U.S. AI in Medical Imaging Market Size, Share, Opportunities, And Trends By Offering (Software, Services), By Technology (Machine Learning, Deep Learning, Computer Vision), By Application (Oncology, Neurology, Cardiology, Pulmonary, Orthopedics, Others), And By End-User (Hospitals & Clinics, Diagnostic Imaging Centers, Research Institutes, Others) – Forecasts From 2025 To 2030

US AI in Medical Imaging Market Size:

The US AI in Medical Imaging market is expected to grow significantly during the forecast period.

US AI in Medical Imaging Market Highlights:

• AI enhances diagnostic accuracy in medical imaging, reducing errors and enabling early interventions.
• FDA approved over 950 AI/ML-enabled imaging devices by August 2024, driving adoption.
• AI integration with EHRs and PACS streamlines workflows, boosting clinical efficiency.
• Data privacy and high costs remain key barriers to widespread AI adoption.

US AI in Medical Imaging Market Introduction:

The integration of artificial intelligence (AI) into medical imaging in the United States represents a transformative shift in healthcare, redefining diagnostic precision, operational efficiency, and patient outcomes. AI technologies, particularly those leveraging machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), are increasingly embedded in medical imaging processes, enabling rapid analysis of complex imaging data from modalities such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-rays. This convergence of AI and medical imaging has ushered in an era of enhanced diagnostic accuracy, personalized treatment planning, and streamlined clinical workflows. As the US healthcare system grapples with rising chronic disease prevalence, an aging population, and workforce shortages, AI in medical imaging is poised to address critical challenges while unlocking new opportunities for innovation.

The US medical imaging market is a cornerstone of modern healthcare, driven by the need for accurate and timely diagnostics across specialties such as radiology, oncology, cardiology, and neurology. AI’s role in this market is to augment human capabilities by automating image analysis, detecting subtle patterns, and providing decision support to clinicians. AI applications range from identifying abnormalities in radiological scans to predicting disease progression and assisting in image-guided interventions. For instance, deep learning algorithms can analyze mammograms to detect breast cancer with accuracy comparable to or exceeding human radiologists, as demonstrated in a 2020 study published in Nature, which reported that an AI system outperformed radiologists in identifying breast cancer from mammograms in a large-scale dataset. Such advancements underscore AI’s potential to enhance diagnostic precision and reduce human error.

The US leads globally in AI adoption for medical imaging, driven by its robust healthcare infrastructure, significant research and development (R&D) investments, and a supportive regulatory environment. The Food and Drug Administration (FDA) has been instrumental in fostering innovation, with over 950 AI/ML-enabled medical devices approved as of August 2024, more than 75% of which are related to radiology. This regulatory support, combined with collaborations between academic institutions, healthcare providers, and technology developers, positions the US as a hub for AI-driven medical imaging innovation.

Recent advancements illustrate the dynamic growth of AI in the US medical imaging market. In June 2025, Perimeter Medical Imaging AI launched the OCT-Tissue Surveillance Registry to collect imaging data for enhancing AI-driven cancer detection models. Additionally, in April 2025, RadNet acquired iCAD for $103 million to bolster its AI-driven breast imaging capabilities, reflecting a trend of mergers and acquisitions to enhance technological portfolios. These developments highlight the industry’s focus on innovation and collaboration to address clinical needs.

US AI in Medical Imaging Market Drivers:

• Rising Demand for Accurate and Efficient Diagnostics

The increasing prevalence of chronic diseases, such as cancer, cardiovascular disorders, and neurological conditions, has heightened the need for precise and timely diagnostic tools in the US. Medical imaging, including modalities like CT, MRI, and mammography, is critical for early detection and treatment planning. AI enhances this process by automating image analysis, detecting subtle abnormalities, and reducing diagnostic errors. For instance, a 2020 study published in Nature demonstrated that an AI system outperformed radiologists in detecting breast cancer from mammograms, achieving a sensitivity of 94.5% compared to 88.4% for human readers in a dataset of over 25,000 images. The American Cancer Society (ACS) reported approximately 2 million new cancer cases in the US in 2024, underscoring the demand for advanced imaging solutions. AI’s ability to process large volumes of imaging data quickly enables earlier interventions, improving patient outcomes and reducing healthcare costs associated with late-stage treatments. Additionally, AI-driven tools can prioritize urgent cases, such as identifying potential strokes in CT scans, allowing radiologists to focus on critical cases first, further driving adoption in busy clinical settings.

