AI in MRI Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Clinical Application (Musculoskeletal, Colon, Prostate, Liver, Cardiovascular, Neurology, Lung, Breast, Others), By Offering Type (Hardware, Software, Services), By Technology (Deep Learning, Machine Learning, Computer Vision, NLP, Speech Recognition, Querying Method, Others), By Deployment Type (On-premises and Cloud), By End Use (Hospitals, Clinics, Research & Laboratories, Others), By Region and Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 1.73 Billion
CAGR (2026-2031)	17.62%
Fastest Growing Segment	Deep Learning
Largest Market	North America
Market Size (2031)	USD 4.58 Billion

Market Overview

The Global AI in MRI Market will grow from USD 1.73 Billion in 2025 to USD 4.58 Billion by 2031 at a 17.62% CAGR. Global AI in MRI comprises machine learning and deep learning solutions integrated into magnetic resonance imaging workflows to automate image reconstruction and assist in diagnostic interpretation. The primary drivers fueling market growth include the imperative to accelerate scan durations for higher patient throughput and the rising demand to mitigate radiologist burnout amidst increasing caseloads. This trend toward technological adoption is substantiated by recent data. According to the European Society of Radiology, in 2024, 48% of surveyed members reported currently using AI tools within their clinical practice.

Nevertheless, a significant challenge potentially impeding market expansion is the high capital investment required for deployment. The substantial costs associated with purchasing software licenses and upgrading IT infrastructure create a barrier to entry for smaller independent clinics and healthcare systems in developing regions. Consequently, budgetary constraints often force facilities to delay implementation despite the operational efficiencies these tools offer.

Key Market Drivers

The critical shortage of radiologists combined with increasing imaging workloads is a primary force propelling the adoption of AI in MRI. Healthcare systems are facing a widening gap between the volume of diagnostic scans required and the available workforce to interpret them, necessitating automated solutions to prevent burnout and diagnostic delays. This disparity is highlighted by recent workforce metrics which show demand consistently outstripping supply. According to the Royal College of Radiologists, June 2024, in the '2023 Clinical Radiology Workforce Census Report', while the UK clinical radiology workforce grew by 6% in 2023, the demand for CT and MRI reporting surged by 11% during the same period, exacerbating the pressure on existing staff.

Advancements in deep learning for superior image reconstruction further catalyze market expansion by directly addressing the need for operational efficiency and faster patient throughput. Modern AI algorithms enable the reconstruction of high-fidelity images from undersampled raw data, drastically reducing the time patients must spend inside the scanner without compromising diagnostic quality. This technological capability was underscored when, according to GE HealthCare, December 2024, in the article 'GE HealthCare Expands Its Effortless Recon DL Portfolio', the company introduced Sonic DL for 3D, a deep learning innovation designed to reduce MRI scan times by up to 86%. Such potential for efficiency has attracted significant capital; according to Ezra, February 2024, in the announcement 'Ezra Raises $21M to Increase Access to Care', the company secured $21 million in new funding to accelerate the expansion of its AI-powered MRI services.

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Key Market Challenges

The high capital investment required for deployment significantly hampers the growth of the Global AI in MRI Market. Implementing these solutions involves substantial costs associated with purchasing software licenses and upgrading IT infrastructure to support data-intensive workflows. These financial requirements create a formidable barrier to entry, especially for smaller independent clinics and healthcare systems in developing regions that operate on restricted budgets. Consequently, facilities often delay adoption despite the potential for improved operational efficiency, preventing the market from realizing its full potential volume.

Recent data reinforces the impact of these economic constraints on the sector. According to the European Society of Radiology, in 2024, 49.5% of surveyed members identified costs or lack of budget as the main potential barrier to AI implementation in clinical practice. This indicates that financial hurdles are a primary reason why many radiology departments remain hesitant to integrate these tools. Without the necessary funding to cover initial expenditures, a significant portion of the market is unable to leverage AI capabilities, thereby directly restricting overall market expansion.

Key Market Trends

The Integration of Generative AI for Radiology Reporting is rapidly emerging as a critical solution to the administrative burdens overwhelming diagnostic workflows. Moving beyond traditional image analysis, this trend involves using large language models to automatically draft, customize, and structure radiological reports based on image findings and radiologist dictation, thereby reducing documentation time and radiologist fatigue. The market viability of this technology was strongly affirmed when, according to AuntMinnie, January 2025, in the article 'Rad AI raises $60M in funding round', Rad AI secured $60 million in Series C financing to accelerate the adoption of its generative AI reporting platform across major healthcare systems.

Simultaneously, the Growth of Vendor-Neutral AI App Stores is reshaping how healthcare facilities procure and deploy artificial intelligence tools. Rather than navigating fragmented contracts with individual developers, hospitals are increasingly utilizing centralized orchestration platforms that provide a single interface for managing diverse algorithms from multiple vendors. This consolidation strategy was highlighted when, according to Nasdaq, December 2025, in the article 'DeepHealth Unveils Next-Generation Imaging Informatics And Clinical AI Solutions At RSNA 2025', DeepHealth introduced AI Studio, an orchestration platform integrating over 140 AI algorithms from more than 75 vendors to streamline clinical deployment and governance.

