical practice.
- According to The Lancet Digital Health, a scoping review shows the USA and China lead in randomized controlled trials focused on deep learning systems for medical imaging. Additionally, AI trials report an 81% positive outcome rate in medical imaging applications, especially in gastroenterology and radiology.
- Furthermore, artificial neural networks demonstrate higher sensitivity in detecting subtle lesions in mammography compared to radiologists. Moreover, The Lancet Digital Health reports that 81% of randomized controlled trials on AI in clinical practice delivered positive outcomes, with a strong focus on medical imaging applications.
- Additionally, The Lancet Digital Health notes that the USA and China are leading globally in the number of AI-focused clinical trials, particularly in radiology and gastroenterology.
- The Lancet Digital Health reports the Medical AI Data for All (MAIDA) initiative, which is creating a global framework for medical imaging data sharing to enhance the evaluation of AI models across diverse populations.
- According to The Guardian, a study involving 461,818 women found that AI-assisted radiologists detected 17.6% more breast cancer cases without increasing false positives.
- According to The Sun, an NHS trial involving nearly 700,000 British women will deploy AI to detect breast tumors earlier and faster, potentially doubling radiology throughput.
- According to the National Library of Medicine (NLM), AI's role in image-based medicine is expanding, shaping the future of diagnostic workflows.
- According to the Journal of the American Medical Association (JAMA) Network Open, a study found that the use of AI in radiology is associated with reduced radiologist burnout, suggesting that AI can alleviate workload and improve job satisfaction.
- According to the U.S. Food and Drug Administration (FDA), as of July 30, 2023, there are over 692 market-cleared artificial intelligence (AI) medical algorithms available in the United States, with more than 76% related to medical imaging.
Global Artificial Intelligence (AI) in Medical Imaging Market: Market Dynamic
Global Artificial Intelligence (AI) in Medical Imaging Market: Driving Factors
Rising Demand for Diagnostic Accuracy and Workflow Efficiency
With healthcare institutions generating a large volume of imaging data, radiologists are under pressure to deliver accurate assessments within shorter timeframes. AI-powered tools help mitigate diagnostic variability by identifying subtle anomalies and patterns that might be overlooked during manual analysis. These systems reduce interpretation times while boosting the confidence of clinicians in their diagnoses. The ability of AI solutions to automate repetitive tasks, prioritize critical cases, and optimize imaging workflows is leading to their widespread adoption in hospitals and imaging centers globally. This trend is especially significant in high-demand areas such as oncology and emergency care, where rapid and accurate imaging is crucial for saving lives.
Rising Chronic Disease Burden and Shift toward Value-Based Care
Diseases like cancer, heart conditions, and neurological disorders are on the rise, necessitating frequent and advanced imaging for early detection and effective disease management. AI-based imaging solutions support clinicians by providing early warnings, automating measurements, and enabling predictive insights that guide long-term care strategies. As healthcare systems seek to enhance patient outcomes while reducing operational costs, AI technologies are becoming integral to achieving these goals. By supporting personalized treatment approaches, minimizing unnecessary interventions, and optimizing clinical workflows, AI is helping healthcare providers deliver higher-quality care at a lower cost, reinforcing its role as a key enabler in modern medical imaging.
Global Artificial Intelligence (AI) in Medical Imaging Market: Restraints
High Implementation Costs and Budget Constraints
Many healthcare providers, particularly in developing countries and smaller healthcare facilities, face financial challenges when investing in advanced AI technologies. The costs are not limited to acquiring AI software alone but also include the need for compatible high-performance hardware, data storage infrastructure, integration with existing imaging systems, and continuous system updates. Additionally, training staff to effectively use these AI tools further adds to the financial burden. For organizations operating under strict budgets, particularly public healthcare institutions, the return on investment may not be immediately apparent, slowing down the pace of AI adoption.
Data Privacy Concerns and Regulatory Challenges
AI systems rely heavily on large volumes of patient imaging data to train and refine algorithms. However, strict regulations regarding patient data protection, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe, create significant hurdles for organizations looking to leverage this sensitive information.
Healthcare providers must ensure that patient data is anonymized, securely stored, and handled in compliance with these regulations, which can complicate AI development and deployment processes. Moreover, the regulatory landscape for AI-driven medical devices is still evolving, with different countries setting varied and sometimes unclear requirements for approval and certification. This uncertainty makes it challenging for AI developers and healthcare providers to navigate compliance while scaling up AI adoption in clinical settings.
