AI in Medical Imaging Market Size - Global Industry, Share, Analysis, Trends and Forecast 2023 - 2032

AI in Medical Imaging Market Highlights

• Global artificial intelligence in medical imaging market revenue is poised to garner USD 23.7 billion by 2032 with a CAGR of 33.6% from 2023 to 2032
• North America AI in medical imaging market value occupied around 598 million in 2022
• Asia-Pacific AI in medical imaging market growth will record a CAGR of more than 34% from 2023 to 2032
• Among modality, the CT scan sub-segment generated over US$ 476 million revenue in 2022
• Based on application, the neurology sub-segment generated around 38% share in 2022
• Use of AI in monitoring disease progression and treatment response market trend that fuels the industry demand

The integration of artificial intelligence (AI) methods into different imaging modalities such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound to enhance and automate the processing of medical pictures is referred to as AI in medical imaging. It entails using complex algorithms and machine learning models to analyze and extract significant information from medical pictures in order to help in the identification, diagnosis, and treatment of various illnesses and ailments. AI algorithms may be trained on vast datasets of annotated medical pictures, allowing them to discover trends, anomalies, and tiny features that human radiologists may miss. Medical imaging may be converted into a more efficient and accurate process by employing AI, allowing for faster and more exact diagnosis, minimizing mistakes, and increasing patient outcomes. AI can also help radiologist’s priorities critical cases, manage workloads, and improve overall workflow efficiency in radiology units. The use of AI in medical imaging has the potential to revolutionize healthcare by giving radiologists with sophisticated decision-making tools, thereby improving patient care.

Global AI in Medical Imaging Market Dynamics

Market Drivers

• Increasing demand for efficient and accurate diagnostic tools in healthcare
• Growing prevalence of chronic diseases and the need for early detection
• Advancements in imaging technologies and data management systems
• Rising adoption of electronic health records (EHR) and integration of AI systems
• Availability of large datasets for training AI algorithms
• Supportive government initiatives and funding for AI research in healthcare

Market Restraints

• Concerns regarding data privacy and security
• Lack of standardized protocols for integrating AI systems into existing imaging workflows
• Resistance from healthcare professionals towards adopting AI technology
• High initial costs associated with implementing AI systems
• Regulatory challenges and the need for approval of AI algorithms for clinical use
• Limited interpretability and explainability of AI models, leading to potential liability issues

Market Opportunities

• Development of AI algorithms for rare diseases and complex conditions
• Integration of AI with telemedicine platforms for remote diagnosis and consultation
• Application of AI in image-guided interventions and surgical planning
• Collaboration between AI developers and healthcare institutions for research and development
• Expansion of AI applications beyond imaging, such as pathology and genomics, for comprehensive patient care

AI in Medical Imaging Market Report Coverage

AI in Medical Imaging Market Insights

The AI in medical imaging market is driven by several key factors. One key factor is the growing demand in healthcare for efficient and precise diagnostic instruments. Medical imaging is critical in illness identification and diagnosis, and AI may improve imaging modalities' capabilities by automating processing and offering sophisticated picture interpretation. The necessity for early illness identification, particularly in chronic ailments, stimulates the demand for AI in medical imaging.

Imaging technology and data management systems advancements also help to market expansion. The continual advancement of imaging modalities such as CT, MRI, and ultrasound, as well as enhanced data storage and processing capabilities, allow for the fabrication of high-quality medical pictures. These breakthroughs may be used by AI systems to extract significant information from photos and deliver precise diagnostic findings.

Moreover, the increasing use of electronic health records (EHR) and the integration of AI systems open up new prospects for AI in medical imaging. EHR systems hold massive volumes of patient data, including medical pictures that may be used to train AI algorithms. Integrating AI technology into current healthcare infrastructure enables radiologists and doctors to make better informed judgements by providing seamless access to patient information.

However, there are certain constraints in the market. Concerns about data privacy and security pose a substantial barrier to AI use in medical imaging. Medical photographs include sensitive patient information, thus protecting the privacy and security of this data is critical. Establishing strong data security measures and adhering to regulatory norms are critical for garnering confidence and widespread use of AI systems in medical imaging.