Segmental Insights

The Deep Learning segment represents the fastest-growing category in the Global AI in MRI Market, driven by its ability to substantially improve image quality and reduce scan durations. Unlike traditional machine learning, deep learning algorithms effectively automate complex image reconstruction tasks, which enhances diagnostic confidence and patient throughput. This growth is further supported by a rise in regulatory clearances from the U.S. Food and Drug Administration for deep learning applications, validating their clinical safety and utility. Consequently, healthcare providers are increasingly adopting these solutions to optimize radiology workflows and manage higher patient volumes efficiently.

Regional Insights

North America maintains a leading position in the Global AI in MRI market, driven by the early adoption of advanced diagnostic solutions and substantial investment in healthcare infrastructure. The region’s dominance is reinforced by a supportive regulatory framework, specifically the frequent approval of AI-based algorithms by the U.S. Food and Drug Administration (FDA). This regulatory clarity encourages medical technology manufacturers to develop and launch new imaging applications. Additionally, the strong presence of key market players and research institutions facilitates the integration of artificial intelligence into radiology workflows, ensuring sustained market expansion across the region.

Recent Developments

• In December 2024, GE HealthCare launched new deep learning-based image processing solutions, including Sonic DL for 3D, at the Radiological Society of North America annual meeting. This artificial intelligence-driven application was developed to significantly accelerate magnetic resonance imaging acquisition speeds for volumetric scans across various anatomical areas, such as the brain and spine. The company reported that the technology could reduce scan times by up to 86% while maintaining high image resolution. These advancements were part of a broader portfolio intended to address radiology department challenges by improving operational efficiency and diagnostic confidence through automated workflows.
• In October 2024, Ezra announced a strategic partnership with Princeton Radiology to expand its artificial intelligence-powered full-body magnetic resonance imaging services into the Philadelphia area. This collaboration allowed the healthcare technology company to deploy its proprietary AI technology at imaging centers managed by the radiology provider. The initiative aimed to enhance early cancer detection capabilities by leveraging advanced software that assists in the analysis and reporting of screening scans. By integrating these solutions into established clinical workflows, the companies sought to make comprehensive preventative screening more accessible to patients in Pennsylvania and the surrounding regions.
• In May 2024, United Imaging unveiled the uMR Jupiter 5T, a whole-body 5 Tesla magnetic resonance imaging system, at the International Society for Magnetic Resonance in Medicine annual meeting. The company highlighted that this system transcended traditional constraints of ultra-high-field imaging, which were previously limited to specific anatomies like the brain. The new platform featured the uAIFI technology suite, an end-to-end artificial intelligence solution designed to integrate hardware and software for improved image quality and workflow. This development represented a significant advancement in the potential for ultra-high-field clinical applications across the abdomen, heart, and pelvis.
• In February 2024, Siemens Healthineers introduced the Magnetom Flow, a new 1.5 Tesla magnetic resonance imaging platform, during the European Congress of Radiology. The system was designed with a closed helium circuit and eliminated the need for a quench pipe, significantly reducing the liquid helium required for cooling. The company integrated extensive artificial intelligence-supported image reconstruction technologies into the platform to shorten measurement times and enhance image quality. This launch aimed to increase clinical efficiency and sustainability by minimizing energy consumption and installation requirements while maintaining high diagnostic standards for routine clinical practice.

Key Market Players

• Digital Diagnostics Inc.
• Tempus AI, Inc.
• Advanced Micro Devices, Inc.
• HeartFlow, Inc.
• Enlitic, Inc.
• Viz.ai, Inc.
• EchoNous Inc.
• HeartVista Inc.
• Exo Imaging, Inc.
• Nano-X Imaging Ltd.

By Clinical Application	By Offering Type	By Technology	By Deployment Type	By End Use	By Region
Musculoskeletal Colon Prostate Liver Cardiovascular Neurology Lung Breast Others	Hardware Software Services	Deep Learning Machine Learning Computer Vision NLP Speech Recognition Querying Method Others	On-premises and Cloud	Hospitals Clinics Research & Laboratories Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global AI in MRI Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• AI in MRI Market , By Clinical Application:

• Musculoskeletal
• Colon
• Prostate
• Liver
• Cardiovascular
• Neurology
• Lung
• Breast
• Others

• AI in MRI Market , By Offering Type:

• Hardware
• Software
• Services

• AI in MRI Market , By Technology:

• Deep Learning
• Machine Learning
• Computer Vision
• NLP
• Speech Recognition
• Querying Method
• Others

• AI in MRI Market , By Deployment Type:

• On-premises and Cloud

• AI in MRI Market , By End Use:

• Hospitals
• Clinics
• Research & Laboratories
• Others

• AI in MRI Market , By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE