AI Medical Image Analysis

AI Medical Image Analysis Market Outlook

The global AI medical image analysis market is projected to reach approximately USD 3.3 billion by 2035, growing at a compound annual growth rate (CAGR) of about 25% during the forecast period from 2025 to 2035. The escalating demand for enhanced diagnostic accuracy, the growing prevalence of chronic diseases, and the increasing adoption of AI technologies in healthcare are significantly propelling market growth. Additionally, the rise in healthcare expenditure, technological advancements in imaging techniques, and a surge in the aging population are also contributing to the robust growth trajectory of this market. Furthermore, the integration of AI with deep learning algorithms for image analysis is driving innovative solutions that enhance clinical decision-making.

Growth Factor of the Market

One of the primary growth factors for the AI medical image analysis market is the increasing data generated through medical imaging procedures. As imaging technologies advance, healthcare providers are facing challenges in managing vast amounts of data efficiently. AI-driven solutions help in automating image analysis, thereby reducing the burden on radiologists and improving diagnostic throughput. Furthermore, the implementation of AI in imaging offers enhanced accuracy, allowing for faster and more reliable results, which is particularly crucial in critical scenarios like emergency care. Another significant factor is the rise in the prevalence of lifestyle-related diseases, such as obesity and diabetes, which demand advanced imaging techniques for effective diagnosis and monitoring. The global shift towards personalized medicine is also contributing to the market's growth, as AI can tailor imaging analysis to individual patient needs. Finally, initiatives by government and regulatory bodies to promote AI integration in healthcare are creating a favorable environment for market advancement.

Key Highlights of the Market

• The market is expected to grow at a CAGR of 25% between 2025 and 2035.
• North America holds the largest market share due to advanced healthcare infrastructure.
• AI software solutions are leading the product type segment.
• Diagnostic imaging centers are projected to be significant end-users.
• Growing adoption of AI in various medical applications enhances diagnosis efficiency.

By Product Type

Software :

Software is the leading segment in the AI medical image analysis market due to its critical role in processing, interpreting, and analyzing medical images. The capabilities of AI software to utilize deep learning and machine learning algorithms enable it to extract meaningful insights from complex imaging data more efficiently. This software typically incorporates advanced analytical tools that assist radiologists and clinicians in diagnosing diseases with higher accuracy. Additionally, the continuous advancements in AI technologies, including natural language processing and image recognition, are enhancing the functionalities of these software solutions. As a result, hospitals and diagnostic imaging centers are increasingly adopting AI software to streamline workflows, reduce diagnostic errors, and ultimately improve patient care. The integration of AI software into existing imaging systems further boosts its adoption, making it a pivotal resource in modern healthcare settings.

Hardware :

In the AI medical image analysis market, hardware supports the implementation of AI solutions through advanced computing resources necessary for processing large datasets generated by medical imaging. Hardware solutions, including high-performance servers and specialized imaging machines, are integral to the effective functionality of AI software. With the growing demand for faster and more efficient image processing, the hardware segment has gained substantial traction as healthcare providers seek to upgrade their technological infrastructure. Moreover, the advent of edge computing and improved data storage solutions are facilitating the development of robust hardware that can accommodate AI applications in real-time analysis. As healthcare facilities increasingly recognize the importance of investing in cutting-edge hardware combined with AI capabilities, this segment is expected to witness significant growth, driven by the need for efficient and accurate diagnostic solutions.

By Imaging Modality

X-ray :

X-ray imaging remains one of the most widely used modalities in medical diagnosis, and its integration with AI technologies is revolutionizing the way radiologists interpret results. AI algorithms can analyze X-ray images to detect abnormalities such as fractures, infections, and tumors with enhanced precision. This modality benefits immensely from AI due to its ability to process large volumes of images quickly, enabling timely diagnoses. As AI continues to evolve, the accuracy of X-ray interpretations is improving, reducing the likelihood of missed diagnoses. Moreover, the combination of AI with X-ray imaging can facilitate routine screenings and preventive health measures, ultimately contributing to better patient outcomes and operational efficiency in healthcare facilities.

