US AI in Radiology Market Report, Size, Share, Opportunities, and Trends Segmented By Technology, Application, and End-User – Forecasts from 2025 to 2030

Description

US AI in Radiology Market Size:

US AI in Radiology Market is anticipated to expand at a high CAGR over the forecast period.

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The landscape of diagnostic medicine in the United States is undergoing a fundamental transformation catalyzed by the integration of Artificial Intelligence into radiological workflows. This technological pivot is an imperative response to critical systemic pressures, notably the escalating demand for advanced diagnostic imaging driven by an aging population and the persistent, widening gap between imaging volume growth and the availability of qualified radiologists. AI applications, particularly those utilizing deep learning and natural language processing, are transitioning from conceptual tools to clinical necessities, providing tangible value by augmenting the diagnostic capacity and efficiency of healthcare institutions. The market dynamics are currently being defined by the interplay between rapid technological innovation, a receptive regulatory environment focused on device safety and efficacy, and a complex reimbursement structure that dictates the pace of commercial scale. This analysis dissects these core drivers, constraints, and opportunities to provide an in-depth perspective for industry professionals navigating this high-stakes sector.

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US AI in Radiology Market Growth Drivers:

The primary catalyst propelling market demand is the radiologist shortage and the concomitant surge in imaging volume. Radiologists' workloads have intensified, with imaging volume outpacing the workforce growth, creating an operational imperative for efficiency tools. AI-enabled triage software, for instance, directly increases demand for the AI product by offering an immediate solution to clinical pressure by automatically flagging time-critical findings like intracranial hemorrhages or pulmonary embolisms. Secondly, the rapid accumulation of FDA 510(k) clearances for AI algorithms in radiology validates the clinical utility and safety of these products, lowering the adoption risk for hospitals and diagnostic centers and thus stimulating purchasing demand. The third driver is the shift toward value-based care models; AI's ability to reduce diagnostic error rates and decrease turnaround times for reports directly aligns with these models, making the technology a strategic investment for institutions seeking improved patient outcomes and efficiency.

• Challenges and Opportunities

A significant constraint on demand is the lack of a consistent, clear CMS reimbursement pathway for many AI in radiology solutions. Despite over 700 AI radiology tools having FDA clearance, the limited number of CPT codes and the absence of a dedicated payment mechanism for "assistive" AI functionality forces hospitals to absorb the cost, leading to slower procurement cycles and market penetration. This acts as a substantial headwind against product demand. Conversely, a major opportunity lies in the integration of AI into clinical reporting (NLP/Generative AI). Products that streamline report generation, such as those that structure findings or populate preliminary reports, directly address the high cognitive load and administrative burden on radiologists. The demand is shifting from pure detection tools to workflow augmentation and automation tools that offer immediate, measurable return on investment in time savings and throughput, presenting a lucrative avenue for vendors.

• Supply Chain Analysis

The AI in Radiology market is intrinsically a software-as-a-service (SaaS) and intellectual property (IP)-centric sector, fundamentally driven by algorithms and data, not physical raw materials. The global supply chain is characterized by a reliance on three key, non-physical components: data sourcing/curation, algorithm development hubs, and cloud infrastructure providers. Data acquisition is a significant logistical complexity, requiring secure, de-identified patient data from diverse hospital systems for algorithm training and validation. Key development hubs are concentrated in the U.S. (Boston, Silicon Valley) and certain international locations (e.g., Israel, Canada). The ultimate dependency is on major cloud service providers (Amazon Web Services, Microsoft Azure, Google Cloud), which serve as the computational backbone for hosting, running, and orchestrating the AI applications in clinical settings, creating a critical technological and security dependency.

US AI in Radiology Market Government Regulations:

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US AI in Radiology Market Segment Analysis:

• By Technology: Deep Learning

The Deep Learning segment holds the largest share of technological focus, primarily because its architecture—specifically Convolutional Neural Networks (CNNs)—excels at complex pattern recognition directly from raw image data, which is the cornerstone of radiological diagnosis. The demand for Deep Learning is intensely focused on augmenting detection and quantification tasks. For instance, in breast cancer screening, Deep Learning algorithms can serve as an effective second reader for mammograms, demonstrably reducing false-negative rates and increasing radiologist confidence. Similarly, the ability of Deep Learning to quickly and accurately segment anatomical structures and quantify disease burden (e.g., in CT lung nodule tracking) directly addresses the necessity for precision and speed in high-volume settings. The availability of increasingly large and diverse public and private imaging datasets for training purposes further fuels the demand for this technology, as model performance scales with the quality and volume of data it processes. The complexity of these models drives demand for integrated, enterprise-level solutions that can manage model deployment and monitoring seamlessly.

• By End-User: Hospitals And Clinics

Hospitals and Clinics represent the largest end-user segment due to their status as the primary hubs for diagnostic imaging and patient intervention. The demand in this segment is a direct function of the operational and clinical imperatives facing hospital administration. Operationally, AI in radiology addresses the challenge of increasing patient throughput and reducing the length of stay, a critical metric for financial performance. Clinically, the demand is driven by the need to improve diagnostic accuracy and reduce inter-reader variability, particularly in emergency and critical care pathways (e.g., stroke or trauma protocols). For a large hospital system, a certified AI solution that can automatically prioritize critical studies (Computer-Assisted Triage and Notification) is an indispensable asset because it directly minimizes the risk of a missed or delayed diagnosis, mitigating potential liability and improving time-to-treatment. This segment's purchasing decisions are heavily influenced by the evidence of clinical validation and the integration capability of the AI product into existing Electronic Health Record (EHR) and Picture Archiving and Communication System (PACS) infrastructure.