• Technological Advancements in AI

 Rapid advancements in AI, particularly in deep learning and CNNs, have significantly improved the accuracy and efficiency of medical imaging analysis. These technologies enable AI systems to detect patterns in imaging data that may be imperceptible to the human eye, such as early signs of Alzheimer’s disease in MRI scans or microcalcifications in mammograms. A study in the Lancet Digital Health showcased an AI-driven denoising algorithm that reduced radiation doses in CT imaging by up to 80% while preserving diagnostic image quality, addressing patient safety concerns, and enhancing imaging accessibility. Furthermore, generative adversarial networks (GANs) are being explored to reconstruct high-quality images from low-dose scans, expanding the utility of AI in resource-constrained settings. The development of foundation models, which can be fine-tuned for multiple imaging modalities, is another leap forward. For example, in 2024, GE Healthcare and Mass General Brigham announced a collaboration to develop and integrate such models into clinical workflows, enabling scalable AI solutions across specialties like radiology and oncology. These technological strides make AI a vital tool for modernizing medical imaging practices.

• Government and Institutional Support

Federal and institutional initiatives are accelerating AI adoption in medical imaging through funding, regulatory frameworks, and collaborative research. The National Institutes of Health (NIH) launched the Bridge2AI initiative in 2022 with $130 million to advance AI in biomedical research, including the creation of large-scale imaging datasets and standardized AI tools. This program supports the development of interoperable AI models that can be deployed across diverse healthcare settings. Additionally, the FDA has streamlined the approval process for AI/ML-enabled medical devices, with over 950 approvals by August 2024, of which more than 75% are related to radiology. The FDA’s transparent regulatory framework encourages innovation while ensuring patient safety, fostering confidence among developers and healthcare providers. Academic-industry partnerships, such as those between Stanford University and NVIDIA, are also driving advancements by leveraging high-performance computing to train complex AI models for imaging applications. These efforts collectively bolster the infrastructure needed for widespread AI integration in medical imaging.

US AI in Medical Imaging Market Restraints:

• Data Privacy and Security Concerns

The use of sensitive patient imaging data in AI development raises significant concerns about privacy and compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA). AI systems require large datasets for training, often involving cloud-based processing, which increases the risk of data breaches or unauthorized access. A HIPAA journal highlights the rising number of data breaches, propelling the need for better security. Ensuring robust encryption, secure data transfer protocols, and compliance with federal regulations is resource-intensive and can slow AI adoption, particularly for smaller healthcare providers. Moreover, patient trust is critical; any perceived misuse of data can lead to reluctance in sharing medical records, limiting the datasets available for AI training. Addressing these concerns requires significant investment in cybersecurity infrastructure and transparent data governance policies, posing a challenge to market growth.

• High Development and Implementation Costs

Developing and deploying AI algorithms for medical imaging involves substantial financial investment, including costs for computational resources, skilled personnel, and clinical validation. Training deep learning models requires high-performance computing infrastructure, such as GPU clusters, which can be prohibitively expensive for smaller hospitals or imaging centers. A 2024 article in Health Affairs noted that the cost of implementing AI-driven imaging solutions, including integration with existing systems like Picture Archiving and Communication Systems (PACS), can range from hundreds of thousands to millions of dollars, creating barriers for resource-constrained facilities. Additionally, the need for continuous updates to AI models to account for new imaging modalities or clinical guidelines adds to long-term costs. These financial challenges can limit market penetration, particularly in rural or underserved areas, where healthcare providers may lack the capital to adopt cutting-edge AI technologies. This economic barrier risks creating disparities in access to AI-enhanced imaging, slowing overall market growth.