Global Artificial Intelligence (AI) in Medical Imaging Market: Opportunities
Integration of AI with Cloud-Based and Telemedicine Platforms
The rise of remote healthcare services, accelerated by the global shift toward virtual consultations, has created a need for advanced diagnostic solutions that can be accessed beyond traditional hospital settings. AI-powered imaging solutions hosted on cloud platforms allow healthcare professionals to analyze medical images remotely and collaborate with specialists across different regions. This is especially beneficial for rural and underserved areas where access to skilled radiologists may be limited. The combination of AI with telehealth services can significantly improve diagnostic speed and accuracy, enhance clinical decision-making, and expand healthcare access on a global scale.
Advancement of Personalized Medicine and Predictive Analytics
As healthcare shifts toward individualized treatment strategies, there is a growing demand for tools that can analyze patient-specific data and deliver customized diagnostic insights. AI systems capable of integrating imaging data with genetic information, clinical history, and lifestyle factors can help predict disease risks and customize treatment plans more effectively. This opens doors for AI technologies to play a key role not only in diagnostics but also in prognostics and treatment monitoring. The ability of AI to provide predictive analytics that supports early intervention and precision healthcare is driving new interest and investments in developing specialized AI imaging tools for personalized care pathways.
Global Artificial Intelligence (AI) in Medical Imaging Market: Trends
Shift Toward Explainable and Transparent AI Models
Healthcare providers and regulators are demanding AI tools that offer clear, interpretable insights into how decisions and diagnoses are generated. Clinicians need to understand the rationale behind AI-generated outputs to trust and effectively integrate these tools into clinical workflows. As a result, companies are now focusing on designing AI algorithms that not only deliver high accuracy but also provide visual cues, probability scores, and step-by-step reasoning for each result. This shift is crucial for promoting clinician confidence, supporting regulatory approvals, and fostering collaboration between AI systems and human experts within diagnostic processes.
Growing Use of AI in Multi-Modal Imaging
AI algorithms are being developed to integrate and analyze data from multiple imaging modalities, providing a fuller picture of patient health and improving the accuracy of diagnoses. This trend is particularly impactful in complex cases, such as oncology or neurological disorders, where multi-modal imaging can reveal different aspects of disease progression. By synthesizing diverse data streams, AI systems enhance diagnostic precision, help detect subtle correlations, and support more personalized and holistic treatment plans, driving broader adoption across healthcare settings.
Global Artificial Intelligence (AI) in Medical Imaging Market: Research Scope and Analysis
By Component Analysis
Software components are expected to maintain a leading position within AI in the medical imaging market, accounting for approximately 62.3% of the total market share in 2025. This dominance is primarily due to the growing demand for advanced AI-driven algorithms and platforms that assist in the interpretation and analysis of complex medical images. Software solutions are integral to improving diagnostic accuracy, reducing radiologist workload, and streamlining imaging workflows.
These platforms utilize deep learning, computer vision, and natural language processing to detect abnormalities, classify diseases, and generate actionable insights for clinicians. Furthermore, the flexibility of software components allows for easy integration into existing radiology information systems (RIS) and picture archiving and communication systems (PACS), making them highly scalable and adaptable to different healthcare settings. The rise of cloud-based deployment models and AI-as-a-service offerings has also contributed to the rapid adoption of software solutions, enabling healthcare providers to access AI tools without heavy investment in on-premises infrastructure.
On the other hand, the hardware segment plays a crucial but more supportive role in the overall AI in the medical imaging ecosystem. While software applications handle the bulk of image interpretation and analytics, they rely heavily on robust hardware to function efficiently. High-performance computing systems, including GPUs and specialized AI processors, are essential for training complex deep-learning models and running them in real-time clinical environments. Medical imaging hardware such as advanced CT scanners, MRI machines, and ultrasound devices are now being equipped with integrated AI chips and embedded software to perform on-device analytics. These AI-enhanced imaging systems reduce the need for external data processing and enable faster turnaround times for diagnostic reports.