MRI :

Magnetic Resonance Imaging (MRI) is another crucial imaging modality where AI is making significant inroads. AI technologies help to enhance image clarity, optimize scanning parameters, and automate the interpretation of complex sequences. This improvement in MRI analysis not only assists radiologists in diagnosing conditions such as brain lesions and musculoskeletal disorders but also accelerates the workflow in busy imaging departments. Additionally, the use of AI can reduce the time required for scans, improving patient comfort and throughput. The growing emphasis on precision medicine and personalized healthcare is further driving the adoption of AI-enhanced MRI solutions, fostering better diagnostic accuracy and treatment planning.

CT :

Computed Tomography (CT) imaging is widely recognized for its ability to provide detailed cross-sectional images of the body, and the integration of AI in this modality is enhancing its diagnostic capabilities. AI algorithms are employed to improve the detection of conditions such as cancers, pulmonary diseases, and cardiovascular issues by analyzing CT scans more effectively than traditional methods. The use of AI in CT imaging also facilitates faster interpretation, allowing healthcare professionals to make more informed decisions in critical care settings. Furthermore, the ability of AI to learn from vast datasets enables continuous improvement in diagnostic accuracy, making CT imaging a powerful tool in the fight against various health conditions.

Ultrasound :

Ultrasound imaging has witnessed a transformation with the incorporation of AI technologies, leading to enhanced diagnostic capabilities and improved patient experiences. AI algorithms for ultrasound analysis can automate measurements and highlight areas of concern, aiding radiologists in making quicker and more accurate assessments. This modality is particularly advantageous in obstetrics, cardiology, and emergency medicine, where prompt decision-making is essential. The portability of ultrasound equipment combined with AI capabilities allows for bedside imaging and remote diagnostics, which is especially beneficial in low-resource settings. As the demand for accessible and efficient imaging solutions increases, the integration of AI in ultrasound is expected to grow significantly, thus expanding its application across diverse medical fields.

PET :

Positron Emission Tomography (PET) imaging plays a pivotal role in diagnosing and monitoring various cancers and neurological conditions. The integration of AI in PET imaging enhances the accuracy of interpreting complex metabolic patterns by providing advanced analytical capabilities. AI algorithms can assist in distinguishing between benign and malignant lesions, offering valuable insights for treatment planning. Furthermore, the synergy of PET with other imaging modalities, such as CT and MRI, when coupled with AI technologies, enables comprehensive diagnostic assessments. As the healthcare industry shifts towards personalized medicine, the role of AI in PET imaging will become increasingly important in tailoring patient-specific treatment strategies and improving overall care outcomes.

By Application

Tumor Detection :

Tumor detection is one of the most critical applications of AI medical image analysis, significantly enhancing the diagnostic accuracy for various types of cancers. AI algorithms can analyze imaging data from modalities such as MRI, CT, and PET to identify abnormal growths that may indicate malignancy. The ability of AI to recognize patterns that may not be visible to the human eye allows for early detection, which is vital for successful treatment outcomes. Moreover, AI-driven tools can assist radiologists by providing second opinions and highlighting regions of interest, thereby reducing the potential for misdiagnosis. The growing prevalence of cancer globally is driving the demand for advanced diagnostic solutions, and AI's role in tumor detection is becoming increasingly indispensable in clinical practice.

Fracture Detection :

AI medical image analysis plays a prominent role in fracture detection by automating the assessment of X-ray and CT images to identify breaks in bones accurately. The algorithms are designed to recognize various types of fractures, including subtle or complex fractures that may be overlooked during manual review. This is particularly beneficial in emergency departments where timely diagnosis is crucial for effective management. The implementation of AI solutions not only speeds up the diagnostic process but also enhances the overall workflow efficiency in radiology departments. With the rise in sports-related injuries and accidents, the demand for effective fracture detection solutions is expected to grow, making AI an essential tool in orthopedic imaging.

