Industry Outlook

Artificial Intelligence (AI) in Medical Imaging Market accounted for USD 1.4 Billion in 2024 and is expected to reach USD 30.3 Billion by 2035, growing at a CAGR of around 31.8% between 2025 and 2035. AI in medical imaging utilizes advanced algorithms, particularly machine learning and deep learning, to interpret and analyze medical images such as X-rays, CT scans, MRI scans, and ultrasounds. The primary purpose is to enhance diagnostic accuracy, speed up image interpretation, and improve patient outcomes by identifying abnormalities, assisting in the early detection of diseases, and providing quantitative analysis that aids in treatment planning. AI-powered tools can also automate repetitive tasks for radiologists, allowing them to focus more on complex cases and patient care. This technology holds promise for revolutionizing medical imaging by augmenting clinical decision-making and optimizing healthcare delivery.

Report Scope:

Market Dynamics

Integration of AI in Radiology is the Driver for Growth

The integration of AI in radiology represents a significant driver for the AI in medical imaging market. AI technologies are revolutionizing radiology by enhancing diagnostic accuracy, speeding up image interpretation, and improving workflow efficiency for healthcare professionals. These advancements are particularly crucial in addressing the growing demand for faster and more precise diagnoses, ultimately leading to improved patient outcomes. As AI continues to evolve, its application in radiology is expected to play a pivotal role in transforming how medical imaging is utilized and interpreted across healthcare systems worldwide.

High Cost Is One Of The Restraints

Healthcare providers, especially smaller institutions and those in resource-constrained settings, face additional financial burdens related to ongoing maintenance and updates of AI systems. These expenses include recurrent costs for training and retraining AI models, integrating new technologies with existing IT systems, and ensuring compliance with evolving regulatory standards. Moreover, the need for specialized technical expertise to manage and optimize AI applications further escalates operational expenses. Addressing these financial challenges is crucial to democratizing access to AI-driven medical imaging technologies and ensuring equitable healthcare delivery across diverse healthcare settings.

Remote and Telehealth Applications

Advancements in AI technology is revolutionizing remote and telehealth applications in medical imaging by overcoming traditional barriers of geographical distance and limited access to specialized healthcare services. AI-powered imaging solutions enable healthcare providers to conduct diagnostic procedures such as MRI, CT scans, and X-rays remotely, transmitting images securely for interpretation by experts located elsewhere. This capability is particularly crucial for underserved or remote areas where access to radiologists and medical specialists is limited. By facilitating timely and accurate diagnoses through telehealth platforms, AI helps improve patient outcomes and reduces the need for patients to travel long distances for medical consultations.

Industry Experts Opinion

The current state of the technology is such that Al is a useful adjunct to radiologists in image interpretation and allows radiologists to improve their efficiency and quality of care in other parts of the imaging lifecycle."

• Steven L Blumer, Associate Medical Director of Radiology Informatics, UPMC

Segment Analysis

Based on the Modality: MRI (Magnetic Resonance Imaging) is currently dominant in AI applications within medical imaging. MRI generates highly detailed images of the body's internal structures using strong magnetic fields and radio waves, making it crucial for diagnosing various conditions such as tumors, neurological disorders, and musculoskeletal issues. AI in MRI imaging leverages advanced algorithms to analyze complex data and enhance image quality, aiding radiologists in accurate diagnosis and treatment planning.

Based on the Technology: Deep Learning is currently the dominant sector in AI applications within medical imaging. Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in analyzing medical images such as CT scans, MRI scans, X-rays, and ultrasound images. These algorithms excel in feature extraction and pattern recognition, enabling them to detect abnormalities, classify diseases, and assist radiologists in making accurate diagnoses.

Regional Analysis

The Asia Pacific region's AI in the medical imaging market is experiencing rapid growth fueled by advancements in healthcare technology and increasing adoption of artificial intelligence solutions. Countries like China, Japan, and India are leading the market expansion, driven by rising healthcare investments, the growing prevalence of chronic diseases, and improving healthcare infrastructure. AI technologies such as deep learning and computer vision are being integrated into medical imaging systems to enhance diagnostic accuracy, optimize workflow efficiency, and support personalized patient care. The region's diverse healthcare landscape and proactive regulatory initiatives are further propelling the adoption of AI in medical imaging, positioning the Asia Pacific as a key hub for innovation in this field.

Competitive Landscape

The field of AI in medical imaging is rapidly evolving, with several key players leading the way in developing innovative technologies and applications. IBM Watson Health is notable for its Watson Imaging platform, which utilizes AI to analyze medical images and assist radiologists in making diagnoses. GE Healthcare's Edison AI platform integrates AI and machine learning into medical imaging devices, aiming to improve diagnostic accuracy and efficiency. NVIDIA's Clara platform provides tools for developers and researchers to create AI-powered medical imaging analytics applications. Google Health has been applying AI to medical imaging through projects like DeepMind Health, focusing on developing algorithms for diagnosing diseases from medical images. Siemens Healthineers offers AI-Rad Companion, an AI-powered software assistant for radiologists to interpret medical images and automate routine tasks. These companies are driving advancements in AI technology to enhance diagnostic accuracy, streamline workflows for healthcare professionals, and ultimately improve patient outcomes in the field of medical imaging.

Artificial Intelligence (AI) in Medical Imaging Market, Company Shares Analysis, 2024

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Recent Developments:

• In July 2024, Eden, a healthcare technology company specializing in generative artificial intelligence (AI) for medical imaging and diagnostics, secured $10 million in its latest funding round.
• In July 2024, Sweden-based image enhancement developer ContextVision is forging clinical and commercial partnerships to broaden its presence in the data quality market.
• In July 2024, NVIDIA Launches Comprehensive Suite of Healthcare Microservices to Advance AI-Driven Medical Innovations

Report Coverage:

By Modality

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

By Technology

• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Others

 By Application

• Breast Imaging
• Respiratory and Pulmonary
• Neurology
• Orthopedics
• Others

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

List of Companies:

• GE Healthcare
• Microsoft
• Butterfly Network, Inc.
• Canon Medical Systems USA, Inc.
• Viz.ai, Inc.
• EchoNous, Inc.
• HeartFlow, Inc.
• Enlitic, Inc.
• HeartVista Inc.
• Exo Imaging, Inc
• Digital Diagnostics Inc. (formerly known as Butterfly Network)
• Siemens Healthineers AG
• Nanox Imaging Ltd.
• Advanced Micro Devices, Inc.
• ContextVision (Image Enhancement Developer)
• Fujifilm Medical Systems USA, Inc.
• Agfa-Gevaert Group
• Carestream Health
• Koninklijke Philips N.V. (Philips Healthcare)
• Zebra Medical Vision

Frequently Asked Questions (FAQs)