By Technology Analysis
Deep learning technologies are projected to hold a dominant position in the AI medical imaging market, expected to capture around 57.3% of the total market share in 2025. The widespread adoption of deep learning is largely attributed to its unmatched ability to process and learn from large volumes of complex imaging data, such as X-rays, MRIs, CT scans, and ultrasound images. By utilizing convolutional neural networks (CNNs) and other deep learning architectures, these technologies can identify intricate patterns and subtle anomalies in medical images that might be difficult for the human eye to detect.
Deep learning models continuously improve their performance over time through exposure to diverse datasets, making them highly reliable for tasks such as tumor detection, segmentation, classification, and risk prediction. This has made deep learning indispensable in areas like oncology, cardiology, and neurology, where diagnostic accuracy and speed are critical. Additionally, deep learning’s ability to automate time-consuming image-processing tasks has led to faster clinical decision-making and optimized workflow efficiency in healthcare facilities globally.
Natural language processing (NLP) also plays an important role within AI in the medical imaging market. While deep learning focuses on the analysis of visual data, NLP technologies bridge the gap between imaging outputs and textual information found in clinical notes, radiology reports, and electronic health records (EHRs).
NLP enables AI systems to extract relevant patient history, correlate it with imaging findings, and generate structured, context-rich reports. This integration of visual data with clinical narratives enhances the overall diagnostic process, allowing healthcare providers to deliver more comprehensive and patient-centric care. For instance, NLP algorithms can automatically summarize key observations from imaging results and match them with prior medical records to provide insights into disease progression or treatment response.
By Application Analysis
Neurological applications are projected to dominate AI in the medical imaging market, accounting for approximately 33.9% of the total market share in 2025. The growing incidence of neurological disorders such as stroke, brain tumors, Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis is driving the demand for advanced imaging solutions in neurology. AI-powered imaging tools play a critical role in supporting neurologists and radiologists by automating the detection of abnormalities like brain hemorrhages, ischemic strokes, or early signs of neurodegenerative diseases.
The precision offered by AI algorithms, particularly deep learning models, enables early diagnosis and more effective treatment planning, which is essential in managing time-sensitive conditions like acute stroke. Additionally, AI enhances the interpretation of complex modalities such as MRI and CT scans of the brain, reducing diagnostic errors and providing quantitative insights such as lesion volume measurements and brain atrophy analysis. This has made AI indispensable in neurology, where accurate and timely imaging is crucial for patient outcomes.
In addition, the respiratory and pulmonary applications are emerging as a significant focus area within the AI in the medical imaging market. These applications are gaining traction due to the rising prevalence of chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), lung cancer, asthma, and interstitial lung diseases. AI solutions in this segment are being leveraged to enhance chest imaging, such as X-rays and CT scans, enabling more precise detection of pulmonary nodules, fibrosis, and airway obstructions. AI-based tools assist in identifying early-stage lung cancer or complications associated with infectious diseases such as pneumonia or tuberculosis, which is particularly valuable in areas with high disease burdens and limited access to specialized care.
By Modalities Analysis
CT scans are anticipated to firmly consolidate their leading position in the modalities segment of the AI in medical imaging market, projected to capture approximately 67.9% of the total market share in 2025. This dominance is largely attributed to the widespread utilization of CT imaging across various clinical applications, including trauma care, oncology, cardiology, and neurology. CT scanners provide rapid, high-resolution images of the body's internal structures, making them indispensable in emergencies and routine diagnostic procedures alike.
The integration of AI into CT imaging has significantly improved the modality’s capabilities, allowing for enhanced image reconstruction, automatic detection of lesions, and faster analysis of large datasets. AI-driven CT tools are now being used to detect early signs of complex diseases such as lung cancer, cardiovascular disorders, and stroke with greater speed and accuracy. Additionally, AI algorithms help optimize radiation doses during CT procedures, balancing diagnostic quality with patient safety, which has become a critical focus in modern medical imaging practices.
MRI also plays a crucial role, particularly in imaging soft tissues and neurological structures. AI-powered MRI applications are being used for the detection and monitoring of neurological disorders, musculoskeletal injuries, and oncological conditions, where high-contrast resolution is necessary. Unlike CT, which is often favored for speed, MRI is known for its superior ability to capture fine tissue details without ionizing radiation, making it the preferred modality in cases such as brain imaging, spinal cord assessments, and soft tissue tumor evaluations.