US AI in Medical Imaging Market Segmentation Analysis:

• By offering, the software segment is expected to grow significantly

Software is the cornerstone of AI in medical imaging, providing the algorithms and platforms that drive diagnostic accuracy and efficiency. AI software solutions, such as those for image segmentation, anomaly detection, and predictive analytics, are integrated into imaging modalities like CT, MRI, and X-rays to enhance clinical workflows. These tools leverage advanced algorithms to analyze vast datasets, identify patterns, and provide actionable insights, often outperforming traditional methods. For instance, a 2020 study in Nature demonstrated that an AI software system for breast cancer detection in mammograms achieved a sensitivity of 94.5%, surpassing human radiologists’ 88.4% in a dataset of over 25,000 images. The dominance of software is further evidenced by recent industry developments, such as GE Healthcare’s 2024 acquisition of MIM Software, which enhances its AI-driven imaging analytics for oncology and radiology, enabling precise diagnostics across multiple care areas. Additionally, AI software’s scalability and ability to integrate with existing systems like PACS make it indispensable. The FDA’s approval of over 950 AI/ML-enabled medical devices by August 2024, predominantly software-based solutions for radiology, underscores the segment’s market leadership. Software’s dominance is driven by its ability to reduce diagnostic errors, optimize radiologist workflows, and support personalized treatment, making it a critical component of modern medical imaging.

• The Deep Learning technology is gaining a large market share

Deep Learning (DL) dominates the AI in the medical imaging market due to its unparalleled capacity to analyze complex medical images with high accuracy and efficiency. Utilizing convolutional neural networks (CNNs) and other advanced architectures, DL algorithms excel at identifying intricate patterns in imaging data, such as tumors, lesions, or subtle anatomical changes, often surpassing human performance. A 2023 study in The Lancet Digital Health highlighted a DL-based denoising algorithm that reduced radiation doses in CT imaging significantly while maintaining diagnostic quality, enhancing patient safety, and imaging accessibility. DL’s dominance is further reinforced by its widespread adoption in clinical settings. For example, GE Healthcare introduced an advanced DL-based platform that reduces MRI scan times by up to 83% using its Sonic DL technology, capturing cardiac motion in real-time and improving diagnostic speed. DL’s ability to learn from large datasets and adapt to diverse imaging modalities, such as MRI, CT, and mammography, makes it the preferred technology. Its integration into platforms like NVIDIA’s Clara, which accelerates image processing through GPU-powered DL, further solidifies its market position. The technology’s robustness and adaptability drive its dominance, enabling faster, more accurate diagnoses and supporting the growing demand for AI in medical imaging.

• Neurology is anticipated to lead this market’s expansion

Neurology is the dominant application segment in the AI medical imaging market, driven by the increasing prevalence of neurological disorders and the critical need for precise imaging in diagnosis and treatment. Conditions such as brain tumors, strokes, multiple sclerosis, and dementia require advanced imaging modalities like MRI and CT, where AI enhances detection and monitoring. A study in Frontiers in Artificial Intelligence demonstrated that DL-based AI models improved the accuracy of MRI analysis for multiple sclerosis by automating lesion detection and quantification, reducing inter-observer variability, and enabling consistent assessments. The growing burden of neurological diseases, with over 610,000 new stroke cases annually in the US according to the CDC, underscores the demand for AI-driven solutions. Recent developments, such as TeraRecon’s Neuro Suite, launched in 2023, leverage AI to provide insights for neurological conditions like neuro-oncology and dementia, facilitating faster diagnosis and care activation. AI’s ability to detect subtle changes, such as early signs of brain tumors in less than 150 seconds using optical imaging and CNNs, further drives its adoption in neurology. The segment’s dominance is fueled by AI’s capacity to address the complexity of brain imaging, improve diagnostic precision, and support timely interventions, making it a pivotal application in the US market.

