AI In Breast Imaging Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component (Hardware and Software), By Imaging Modality (Mammography, Ultrasound Imaging, and MRI), By Application (Screening, Diagnostics, and Image-guided Biopsy), By End Use (Hospitals & Clinics, Diagnostic Imaging Centers, and Research Institutes), By Region and Competition, 2020-2030F

Market Overview

Global AI in Breast Imaging Market was valued at USD 320.32 Million in 2024 and is expected to reach USD 441.98 Million by 2030 with a CAGR of 5.47% during the forecast period. The global AI in breast imaging market is experiencing significant growth, driven by the increasing demand for early and accurate breast cancer detection. AI technologies, particularly deep learning algorithms, are revolutionizing the diagnostic process by improving image analysis, reducing human error, and enhancing the speed of diagnosis. These advancements allow for more precise detection of tumors and abnormalities, leading to better patient outcomes. The growing prevalence of breast cancer, along with the rise in the adoption of digital imaging technologies, is contributing to the market's expansion. The integration of AI with other medical imaging modalities, such as mammography and ultrasound, further enhances the market potential. As healthcare systems increasingly prioritize personalized and efficient care, AI's role in breast imaging is expected to continue growing.

Key Market Drivers

Increasing Incidence of Breast Cancer

Breast cancer continues to be one of the most prevalent types of cancer worldwide, with millions of new cases diagnosed annually. The rising incidence of breast cancer is primarily attributed to lifestyle changes, aging populations, and environmental factors. According to the American Cancer Society, breast cancer is the second leading cause of cancer deaths among women in the United States. As the incidence of breast cancer increases, so does the need for improved diagnostic methods to detect the disease at the earliest possible stage. Early detection is critical for improving prognosis, as it allows for more effective treatment options and a higher chance of survival. According to WHO, in 2022, breast cancer led to approximately 670,000 deaths worldwide. Around half of all breast cancer cases occur in women with no distinct risk factors other than their sex and age. It was the most common cancer among women in 157 out of 185 countries that year. Breast cancer affects individuals globally, with occurrences in every country. While predominantly a women’s disease, about 0.5–1% of breast cancer cases are found in men.

Traditional breast cancer detection methods, such as mammography, although effective, have limitations. Mammograms rely heavily on the radiologist’s skill and can sometimes miss small tumors, particularly in dense breast tissue. AI technologies in breast imaging address these challenges by offering more accurate and automated solutions for detecting abnormalities, providing consistent results regardless of the radiologist’s experience level. AI algorithms, particularly deep learning models, are capable of analyzing complex medical images in a fraction of the time it takes human experts. These AI-powered systems can detect even the smallest of tumors and anomalies in mammograms, MRIs, and ultrasounds, often identifying signs of cancer at stages when traditional methods may miss them.

The increasing rate of breast cancer diagnoses worldwide is prompting healthcare providers to adopt more advanced imaging technologies to improve detection rates and minimize false negatives. With early detection proving critical to the overall success of treatment, AI’s ability to facilitate early diagnosis is propelling its integration into breast cancer screening programs globally.

Advancements in Artificial Intelligence and Machine Learning

Over the past few years, there have been substantial advancements in artificial intelligence (AI) and machine learning (ML), both of which have significantly transformed the medical imaging field. Deep learning, a subset of AI, has emerged as one of the most influential tools in this area, allowing for the automation of complex image analysis tasks. In the context of breast imaging, AI systems can analyze thousands of mammograms or ultrasounds in minutes, identifying potential areas of concern with a high degree of accuracy. These algorithms are trained on vast datasets of annotated medical images, which help the system learn to detect abnormalities, such as tumors, calcifications, or cysts, by recognizing subtle patterns that might not be apparent to the human eye.