Brain Anomalies :

AI medical image analysis is instrumental in the detection of brain anomalies, including tumors, strokes, and degenerative diseases. Leveraging advanced algorithms, AI systems can analyze MRI and CT scans to identify structural abnormalities within the brain with high accuracy. The ability to differentiate between various types of brain lesions empowers clinicians to make informed decisions regarding treatment and management strategies. Moreover, AI can streamline the workflow in neurology departments, allowing specialists to focus on complex cases while ensuring routine scans are analyzed efficiently. The increasing incidence of neurological disorders is driving the need for reliable diagnostic solutions, highlighting the significant role of AI in brain anomaly detection.

Cardiovascular Disorders :

The application of AI in the analysis of cardiovascular disorders is gaining traction, particularly in the assessment of cardiac imaging modalities such as echocardiography, MRI, and CT. AI algorithms can process and analyze vast amounts of data generated from cardiac images, enabling the identification of conditions such as heart disease and vascular abnormalities. By providing real-time analysis, AI enhances the ability of healthcare providers to make timely and accurate diagnoses, which is crucial in managing cardiovascular health effectively. Additionally, the integration of AI in cardiovascular imaging supports risk stratification and personalized treatment approaches, thus improving patient outcomes significantly. As cardiovascular diseases remain a leading cause of morbidity and mortality worldwide, the role of AI in this application is expected to expand considerably.

Others :

In addition to the key applications mentioned, AI medical image analysis encompasses various other areas such as dental imaging, orthopedic assessments, and infectious disease diagnosis. These applications leverage AI technologies to enhance the accuracy and efficiency of image interpretation across diverse medical fields. For example, AI can assist in analyzing dental radiographs to detect early signs of tooth decay or periodontal issues. Similarly, in orthopedics, AI can aid in evaluating joint conditions and other musculoskeletal disorders through advanced imaging techniques. As AI continues to evolve, its applications in these and other specialized areas are expected to grow, providing invaluable support to healthcare professionals in delivering high-quality patient care.

By User

Hospitals :

Hospitals represent a significant user segment in the AI medical image analysis market, as they are primary providers of diagnostic imaging services. The growing demand for rapid and accurate diagnostics within hospital settings drives the adoption of AI technologies to enhance radiological practices. AI solutions not only assist radiologists in analyzing imaging data but also improve workflow efficiency by automating routine tasks. This enables healthcare professionals to focus on complex cases and patient interactions, ultimately improving the quality of care. Furthermore, hospitals are increasingly integrating AI into their existing imaging systems to optimize resource utilization and reduce operational costs. The robust growth of the healthcare sector, coupled with the need for advanced diagnostic tools, positions hospitals as a key driver of AI medical image analysis market growth.

Diagnostic Imaging Centers :

Diagnostic imaging centers are pivotal players in the AI medical image analysis ecosystem, leveraging AI technologies to enhance diagnostic accuracy and improve patient outcomes. These centers specialize in various imaging modalities and are often at the forefront of adopting innovative solutions to streamline operations. The automation of image analysis through AI not only expedites the diagnostic process but also minimizes the risk of human error, ensuring reliable results for patients. As these centers handle a high volume of imaging studies, the implementation of AI-driven tools can significantly enhance efficiency and reduce turnaround times. With increasing competition and a heightened focus on quality care, diagnostic imaging centers are expected to continue investing in AI technologies to maintain their competitive edge in the market.

Research Institutes :

Research institutes play a crucial role in advancing the field of AI medical image analysis through the development and validation of innovative algorithms and models. These institutions often collaborate with healthcare providers and technology companies to conduct studies that evaluate the efficacy of AI applications in clinical settings. By leveraging large datasets and advanced analytical techniques, research institutes contribute to the continuous improvement of AI technologies, enabling better diagnostic capabilities across various medical fields. Moreover, as the demand for evidence-based medicine grows, the insights derived from research studies will further encourage the adoption of AI in medical imaging. The focus on research and development in this sector is expected to foster a deeper understanding of AI's potential in enhancing diagnostic accuracy and patient care.

By Region

The North American region holds a significant share of the global AI medical image analysis market, driven by the presence of advanced healthcare infrastructure, higher adoption rates of cutting-edge technologies, and significant investments in research and development. The region is characterized by a robust market for AI solutions in medical imaging, with hospitals and diagnostic centers increasingly integrating AI technologies into their practices. The United States, in particular, has been at the forefront of AI advancements in healthcare, with numerous startups and established companies focusing on developing innovative solutions. The growth of the North American market is expected to maintain a CAGR of approximately 23% during the forecast period, owing to the growing prevalence of chronic diseases and the rising demand for accurate diagnostics.

Europe follows North America, with a strong emphasis on enhancing healthcare delivery through AI technologies in medical imaging. The region is witnessing a surge in collaborations between healthcare providers and technology firms aimed at developing AI-driven solutions tailored to meet the unique needs of European healthcare systems. Countries such as Germany, France, and the UK are leading the way in AI adoption, supported by favorable regulatory frameworks and government initiatives promoting digital health integration. The growing awareness of the benefits of AI in improving diagnostic accuracy and efficiency is propelling market growth across Europe, with projections indicating a steady increase in demand for AI medical image analysis solutions.

Opportunities

The AI medical image analysis market presents a myriad of opportunities driven by technological advancements and changing healthcare dynamics. One of the notable opportunities lies in the integration of AI with telemedicine and remote diagnostics, especially in the wake of global health crises that have highlighted the importance of accessible healthcare. As telehealth services continue to expand, the demand for AI-powered imaging solutions that can assist healthcare professionals in remote consultations is expected to grow. This trend not only enhances patient access to diagnostic services but also allows for timely interventions in critical cases. Furthermore, partnerships between AI technology providers and healthcare organizations can lead to the development of customized imaging solutions that cater to specific medical specialties, thereby expanding the market reach and application of AI in medical imaging.

Another promising opportunity in the AI medical image analysis market is the potential for advancing personalized medicine. With a growing focus on tailoring treatment strategies to individual patient needs, AI can play a pivotal role in analyzing imaging data to provide insights that inform personalized diagnostic and treatment pathways. The ability to predict patient outcomes based on imaging analyses can transform the approach to patient management, leading to improved treatment efficacy and reduced healthcare costs. Additionally, the ongoing development of AI algorithms that can learn and adapt over time presents opportunities for continuous improvement in diagnostic accuracy. Embracing these opportunities will be crucial for stakeholders looking to capitalize on the evolving landscape of AI in medical imaging.

Threats

Despite its promising outlook, the AI medical image analysis market faces several threats that could hinder its growth trajectory. One of the primary challenges is the widespread concern regarding data privacy and security, particularly given the sensitive nature of medical imaging data. As healthcare organizations increasingly adopt AI technologies, ensuring the protection of patient information becomes critical. A breach in data security can lead to severe legal repercussions and erode trust in AI systems, deterring their adoption. Additionally, the lack of standardization in AI algorithms and imaging protocols can create inconsistencies in clinical outcomes, raising concerns about the reliability of AI-driven diagnoses. This fragmentation may contribute to resistance from healthcare professionals who may be skeptical about integrating AI into their practices.

Another significant threat to the AI medical image analysis market is the potential for regulatory hurdles. The healthcare sector is heavily regulated, and the introduction of AI solutions in medical imaging must comply with various regulatory requirements. Navigating the complex landscape of approvals and certifications can be time-consuming and costly for technology developers. Moreover, the rapid pace of AI advancements may outstrip the existing regulatory frameworks, leading to uncertainty and potential delays in bringing innovative solutions to market. A cautious regulatory environment can stifle innovation and make it challenging for smaller companies to compete with established players. Addressing these threats will require a concerted effort from stakeholders to ensure data security, establish standardization, and engage with regulatory bodies effectively.

Competitor Outlook

• IBM Watson Health
• Siemens Healthineers
• GE Healthcare
• Philips Healthcare
• Canon Medical Systems Corporation
• Agfa-Gevaert Group
• RadNet, Inc.
• Epic Systems Corporation
• Zebra Medical Vision
• Aidoc
• Fujifilm Holdings Corporation
• Qure.ai
• DeepMind Technologies
• PathAI
• Tempus Labs, Inc.

