AI In Medical Imaging Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Technology (Deep Learning, Natural Language Processing, and Others), By Application (Neurology, Respiratory & Pulmonary, Cardiology, Breast Screening, Orthopedics, and Others), By Modalities (CT scan, MRI, X-rays, Ultrasound, and Nuclear Imaging), By End Use (Hospitals, Diagnostic Imaging Centers, and Others), By Region and Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 1.65 Billion
CAGR (2026-2031)	17.54%
Fastest Growing Segment	Neurology
Largest Market	North America
Market Size (2031)	USD 4.35 Billion

Market Overview

The Global AI In Medical Imaging Market will grow from USD 1.65 Billion in 2025 to USD 4.35 Billion by 2031 at a 17.54% CAGR. Artificial Intelligence in medical imaging involves the application of machine learning and deep learning algorithms to analyze diagnostic scans, such as X-rays, CTs, and MRIs, for the purpose of identifying pathologies and quantifying physiological data. The market expansion is primarily driven by the escalating volume of imaging data which necessitates automated solutions to mitigate radiologist workload burnout and improve throughput. Furthermore, the imperative for early disease detection and the transition toward value-based healthcare incentivize the adoption of these technologies to enhance diagnostic accuracy and operational efficiency.

Despite this progress, the market faces a significant impediment regarding the seamless integration of these tools into existing clinical workflows and electronic health record systems, often creating interoperability silos that delay deployment. This concentration of development is evident in regulatory activity. According to the American College of Radiology, in 2024, radiology applications constituted nearly 80 percent of all FDA-cleared medical AI algorithms. This high density of clearance highlights the sector's rapid development yet underscores the critical challenge of validating and implementing these numerous models effectively in diverse clinical environments.

Key Market Drivers

The critical shortage of radiologists and skilled imaging professionals acts as a primary accelerant for the adoption of artificial intelligence in diagnostic workflows. As the disparity between the escalating volume of medical imaging and the available workforce widens, healthcare institutions are increasingly compelled to integrate automated solutions that prioritize urgent cases and alleviate administrative burdens. This resource constraint is quantifiably severe in major healthcare systems; for instance, according to the Royal College of Radiologists, June 2024, in the 'Clinical Radiology Census 2023', the United Kingdom faces a projected 30 percent shortfall in clinical radiologists by 2028 if current retention and recruitment trends persist. Consequently, hospitals are leveraging AI not merely for clinical enhancement but as an operational necessity to maintain continuity of care and manage backlog pressures effectively.

Concurrently, the surge in venture capital investment and government funding is facilitating the rapid transition of algorithmic concepts into commercially viable products. This influx of capital allows developers to refine deep learning models and navigate complex regulatory pathways, thereby ensuring that robust tools reach the market faster. A notable example of this financial momentum is evident in recent corporate activities; according to Rad AI, May 2024, in the 'Series B Funding Announcement', the company secured $50 million to advance its generative AI technologies specifically designed to automate radiology reporting. Such investments are directly correlating with increased product availability. Highlighting this trend, according to the U.S. Food and Drug Administration, in 2024, the agency confirmed that the cumulative number of authorized AI and machine learning-enabled medical devices had surpassed 950, demonstrating the tangible impact of capital infusion on market supply.

Download Free Sample Report

Key Market Challenges

The integration of AI tools into existing clinical workflows and electronic health record systems presents a substantial barrier to the "Global AI In Medical Imaging Market". Although algorithms offer diagnostic value, they often function as isolated silos that do not communicate effectively with Picture Archiving and Communication Systems (PACS). This fragmentation compels radiologists to toggle between disparate applications to access AI insights, creating administrative friction that negates the efficiency gains of automation. Consequently, clinicians face increased cognitive load and are hesitant to adopt solutions that disrupt their established reading routines.

This lack of interoperability directly impedes market expansion by stalling facility-wide deployment. Healthcare providers are reluctant to invest in technologies that require complex, custom configurations or fail to embed results directly into patient history files, leading to elongated procurement cycles. According to the European Society of Radiology, in 2024, 24 percent of radiology professionals identified information technology and systems integration as a primary barrier to implementing AI in clinical practice. Without plug-and-play compatibility, AI innovations struggle to transition from pilot programs to scalable, revenue-generating operations, thereby limiting the sector's growth potential.

Key Market Trends

The Adoption of Generative AI for Synthetic Data Generation and Image Reconstruction is fundamentally reshaping the market by overcoming data scarcity and enhancing scan quality. Unlike traditional diagnostic algorithms that require vast labeled datasets, generative models are now deployed to create high-fidelity synthetic images for training purposes, effectively mitigating privacy concerns and dataset bias. Furthermore, this technology is revolutionizing image reconstruction, enabling the production of diagnostic-quality scans from low-dose inputs, which significantly reduces patient radiation exposure and accelerates MRI acquisition times. This strategic shift towards generative capabilities is evident in rapid industry adoption; according to NVIDIA, March 2025, in the 'State of AI in Healthcare and Life Sciences: 2025 Trends' report, 54 percent of healthcare organizations reported actively using generative AI workloads, signaling a departure from purely analytical models toward creative data solutions.