One of the key advantages of AI in breast imaging is its ability to improve over time. As these algorithms are exposed to more data, they become more adept at detecting subtle differences in imaging patterns, making them more accurate and reliable. This continuous learning and improvement are particularly important in the fight against breast cancer, where early-stage tumors can often be hard to distinguish from healthy tissue or benign growths. AI’s capacity for self-improvement and pattern recognition is one of the key factors that make it a powerful tool in the breast imaging market. In June 2022, GE Healthcare partnered with the National Oncology Center of Singapore (NCCS) to leverage artificial intelligence and text processing. This collaboration aims to promote more personalized cancer treatment options and provide patients with the opportunity to access clinically significant information throughout their healthcare journey.

AI and ML enable more personalized and precise treatment plans. By analyzing imaging data alongside other patient information, AI systems can assist clinicians in understanding the specific characteristics of an individual’s breast cancer, which can help guide treatment decisions. These advances in AI not only improve diagnostic accuracy but also contribute to more effective and personalized care for patients. As AI technologies continue to evolve, the level of accuracy, efficiency, and speed in breast cancer detection is expected to improve even further, providing greater benefits to both clinicians and patients. The growing sophistication of these technologies is undoubtedly a key driver in the AI in breast imaging market.

Technological Advancements in Imaging Systems

Technological innovations in imaging systems have played a critical role in the widespread adoption of AI in breast imaging. Advances in digital mammography, breast ultrasound, MRI, and 3D imaging technologies have created the need for more advanced and automated tools for interpreting these complex images. AI algorithms can seamlessly integrate with these next-generation imaging systems to enhance their diagnostic capabilities. For example, 3D mammography, or tomosynthesis, captures a series of images from different angles to provide a more detailed view of the breast tissue. AI can help analyze these 3D images more effectively, detecting tumors or abnormalities that may be obscured in traditional 2D images. In November 2022, Google Health and CAD, Inc. formed a strategic partnership to integrate Google Health's AI technology into iCAD's portfolio of breast imaging AI solutions. The collaboration aimed to enhance the accuracy and accessibility of breast cancer screenings. By incorporating Google Health's mammography AI technology into CAD's products, the partnership sought to improve breast cancer detection and assess short-term personal cancer risk for the millions of individuals globally diagnosed with breast cancer each year.

Advancements in ultrasound and MRI imaging have allowed for higher-resolution images, enabling better visualization of tumors. AI systems can work with these high-quality images to provide accurate assessments of tumor size, location, and malignancy. As imaging systems continue to evolve with improved resolution, speed, and accessibility, the integration of AI into these platforms ensures that healthcare providers can maximize the benefits of these advanced imaging technologies. These technological advancements are driving the demand for AI-powered breast imaging solutions, as the industry looks for ways to make the most of the increasingly sophisticated imaging systems available.

Improved Patient Outcomes through Personalized Diagnosis

AI in breast imaging is helping to create a more personalized approach to breast cancer diagnosis and treatment. By analyzing large datasets of imaging and clinical data, AI systems can identify patterns that help clinicians understand the unique characteristics of each patient's condition. These insights can be used to develop personalized treatment plans, tailored to the specific needs of the patient.

For example, AI algorithms can assess tumor characteristics, such as its size, shape, and rate of growth, to determine whether it is more likely to be malignant or benign. This information can help oncologists decide on the most appropriate treatment approach, whether it involves surgery, chemotherapy, radiation, or a combination of therapies. AI tools can predict how a patient’s cancer is likely to respond to certain treatments, allowing for better planning and more effective care. By providing more accurate and detailed diagnostic information, AI in breast imaging improves patient outcomes by enabling earlier and more precise treatment interventions. As the healthcare industry shifts toward more personalized medicine, AI-powered breast imaging tools will play an essential role in shaping the future of breast cancer care.