US AI in Medical Imaging Market Key Developments:

• Perimeter Medical Imaging AI’s OCT-Tissue Surveillance Registry (2025): Perimeter Medical Imaging AI launched the OCT-Tissue Surveillance Registry to collect extensive imaging data from surgical procedures using its optical coherence tomography (OCT) technology. This initiative aims to enhance AI deep-learning models for cancer detection, particularly in breast surgery, by providing high-resolution imaging data to improve diagnostic accuracy and patient outcomes.
• Philips and NVIDIA’s Collaboration on AI-Powered MRI Technology (2025): Philips partnered with NVIDIA to launch an AI-powered MRI foundational model aimed at improving image quality, accelerating scan times, and streamlining diagnostic workflows. This collaboration leverages NVIDIA’s GPU technology to enhance Philips’ MRI systems, reducing scan times by up to 83% through advanced deep learning algorithms like Sonic DL.
• GE Healthcare’s Acquisition of MIM Software (2024): GE Healthcare acquired MIM Software, a leading provider of AI-driven medical imaging analysis tools, to enhance its precision diagnostics capabilities. This acquisition bolsters GE Healthcare’s portfolio in oncology, radiology, and molecular imaging, enabling advanced image analysis for treatment planning and monitoring. MIM Software’s AI solutions, such as automated contouring and lesion detection, integrate with existing imaging systems to improve workflow efficiency and diagnostic accuracy.
• TeraRecon’s Neuro Suite Launch (2023): TeraRecon introduced its Neuro Suite, an AI-driven clinical suite designed for neurological imaging applications, including neuro-oncology, stroke, and dementia. The suite uses deep learning to automate lesion detection and quantification, reducing diagnostic time and improving accuracy for conditions like multiple sclerosis.

US AI in Medical Imaging Market Segmentations:

By Offering

• Software
• Services

By Technology

• Machine Learning
• Deep Learning
• Computer Vision

By Application

• Oncology
• Neurology
• Cardiology
• Pulmonary
• Orthopedics
• Others

By  End-User

• Hospitals & Clinics
• Diagnostics Image Centers
• Research Institutes
• Others

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. US Artificial Intelligence (AI)  in Medical Imaging Market By Offering

4.1. Introduction

4.2. Software

4.3. Services

5. US Artificial Intelligence (AI)  in Medical Imaging Market By Technology

5.1. Introduction

5.2. Machine Learning

5.3. Deep Learning

5.4. Computer Vision

6. US Artificial Intelligence (AI)  in Medical Imaging Market By Application

6.1. Introduction

6.2. Oncology

6.3. Neurology

6.4. Cardiology

6.5. Pulmonary

6.6. Orthopedics

6.7. Others

7. US Artificial Intelligence (AI)  in Medical Imaging Market By End-User

7.1. Introduction

7.2. Hospitals & Clinics

7.3. Diagnostics Image Centers

7.4. Research Institutes

7.5. Others

8. Competitive Environment and Analysis

8.1. Major Players and Strategy Analysis

8.2. Market Share Analysis

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. Company Profiles

9.1. GE Healthcare

9.2. Koninklijke Philips N.V.

9.3. Siemens Healthineers AG

9.4. NVIDIA Corporation

9.5. Microsoft Corporation

9.6. Enlitic, Inc.

9.7. Digital Diagnostics Inc.

9.8. Perimeter Medical Imaging AI, Inc.

9.9. TeraRecon, Inc.

9.10. iCAD, Inc.

GE Healthcare

Koninklijke Philips N.V.

Siemens Healthineers AG

NVIDIA Corporation

Microsoft Corporation

Enlitic, Inc.

Digital Diagnostics Inc.

Perimeter Medical Imaging AI, Inc.

TeraRecon, Inc.

iCAD, Inc.