The competitive landscape of the AI medical image analysis market is characterized by the presence of both prominent market players and emerging startups, each leveraging technological innovations to enhance their offerings. Established companies such as IBM Watson Health and Siemens Healthineers are investing heavily in research and development to integrate advanced AI capabilities into their imaging products. These companies often focus on comprehensive solutions that combine software and hardware components, positioning themselves as full-service providers in the healthcare sector. They also engage in strategic partnerships and collaborations with healthcare institutions to test and validate AI solutions, ensuring alignment with clinical needs and regulatory requirements.

Emerging companies such as Aidoc and Qure.ai are making significant strides in the AI medical image analysis space, focusing on niche applications and innovative technologies. These startups are often agile and can quickly adapt to changing market dynamics, allowing them to introduce cutting-edge solutions that address specific challenges faced by healthcare professionals. By leveraging advancements in machine learning and neural networks, these companies are enhancing diagnostic accuracy and workflow efficiency in imaging centers and hospitals. The competitive landscape encourages collaboration, as established players and startups alike recognize that combining expertise can lead to the development of more robust and reliable AI solutions in medical imaging.

Additionally, the competitive dynamics of the AI medical image analysis market are influenced by factors such as technological advancements, regulatory changes, and evolving customer expectations. Companies that prioritize user-friendly interfaces, real-time analysis capabilities, and seamless integration with existing imaging systems tend to gain a competitive edge. Furthermore, the emphasis on data privacy and security is shaping the strategies of market players as they work to build trust with healthcare providers and patients alike. Overall, the AI medical image analysis market is expected to remain competitive, with ongoing innovations and collaborations driving growth and improving patient care.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 Aidoc
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 PathAI
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 Qure.ai
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 RadNet, Inc.
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 GE Healthcare
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 IBM Watson Health
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 Tempus Labs, Inc.
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 Agfa-Gevaert Group
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 Philips Healthcare
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Siemens Healthineers
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 Zebra Medical Vision
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 DeepMind Technologies
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Epic Systems Corporation
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 Fujifilm Holdings Corporation
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Canon Medical Systems Corporation
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 AI Medical Image Analysis Market, By User
    • 6.1.1 Hospitals
    • 6.1.2 Diagnostic Imaging Centers
    • 6.1.3 Research Institutes
  • 6.2 AI Medical Image Analysis Market, By Application
    • 6.2.1 Tumor Detection
    • 6.2.2 Fracture Detection
    • 6.2.3 Brain Anomalies
    • 6.2.4 Cardiovascular Disorders
    • 6.2.5 Others
  • 6.3 AI Medical Image Analysis Market, By Product Type
    • 6.3.1 Software
    • 6.3.2 Hardware
  • 6.4 AI Medical Image Analysis Market, By Imaging Modality
    • 6.4.1 X-ray
    • 6.4.2 MRI
    • 6.4.3 CT
    • 6.4.4 Ultrasound
    • 6.4.5 PET

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Middle East & Africa - Market Analysis
    • 10.5.1 By Country
      • 10.5.1.1 Middle East
      • 10.5.1.2 Africa
  • 10.6 AI Medical Image Analysis Market by Region

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global AI Medical Image Analysis market is categorized based on

By Product Type

• Software
• Hardware

By Imaging Modality

• X-ray
• MRI
• CT
• Ultrasound
• PET

By Application

• Tumor Detection
• Fracture Detection
• Brain Anomalies
• Cardiovascular Disorders
• Others

By User

• Hospitals
• Diagnostic Imaging Centers
• Research Institutes

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Key Players

• IBM Watson Health
• Siemens Healthineers
• GE Healthcare
• Philips Healthcare
• Canon Medical Systems Corporation
• Agfa-Gevaert Group
• RadNet, Inc.
• Epic Systems Corporation
• Zebra Medical Vision
• Aidoc
• Fujifilm Holdings Corporation
• Qure.ai
• DeepMind Technologies
• PathAI
• Tempus Labs, Inc.