Cost-Effectiveness and Healthcare Budget Efficiency

The high cost of healthcare is a significant concern worldwide, and one of the key reasons healthcare systems are seeking more cost-effective solutions. AI in breast imaging has the potential to reduce healthcare costs by improving diagnostic accuracy and reducing unnecessary follow-up tests and treatments. For instance, AI tools can help prevent false positives, which can lead to unnecessary biopsies, procedures, and emotional distress for patients. Similarly, by detecting cancer in its early stages, AI can help reduce the need for costly treatments for advanced-stage cancers, which are often much more expensive to manage. AI can improve operational efficiency in hospitals and clinics by reducing the time radiologists spend on image analysis and reporting. This reduction in workload can lead to shorter patient wait times, enabling healthcare facilities to see more patients and streamline their operations. As healthcare providers look for ways to do more with less, AI in breast imaging offers a viable solution to help reduce costs while maintaining high-quality care.

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Key Market Challenges

Data Privacy and Security Concerns

As with any technology that deals with sensitive healthcare data, AI in breast imaging faces significant challenges related to data privacy and security. Breast imaging datasets often contain highly personal medical information, and the need to protect this data from unauthorized access or breaches is paramount. Healthcare providers, hospitals, and tech companies developing AI tools must ensure compliance with stringent data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and other regional laws.

AI models are trained using vast amounts of data to ensure accuracy and reliability. This necessitates the collection and sharing of medical imaging data from multiple institutions, which can raise concerns about how the data is stored, shared, and used. If proper security measures are not in place, there is a risk that patient data could be exposed or misused. To address these concerns, robust cybersecurity frameworks, encryption, and secure data-sharing protocols need to be implemented. There must be transparency regarding how patient data is used, and patient consent must be obtained before their medical information is included in AI training datasets. The need to ensure privacy and data security is a significant challenge in the widespread adoption of AI in breast imaging, particularly as healthcare systems are increasingly reliant on digital tools.

Limited Access to High-Quality Data for Training AI Models

AI algorithms, particularly those using deep learning, require access to large, high-quality datasets for training. However, obtaining comprehensive and diverse datasets for breast imaging is a significant challenge. In many cases, data is fragmented and stored in different formats across healthcare institutions, making it difficult to compile and standardize the data for training AI models. Certain regions, particularly in developing countries, may have limited access to the types of high-quality medical imaging data needed to develop robust AI models.

Another issue is that medical imaging data is often anonymized to protect patient privacy, which can lead to the loss of valuable contextual information that could improve the accuracy of AI systems. Without sufficient data, AI algorithms may struggle to generalize across different populations, imaging systems, and clinical settings. This can lead to biases in the AI model, potentially resulting in inaccurate diagnoses, particularly for underrepresented patient groups or rarer types of breast cancer.

Key Market Trends

Growing Demand for Early Detection and Screening

The global healthcare industry is increasingly prioritizing early detection, and breast cancer screening is no exception. The earlier breast cancer is detected, the higher the likelihood of successful treatment and improved survival rates. Consequently, early detection technologies are a significant focus for healthcare systems worldwide. Routine screenings, such as mammograms, are now widely recommended for women above a certain age or those with higher risk factors, such as family history or genetic predispositions. Early screening not only helps identify cancer in its most treatable stages but also lowers overall healthcare costs by preventing the need for more aggressive treatments that are required for late-stage cancers.

All leading medical organizations in the US recommend screening mammography for women aged 40 and above. Studies have shown that screening mammography reduces breast cancer mortality by approximately 20% to 35% in women aged 50 to 69, and by a slightly lower percentage in women aged 40 to 49, with a 14-year follow-up. Despite the effectiveness of mammograms and other imaging techniques, there are challenges in detecting early-stage tumors, particularly in women with dense breast tissue, where mammograms are less sensitive. AI-powered tools can help overcome this limitation by improving image clarity, identifying cancerous areas more accurately, and providing additional insights to clinicians. AI algorithms are trained to detect early-stage cancers that might be missed in a routine manual review of mammograms, reducing the risk of false negatives.