Another impediment is healthcare practitioners' reluctance to utilize AI technology. Some radiologists and physicians may be wary about AI, thinking that it may replace their knowledge or add to their burden. This opposition may be addressed by educating and training healthcare workers on the benefits and successful usage of AI in medical imaging.

The market is also confronted with regulatory obstacles. Obtaining clinical approval for AI algorithms necessitates extensive testing and validation to assure safety and efficacy. It is critical to develop standardized procedures for integrating AI systems into existing imaging operations in order to ensure smooth installation and acceptance.

Artificial Intelligence in Medical Imaging Market Segmentation

The worldwide market for AI in medical imaging is split based on technology, modality, application, end-user, and geography.

Artificial Intelligence in Medical Imaging Technologies

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

According to AI in medical imaging industry analysis, deep learning presently dominates the AI in medical imaging market. Deep learning algorithms, a subset of machine learning techniques, have demonstrated exceptional performance in analysing medical pictures and extracting useful information. Convolutional neural networks (CNNs), for example, have showed great accuracy in image classification, segmentation, and detection tasks.

Deep learning models' capacity to automatically learn detailed characteristics from big datasets has made them extremely useful in medical imaging. Deep learning algorithms can find patterns and anomalies in pictures by training on vast databases of annotated medical photos, assisting in illness identification and diagnosis. Deep learning models' versatility and adaptability allow them to learn from a variety of data sources, allowing them to handle a wide range of imaging modalities and clinical circumstances.

While natural language processing (NLP) is important in healthcare, it is not as prominent in medical imaging as deep learning. NLP is mostly utilised in clinical text data processing and analysis, such as electronic health records and medical literature. It aids with the extraction of information from unstructured clinical notes, the generation of reports, and clinical decision-making. Deep learning approaches, on the other hand, have demonstrated more effect and success in direct application to medical picture analysis.

Artificial Intelligence in Medical Imaging Modalities

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

The CT scan modality segment dominated the AI market in medical imaging. CT scans (Computed Tomography) offer comprehensive cross-sectional pictures of the body and are widely utilised in a variety of medical applications, including illness diagnosis and monitoring. CT scans produce a vast volume of image data, making them ideal for AI analysis and interpretation. Deep learning models, in particular, have demonstrated substantial success in automating tasks such as lesion identification, segmentation, and classification in CT images, resulting in enhanced diagnostic accuracy and efficiency.

However, the AI in medical imaging industry is dynamic and prone to change over time. The dominance of a certain modality segment might change based on variables such as technological improvements, research focus, and clinical demands. Other modalities such as MRI (Magnetic Resonance Imaging), X-rays, ultrasound, and nuclear imaging also have significant applications in medical imaging and may witness increased integration of AI techniques as the field continues to evolve.

Artificial Intelligence in Medical Imaging Applications

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

The use of AI in medical imaging that dominated the industry was in radiology, specifically in neurology. Neurology is concerned with the diagnosis and treatment of nervous system illnesses and diseases. AI algorithms have made substantial advances in the processing and interpretation of neurological imaging, such as MRI and CT scans, assisting in the diagnosis and characterisation of a variety of neurological illnesses such as brain tumours, strokes, and neurodegenerative diseases.

While neurology has been a notable application, AI has also made substantial advances to other fields of medical imaging. Respiratory and pulmonary imaging, including the analysis of chest X-rays and CT scans, has been a hotbed of AI research and development, notably in the diagnosis and characterisation of lung disorders including pneumonia and lung cancer.

Cardiology, which focuses on the detection and treatment of cardiac diseases, has also seen significant breakthroughs in AI applications. AI algorithms have been created to analyse cardiac imaging modalities such as echocardiograms and cardiac MRI, facilitating in the identification and evaluation of cardiac anomalies and heart function. Breast screening, particularly mammography, has also been a significant field for AI in medical imaging. AI algorithms have been created to assist in the identification and categorization of breast lesions, hence increasing the accuracy of breast cancer diagnosis.