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US AI in Radiology Market Competitive Analysis:

The competitive landscape for US AI in Radiology is highly fragmented but dominated by a few major players—a mix of established global medical imaging conglomerates and nimble, specialized AI software startups. Competition centers on three vectors: FDA clearance volume, integration capability, and verifiable clinical utility. Major companies leverage their vast installation bases and robust PACS/EHR integration pipelines, while specialized startups often excel in developing highly specific, best-in-class algorithms for niche applications (e.g., lung nodules, stroke). Successful market penetration requires overcoming the inertia of existing hospital IT systems.

• GE HealthCare

GE HealthCare commands a formidable strategic position, capitalizing on its vast install base of imaging modalities (CT, MRI, X-Ray) and its established enterprise imaging platforms (True PACS, Centricity PACS). The company's strategy focuses on AI orchestration and integration, aiming to offer a single, validated platform for accessing a curated catalog of third-party AI applications. In October 2024, GE HealthCare announced the integration of a third-party AI-enabled application orchestration feature into its PACS solutions, a move designed to simplify the radiologist's experience and accelerate the adoption of diverse AI tools within the workflow. This positioning centers GE HealthCare as a critical gatekeeper and enabler for the broader AI ecosystem, driving demand for its enterprise solutions as the essential conduit for AI deployment.

• Siemens Healthineers

Siemens Healthineers’ strategy emphasizes the deep, native integration of AI into its imaging devices and reading solutions. The company focuses on augmented workflow solutions through its AI-Rad Companion family of applications, which provide multi-modality imaging decision support. A key product example is the integration of AI-powered algorithms into their Biograph PET/CT scanners with the AIDAN intelligent imaging solution, featuring capabilities like FlowMotion AI and FAST PET Workflow AI. This strategy drives demand for their entire imaging hardware and software ecosystem by embedding efficiency and automation directly at the point of image acquisition and interpretation, thereby improving diagnostic outcomes and operational metrics for its customer base.

• Philips

Philips is strategically positioned with its focus on Advanced Visualization and AI automated insights designed to mitigate the effects of staff shortages and burnout. The company's product portfolio is centered around its Advanced Visualization Workspace platform and the Philips AI Manager, an end-to-end AI enablement solution. The AI Manager acts as a vendor-neutral integration point, offering access to over 100 AI applications from various contracted vendors. This approach drives demand by offering healthcare systems a comprehensive, scalable, and flexible platform solution that integrates with their existing IT infrastructure and PACS, ensuring radiologists can leverage diverse AI tools without system-wide IT overhaul or multiple vendor contracts.

US AI in Radiology Market Developments

The following represent significant, verifiable market events focused on M&A, product launches, or capacity additions in the 2024-2025 period.

• October 2025 (Partnership): RSNA Ventures and Rad AI Strategic Partnership RSNA Ventures, a subsidiary of the Radiological Society of North America (RSNA), announced a strategic partnership with Rad AI to integrate over 100 years of RSNA’s trusted peer-reviewed knowledge directly into radiologists' workflows through the Rad AI Reporting platform. This collaboration aims to leverage generative AI to provide case-based insights at the moment of image interpretation, directly addressing the pressure from rapidly rising imaging volumes and the exponentially growing body of medical knowledge by boosting reporting efficiency and confidence.
• October 2024 (Product): GE HealthCare Accelerates Enterprise Imaging AI Adoption GE HealthCare announced the integration of a third-party AI-enabled application orchestration feature, in collaboration with Blackford, into its True PACS and Centricity PACS solutions. This development is designed to provide radiologists with a single, validated platform to access a curated catalog of third-party AI applications spanning multiple clinical use cases, from mammography to lung scans. The strategic move is intended to simplify the deployment and use of AI, accelerating adoption in clinical settings to address high workloads.

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US AI in Radiology Market Scope:

US AI in Radiology Market Segmentation:

• By Technology
  • Natural Language Processing (NLP)
  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Others
• By Application
  • MRI Scan Report Generation
  • CT Scan Report Generation
  • X-Ray Report Generation
  • Ultrasound Report Generation
  • Mammography Report Generation
  • Others
• By End-User
  • Hospitals And Clinics
  • Diagnostic Imaging Centers
  • Research Institutes And Academic Centers
  • Others

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Table Of Contents

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. TECHNOLOGICAL OUTLOOK

5. US AI IN RADIOLOGY MARKET BY TECHNOLOGY

5.1. Introduction

5.2. Natural Language Processing (NLP)

5.3. Machine Learning

5.4. Deep Learning

5.5. Computer Vision

5.6. Others

6. US AI IN RADIOLOGY MARKET BY APPLICATION

6.1. Introduction

6.2. Natural Language Processing (NLP)

6.3. Machine Learning

6.4. Deep Learning

6.5. Computer Vision

6.6. Others

7. US AI IN RADIOLOGY MARKET BY END-USER

7.1. Introduction

7.2. Natural Language Processing (NLP)

7.3. Machine Learning

7.4. Deep Learning

7.5. Computer Vision

7.6. 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. Swift Medical

9.2. Wound Vision

9.3. EKare Inc.

9.4. Healthy.io

9.5. Intellicure

9.6. Spectral AI

9.7. Tissue Analytics

9.8. The Wound Pros

9.9. Healogics

10. APPENDIX

10.1. Currency

10.2. Assumptions

10.3. Base and Forecast Years Timeline

10.4. Key benefits for the stakeholders

10.5. Research Methodology

10.6. Abbreviations

LIST OF FIGURES

LIST OF TABLES

Companies Profiled

Swift Medical

Wound Vision

EKare Inc.

Healthy. io

Intellicure

Spectral AI

Tissue Analytics

The Wound Pros

Healogics

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