Simultaneously, the Expansion of AI-Driven Workflow Automation and Triage Solutions is emerging as a critical response to the operational saturation of radiology departments. Beyond pixel-level diagnosis, these systems are increasingly tasked with orchestrating the entire radiological value chain, from automated protocol selection to the dynamic prioritization of critical cases in worklists. This trend prioritizes the reduction of cognitive load and administrative burnout over mere diagnostic sensitivity, ensuring that urgent pathologies are flagged immediately for radiologist review. The universal recognition of this operational imperative is highlighted by recent leadership sentiment; according to the Journal of the American College of Radiology, January 2025, in the study 'Artificial Intelligence in Radiology: A Leadership Survey', 100 percent of responding academic radiology chairs indicated plans to implement AI specifically to improve departmental quality and operational efficiency.

Segmental Insights

The Neurology segment is recognized as the fastest-growing category within the Global AI in Medical Imaging Market. This rapid expansion is driven by the rising prevalence of neurological disorders and the urgent requirement for precise early diagnosis. Regulatory support plays a significant role, as the U.S. FDA has granted clearance to multiple AI algorithms designed to detect time-sensitive conditions such as strokes and intracranial hemorrhages. Consequently, healthcare providers are increasingly adopting these tools to analyze complex brain imagery, aiming to minimize diagnostic errors and expedite treatment decisions for patients.

Regional Insights

North America maintains a leading position in the global AI in medical imaging market, primarily driven by established healthcare infrastructure and the widespread adoption of digital diagnostic technologies. This market dominance is significantly supported by a robust regulatory framework, characterized by the frequent clearance of new algorithms by the U.S. Food and Drug Administration. Furthermore, the concentration of key industry players and substantial investments in research facilitate constant product development. These elements create a favorable environment for healthcare providers to integrate artificial intelligence solutions into routine clinical workflows across the region.

Recent Developments

• In November 2025, Siemens Healthineers launched a new suite of artificial intelligence-enabled services and simulation tools at the RSNA 2025 annual meeting. The company introduced AI solutions designed to support radiologists by automatically summarizing clinically relevant observations and streamlining the reporting process. Additionally, they unveiled an operational digital twin that utilizes AI to simulate complex hospital scenarios and recommend workflow improvements. These innovations were developed to address the growing demand for diagnostic imaging and the accompanying pressure on healthcare staff, ultimately aiming to reduce cognitive load and enhance departmental productivity.
• In March 2025, GE HealthCare deepened its partnership with NVIDIA to accelerate the development of autonomous medical imaging systems. The collaboration utilized NVIDIA’s advanced computing platforms to train and tune AI models for X-ray and ultrasound devices, aiming to create "physical AI" that enhances device perception and operation. These autonomous capabilities were designed to assist technicians by automating complex workflows, such as image capture and analysis, thereby alleviating workforce shortages in radiology. The companies highlighted that this technology would help democratize access to diagnostic imaging by making systems easier to use and more efficient in diverse clinical settings.
• In December 2024, Royal Philips expanded its strategic collaboration with Amazon Web Services (AWS) to advance the deployment of artificial intelligence in the cloud for integrated diagnostics. This development, highlighted during the RSNA 2024 annual meeting, involved scaling generative AI applications across radiology, pathology, and cardiology workflows. By combining Philips' imaging expertise with AWS's cloud infrastructure, the partnership aimed to streamline clinical data management and deliver actionable insights to healthcare providers. The initiative focused on reducing administrative burdens and enhancing operational efficiency, allowing clinicians to focus more on precision diagnosis and effective patient treatment.
• In April 2024, Bayer and Google Cloud announced a collaboration to develop artificial intelligence solutions specifically designed for the medical imaging sector. The partnership focused on leveraging Google Cloud's generative AI technology to enhance Bayer's innovation platform, enabling the accelerated development and deployment of compliant AI-powered applications for radiologists. This initiative aimed to address the challenges of building scalable medical imaging software by ensuring robust data security while managing complex clinical data. The companies stated that these tools would assist healthcare professionals in analyzing massive datasets, ultimately saving time and improving diagnostic accuracy for patient care.

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 Technology	By Application	By Modalities	By End Use	By Region
Deep Learning Natural Language Processing Others	Neurology Respiratory & Pulmonary Cardiology Breast Screening Orthopedics Others	CT scan MRI X-rays Ultrasound Nuclear Imaging	Hospitals Diagnostic Imaging Centers Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

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

• AI In Medical Imaging Market, By Technology:

• Deep Learning
• Natural Language Processing
• Others

• AI In Medical Imaging Market, By Application:

• Neurology
• Respiratory & Pulmonary
• Cardiology
• Breast Screening
• Orthopedics
• Others

• AI In Medical Imaging Market, By Modalities:

• CT scan
• MRI
• X-rays
• Ultrasound
• Nuclear Imaging

• AI In Medical Imaging Market, By End Use:

• Hospitals
• Diagnostic Imaging Centers
• Others

• AI In Medical Imaging 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