AI integration in MRI workflows enhances image acquisition, automates segmentation tasks, and supports more accurate interpretation of complex datasets. Advanced AI solutions also help reduce lengthy MRI scan times by optimizing sequences and improving image quality, which contributes to better patient experience and higher throughput in imaging departments.
By End-Use Analysis
Hospitals are expected to solidify their dominant position in the end-use segment of the AI in medical imaging market, capturing approximately 57.1% of the total market share in 2025. This trend is driven by hospitals’ comprehensive healthcare infrastructure, high patient volumes, and access to multidisciplinary teams of specialists. Hospitals often handle a wide range of complex and acute cases that require advanced imaging techniques and rapid, accurate diagnoses. The integration of AI into hospital imaging departments enhances clinical decision-making by improving diagnostic precision, reducing turnaround times for reports, and optimizing workflows.
AI algorithms assist radiologists by automating routine tasks such as lesion detection, segmentation, and measurement, allowing for more efficient management of the growing demand for imaging services. Moreover, hospitals are adopting AI tools to support early disease detection and population health initiatives, where early diagnosis plays a key role in reducing long-term healthcare costs and improving patient outcomes.
Diagnostic imaging centers also play a vital role within AI in the medical imaging market. These specialized centers often focus on outpatient imaging services, catering to both routine and specialized diagnostic needs. AI technologies are enabling these centers to improve their service offerings by delivering faster and more accurate diagnostic reports, which is critical in outpatient settings where patient convenience and speed are top priorities.
AI-driven solutions help imaging centers streamline workflows, automate image processing tasks, and reduce human error, thereby enhancing operational efficiency and improving patient satisfaction. Additionally, diagnostic imaging centers are adopting AI to enhance the quality of screenings and routine examinations such as mammography, chest X-rays, and musculoskeletal scans. AI-powered tools can flag subtle anomalies that may be overlooked in high-volume settings, contributing to earlier detection of conditions like breast cancer or pulmonary nodules.
The AI in Medical Imaging Market Report is segmented on the basis of the following:
By Component
- Software
- Hardware
- Services
By Technology
- Deep Learning
- Natural Language Processing (NLP)
- Others
By Application
- Neurology
- Respiratory and Pulmonary
- Cardiology
- Breast Screening
- Orthopedics
- Others
By Modalities
- CT Scan
- MRI
- X-rays
- Ultrasound
- Nuclear Imaging
By End Use
- Hospitals
- Diagnostic Imaging Centers
- Others
Regional Analysis
Region with the Largest Revenue Share
North America is projected to dominate the Global Artificial Intelligence (AI) in medical imaging market, capturing approximately
43.5% of the total market revenue in 2025. This leadership position is fueled by the region’s highly advanced healthcare infrastructure, significant investment in medical technology, and early adoption of AI solutions across clinical settings. The United States, in particular, has been at the forefront of integrating AI into healthcare systems, with numerous hospitals, diagnostic centers, and research institutions actively implementing AI-based imaging tools to enhance diagnostic accuracy and operational efficiency.
The presence of key market players, innovative startups, and established AI technology providers further strengthens North America’s dominance, creating a highly competitive and innovation-driven ecosystem. The region benefits from strong collaboration between the healthcare and technology sectors, resulting in the rapid development and deployment of AI-powered imaging platforms.
Region with the Highest CAGR
Asia Pacific is anticipated to register the highest compound annual growth rate (CAGR) in the Global Artificial Intelligence (AI) in medical imaging market over the forecast period. The region’s growth is primarily driven by the region’s evolving healthcare landscape, rising healthcare expenditure, and rising demand for advanced diagnostic technologies to meet the needs of its vast and aging population. Countries such as China, India, Japan, South Korea, and Australia are leading the way in embracing AI-powered medical imaging solutions, spurred by a growing focus on improving healthcare accessibility and diagnostic accuracy.
The rising incidence of chronic diseases, integrated with the shortage of skilled radiologists and healthcare professionals in several parts of the region, has created a pressing need for AI-based tools to bridge the gap and support overburdened healthcare systems. In parallel, the Asia Pacific region is experiencing significant advancements in digital infrastructure and AI research capabilities, with governments and private sectors heavily investing in AI technologies for healthcare applications.
By Region
North America
Europe
- Germany
- The U.K.