Artificial Intelligence in Medical Imaging End-Users

• Hospitals
• Diagnostic Imaging Centers
• Others

According to the AI in medical imaging market projection, hospitals were the key end-users leading the industry. Hospitals are the principal healthcare facilities where medical imaging operations are done. They usually feature separate radiology departments that are outfitted with advanced imaging modalities including CT scanners, MRI machines, X-ray systems, and ultrasound equipment. The application of artificial intelligence (AI) in medical imaging has been especially relevant in hospital settings, where the goal is to increase diagnostic accuracy, simplify workflow efficiency, and improve patient care.

Hospitals have been at the forefront of AI technology adoption due to their access to varied patient groups and large imaging datasets. The vast quantities of medical pictures produced in hospital settings provide as significant training data for AI systems. By adopting AI, hospitals may improve medical picture processing and interpretation, resulting in better diagnosis, treatment planning, and patient outcomes.

While hospitals have led the AI in medical imaging industry, diagnostic imaging centres are becoming more interested in and using AI technology. Diagnostic imaging centres specialise in providing patients with a wide range of imaging treatments, from basic screenings to more complicated diagnostic procedures. Furthermore, the use of artificial intelligence in medical imaging is not restricted to hospitals and diagnostic imaging centres. Other healthcare environments, such as research institutes, university medical centres, and specialised clinics, contribute to the acceptance and advancement of AI technology in medical imaging. The deployment of AI algorithms in these contexts may be limited to specific research projects, clinical trials, or specialised applications.

Artificial Intelligence in Medical Imaging Market Regional Outlook

North America

• U.S.
• Canada

Europe

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

Asia-Pacific

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

Latin America

• Brazil
• Mexico
• Rest of Latin America

The Middle East & Africa

• South Africa
• GCC Countries
• Rest of the Middle East & Africa (ME&A)

AI in Medical Imaging Market Regional Analysis

North America, notably the United States, has been a significant market for artificial intelligence in medical imaging. The region offers a well-established healthcare system, cutting-edge medical imaging technology, and a regulatory framework that encourages innovation. The presence of significant market players, research institutes, and industry-academia cooperation leads to the growth of the AI in medical imaging market in this area.

Europe is also an important market for artificial intelligence in medical imaging, thanks to factors such as modern healthcare infrastructure, government efforts, and the presence of prominent medical equipment manufacturers. Countries such as Germany, the United Kingdom, and France have been early adopters of AI technology in medical imaging. Furthermore, the General Data Protection Regulation (GDPR) framework of the European Union impacts data privacy and security issues in the application of AI systems.

The AI in medical imaging industry is rapidly expanding in the Asia-Pacific region. AI technologies are increasingly being used in healthcare in countries such as China, Japan, South Korea, and India. A huge patient population, increased frequency of chronic illnesses, government expenditures in healthcare infrastructure, and developments in AI research all contribute to the region's market growth. Furthermore, growing economies in Southeast Asia show promise for expansion in AI applications in medical imaging.

AI in Medical Imaging Market Players

Some of the top AI in Medical Imaging companies offered in our report include IBM Watson Health, GE Healthcare, Siemens Healthineers, Philips Healthcare, NVIDIA Corporation, Aidoc, Butterfly Network, Zebra Medical Vision, Arterys, Hologic, Inc., Samsung Healthcare, Fujifilm Holdings Corporation, Agfa-Gevaert Group, iCAD, Inc., and MIRADA Medical Ltd.

In February 2022, GE Healthcare established a partnership with NVIDIA to advance the use of AI in medical imaging. The collaboration intends to combine NVIDIA's AI computing platform with GE Healthcare's medical imaging devices to improve picture reconstruction, analysis, and interpretation capabilities.

In December 2021, Siemens Healthineers has finalized the acquisition of Varian Medical Systems, a pioneer in cancer care solutions and radiation treatment. Siemens Healthiness will be able to increase its position in the oncology and radiation markets as well as extend its range of precision cancer care solutions as a result of the purchase.