AI technologies help streamline the workflow in breast cancer screening programs. In high-volume screening environments, where thousands of images need to be reviewed, AI tools assist radiologists by providing preliminary readings and flagging suspicious areas. This not only speeds up the screening process but also reduces the strain on healthcare providers, allowing them to focus on more complex cases or confirm the AI-generated findings. Given the growing global emphasis on early detection and preventive care, the demand for AI-enhanced breast imaging technologies will continue to grow. As healthcare providers strive for more effective screening and earlier diagnosis, AI in breast imaging represents an indispensable tool to help achieve these goals.

Shortage of Radiologists and Increasing Workload

One of the most significant challenges facing the global healthcare system is the shortage of radiologists, particularly in rural and underserved areas. According to the World Health Organization, there is an unequal distribution of medical professionals, including radiologists, with many regions facing significant shortages. As the demand for diagnostic imaging continues to rise due to an aging population and the growing incidence of diseases like cancer, radiologists are increasingly burdened with high workloads. The overwhelming volume of imaging studies that need to be reviewed can lead to delays in diagnosis and an increased risk of human error.

AI technology plays a crucial role in addressing this issue by automating the initial stages of image analysis. AI algorithms can process medical images at a much faster rate than human radiologists, reducing the time spent on routine tasks and flagging potentially problematic areas for further examination. This technology allows radiologists to prioritize cases based on severity, improving workflow efficiency and reducing the overall workload. AI can assist in reducing the rate of false positives and false negatives, which in turn can reduce the amount of time spent on follow-up procedures for patients. By supporting radiologists with AI-powered tools, healthcare systems can ensure that fewer cases are missed, and diagnostic accuracy is improved. AI also helps to alleviate the mental and emotional toll on radiologists, who are often under immense pressure to analyze large volumes of images. This combination of improved efficiency, better diagnostic outcomes, and support for overburdened medical professionals is a significant driver of AI adoption in the breast imaging market.

Segmental Insights

End Use Insights

Based on the end use segment, hospitals and clinics was dominating the landscape. These healthcare settings account for a substantial portion of the market share due to their central role in patient care, diagnostic services, and the growing need for efficient, accurate, and timely breast cancer detection. The widespread adoption of AI technologies in hospitals and clinics is driven by their ability to enhance diagnostic workflows, improve patient outcomes, and address challenges such as radiologist shortages and increasing imaging workloads.

Hospitals and clinics serve as the primary venues for breast cancer screening and diagnostic imaging. They typically have access to large volumes of patient data, including mammograms, MRIs, and ultrasounds, which makes them ideal environments for implementing AI-powered breast imaging tools. AI software can significantly improve the accuracy and speed of breast cancer diagnosis in these settings by assisting radiologists in interpreting images and identifying abnormalities that may not be immediately visible to the human eye. This is particularly valuable in the context of early-stage breast cancer detection, where AI can help identify subtle patterns in imaging data that could otherwise go unnoticed, leading to more reliable and timely diagnoses. Hospitals and clinics are increasingly prioritizing patient-centered care, and AI’s ability to reduce diagnostic errors and streamline the screening process directly aligns with this goal. AI-enhanced breast imaging tools can help reduce the time required to process and analyze images, which in turn shortens patients’ wait times and speeds up decision-making. This efficiency is especially important in busy healthcare environments, where high patient volumes can lead to delays in diagnosis and treatment.

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Regional Insights

North America was dominating the global AI in breast imaging market, primarily driven by the region’s advanced healthcare infrastructure, high adoption of cutting-edge technologies, and strong research and development capabilities. The United States, in particular, is a major contributor to this dominance, supported by a combination of factors including significant investments in healthcare, a high rate of breast cancer incidence, and an increasing focus on improving diagnostic accuracy and patient outcomes.