- France
- Italy
- Russia
- Spain
- Benelux
- Nordic
- Rest of Europe
Asia-Pacific
- China
- Japan
- South Korea
- India
- ANZ
- ASEAN
- Rest of Asia-Pacific
Latin America
- Brazil
- Mexico
- Argentina
- Colombia
- Rest of Latin America
Middle East & Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Egypt
- Rest of MEA
Global Artificial Intelligence (AI) in Medical Imaging Market: Competitive Landscape
The global competitive landscape of the AI in medical imaging market is characterized by a dynamic mix of established multinational corporations, emerging technology firms, and innovative startups. Leading companies such as Siemens Healthineers, GE HealthCare, Philips Healthcare, IBM Watson Health, and Canon Medical Systems continue to leverage their deep expertise in medical imaging and artificial intelligence to maintain a strong market presence.
These industry giants are focusing on expanding their AI-driven product portfolios, forging strategic partnerships with healthcare institutions, and investing heavily in research and development to enhance the accuracy, speed, and clinical applicability of their AI solutions. Their expansive global networks and longstanding relationships with hospitals and diagnostic centers give them a competitive advantage in scaling AI technologies across diverse healthcare ecosystems.
At the same time, the competitive environment is being reshaped by the growing influence of specialized AI-focused companies such as Aidoc, Zebra Medical Vision, Arterys, and Qure.ai. These companies are introducing highly specialized, cloud-based AI solutions designed for specific imaging applications like oncology, neurology, cardiology, and pulmonary diagnostics. Their agility in innovation and speed to market has allowed them to compete effectively with larger players, especially in regions where AI adoption is accelerating.
Some of the prominent players in the Global Artificial Intelligence (AI) in Medical Imaging are:
- Siemens Healthineers
- GE HealthCare
- Philips Healthcare
- IBM Watson Health
- Canon Medical Systems
- Fujifilm Holdings Corporation
- Aidoc
- Zebra Medical Vision
- Arterys
- Qure.ai
- Lunit
- Vuno Inc.
- HeartFlow
- EnvoyAI
- iCAD Inc.
- Bay Labs (now Caption Health)
- Butterfly Network
- RadNet Inc.
- ScreenPoint Medical
- DeepHealth (acquired by RadNet)
- Other Key Players
Global Artificial Intelligence (AI) in Medical Imaging Market: Recent Developments
- March 2025: Gleamer, a French radiology AI software provider, expanded into MRI imaging by acquiring two companies, Pixyl, and Caerus Medical. This strategic move aims to enhance Gleamer's AI imaging portfolio, particularly in neuro and lumbar MRI applications.
- December 2024: RadNet Inc., a prominent imaging center operator, disclosed that it had invested over USD 54 million in acquisitions throughout 2024. Notably, this included the purchase of Kheiron Medical Technologies, an AI firm specializing in medical imaging, for USD 1 million.
- December 2024: Macquarie Asset Management re-entered negotiations to acquire I-MED, Australia's largest radiology company, from private equity owner Permira. The transaction is expected to exceed USD 3 billion, reflecting the growing interest in integrating AI into diagnostic services.
- January 2024: GE HealthCare acquired MIM Software Inc., a Cleveland-based provider of imaging analysis software. This acquisition aims to enhance GE's advanced visualization product line with integrated AI capabilities.
- September 2023: MedQIA, WorldCare Clinical, and DARC merged to form Voiant, LLC, establishing a new paradigm in AI-driven clinical trial imaging. This consolidation aims to leverage combined expertise to enhance imaging solutions in clinical trials.
- August 2023: Tempus, a precision medicine company, acquired Arterys, signaling a significant move to bolster its imaging AI technology. This acquisition reflects the ongoing integration of AI beyond traditional radiology into broader healthcare applications.
- July 2023: Commure acquired Augmedix for USD 139 million, marking one of the largest disclosed deals in the medical devices industry related to artificial intelligence. This acquisition underscores the growing importance of AI in medical imaging and related fields.
- June 2023: Aidoc, an AI radiology firm, acquired Imbio, a company specializing in AI-driven imaging analysis for pulmonary and cardiothoracic diseases. This acquisition aims to expand Aidoc's AI capabilities in medical imaging.
- May 2023: Philips Healthcare acquired Carestream Health's Healthcare Information Systems business, aiming to enhance its enterprise imaging informatics and expand its AI-driven diagnostic imaging solutions.