One of the key factors propelling North America’s leadership in the AI in breast imaging market is the mature healthcare system in countries like the U.S. and Canada. These countries have some of the most developed healthcare infrastructures in the world, with a high concentration of hospitals, clinics, and diagnostic imaging centers equipped with advanced imaging technologies such as digital mammography, MRI, and ultrasound. These facilities are increasingly integrating AI-driven tools to enhance diagnostic processes, improve efficiency, and reduce the workload on radiologists. In the U.S., AI technologies have gained traction in both urban and rural healthcare settings, where there are ongoing efforts to address the shortage of radiologists by supplementing their work with AI-assisted image analysis.

Government initiatives and funding in North America also play a crucial role in promoting the adoption of AI in healthcare. Regulatory bodies like the U.S. Food and Drug Administration (FDA) have approved several AI-based medical devices for breast cancer screening, making it easier for healthcare providers to adopt AI technologies. The FDA’s approval of AI solutions, such as those for mammogram readings, has accelerated the commercialization of AI tools in the U.S. The U.S. government’s commitment to improving early cancer detection through national screening programs and increased awareness has created a favorable environment for AI adoption. The focus on reducing healthcare costs while improving diagnostic accuracy has made AI-driven solutions an attractive proposition for hospitals and healthcare providers across the region.

Recent Developments

• In September 2023, Healthyr joined forces with Koning Health to offer advanced, non-invasive breast health solutions through at-home health tests and services. This collaboration aimed to empower women globally with cutting-edge breast imaging technology.
• In October 2023, Medicom Technologies Inc. formed a partnership with Onsite Women's Health, with a shared commitment to enhancing the patient and clinician experience. Their focus on advancing medical image exchange laid the groundwork for transformative progress in breast cancer detection.
• In October 2024, Family Medical Practice (FMP Healthcare Group) Care1 - Health Executive Center and Siemens Healthineers Vietnam officially unveiled the implementation of the MAMMOMAT Inspiration 3D mammography system, integrated with Transpara® AI, to improve the accuracy of breast cancer detection. The event was attended by 40 esteemed guests, including representatives from top media outlets, businesses, and healthcare organizations in Vietnam.
• In November 2023, GE HealthCare (Nasdaq: GEHC) introduced the MyBreastAI Suite*, a comprehensive, all-in-one platform featuring artificial intelligence (AI) applications designed to assist clinicians in breast cancer detection and enhance workflow productivity. The initial release of MyBreastAI Suite incorporates three AI applications from iCAD: ProFound AI for DBT, SecondLook for 2D Mammography, and PowerLook Density Assessment. These tools aim to support early detection, improve patient outcomes, and help radiology departments optimize operational efficiency.

Key Market Players

• Visage Imaging, Inc.
• CureMetrix, Inc.
• DeepHealth, Inc.
• GE HealthCare Technologies Inc.
• Hologic, Inc.
• Siemens Healthineers AG
• Fujifilm Holdings Corporation
• Koninklijke Philips N.V.
• iCAD, Inc.
• Medicalgorithmics SA

Report Scope:

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

• AI In Breast Imaging Market, By Component:

o   Hardware

o   Software

• AI In Breast Imaging Market, By Imaging Modality:

o   Mammography

o   Ultrasound Imaging

o   MRI

• AI In Breast Imaging Market, By Application:

o   Screening

o   Diagnostics

o   Image-guided Biopsy

• AI In Breast Imaging Market, By End Use:

o   Hospitals & Clinics

o   Diagnostic Imaging Centers

o   Research Institutes

• AI In Breast Imaging Market, By Region:

o   North America

§  United States

§  Canada

§  Mexico

o   Europe

§  France

§  United Kingdom

§  Italy

§  Germany

§  Spain

o   Asia-Pacific

§  China

§  India

§  Japan

§  Australia

§  South Korea

o   South America

§  Brazil

§  Argentina

§  Colombia

o   Middle East & Africa

§  South Africa

§  Saudi Arabia

§  UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI In Breast Imaging Market.

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Global AI In Breast Imaging market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).