AI In Breast Imaging Market Reach USD 5944.3 Million by 2033

Trishita Deb
Trishita Deb

Updated · Apr 3, 2024


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The global AI in Breast Imaging Market is on the brink of significant expansion, with forecasts suggesting a leap from USD 451.6 Million in 2023 to an impressive USD 5944.3 Million by 2033. This remarkable growth trajectory, with an annual compound growth rate (CAGR) of 29.4%, is fueled by a host of factors. Key among these are the advancements in AI technology, which have substantially increased diagnostic accuracy, enabled earlier detection of abnormalities, and boosted overall healthcare efficiency. The role of AI in breast imaging has become increasingly pivotal, offering healthcare professionals the tools for more precise and prompt decision-making, thereby improving patient outcomes.

However, this market is not without its challenges. Regulatory barriers and the steep costs associated with cancer treatment are notable obstacles. The stringent requirements for regulatory compliance, especially concerning patient confidentiality and data privacy, complicate the development and integration of AI algorithms. This is due to the exhaustive validation processes they must undergo. Moreover, the financial burden associated with breast cancer treatment can restrict patient access to advanced diagnostic and therapeutic technologies, thereby impeding market growth.

Geographically, North America leads the AI in Breast Imaging Market, attributed to its high breast cancer incidence rates and advanced healthcare infrastructure. Nonetheless, the Asia-Pacific region is poised for the fastest growth, driven by an increasing prevalence of breast cancer, significant healthcare investments, and technological advancements in imaging. This underscores the market’s dynamic nature and its potential to revolutionize healthcare systems globally.

Recent industry developments have seen significant collaborations and innovations aimed at refining breast imaging technologies. For instance, strategic alliances, such as that between Google Health and iCAD, are instrumental in leveraging AI to augment the precision of breast cancer screenings and risk assessments. Such partnerships are vital for market expansion, marrying medical expertise with the latest in technology to push the boundaries of diagnostic capabilities.

Noteworthy advancements include Hologic, Inc.’s unveiling of next-generation AI solutions at the 109th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA) in November 2023. Among these, the Genius AI® Detection 2.0 solution stands out for its potential to enhance breast cancer detection while significantly reducing false positives. Additionally, GE Healthcare’s introduction of the MyBreastAI Suite in November 2023 represents another leap forward. This comprehensive AI application platform is designed to support clinicians in detecting breast cancer and enhancing workflow productivity, showcasing the market’s ongoing innovation and its commitment to improving patient care through technology.

Key Takeaways

  • Market Value in 2023: The AI in Breast Imaging market was valued at USD 451.6 million, setting a foundational year for significant growth.
  • Projected Growth: With a robust CAGR of 29.4%, the market is anticipated to expand to USD 5944.3 million.
  • Leading Technology Segment: Computer Aided Detection (CAD) technology stood out as the most lucrative segment, capturing 35.1% of the market share in 2023.
  • Dominant Application: The screening process claimed the highest revenue share within the market, contributing 43.1%.
  • Primary End-User: Hospitals emerged as the main beneficiaries of AI technology in Breast Imaging, commanding 53.2% of the market.
  • Regional Leadership: North America maintained a leading position, securing 45.5% of the total market revenue, highlighting its stronghold on the market.
AI In Breast Imaging Market Growth
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AI In Breast Imaging Statistics

  • Breast cancer is the leading cause of cancer deaths in women globally, with 684,996 reported deaths in 2020.
  • Mammograms, a key breast imaging tool, have reduced breast cancer mortality by 30.0% since 1989.
  • Women undergoing mammogram screening have shown a 41.0% decrease in mortality and a 25.0% lower risk of advanced breast cancer.
  • Mammogram sensitivity ranges from 68.0%–90.0% for women aged 50, and approximately 62.0% for those aged 40–49.
  • Sensitivity drops to 57.0% for women with dense breasts but increases to 93.0% for those with high adipose tissue.
  • Mass detection and classification accuracy with AI exceeds 98.96%, enhancing radiologists’ diagnostic accuracy.
  • Microcalcification detection using CNN models achieves a classification precision of 89.32%.
  • Breast mass segmentation with AI techniques yields an average true positive rate of 91.12%.
  • AI assists in breast density assessment, achieving an AUC of 0.94-0.98, reducing inter-radiologist variation.
  • DL models assess breast density with good agreement compared to radiologists, with a kappa value of 0.67.
  • AI predicts breast cancer risk more accurately than traditional methods, aiding in early interventions.
  • Image quality enhancement techniques improve mammogram clarity, with an accuracy of up to 93.45%.
  • Breast ultrasound AI systems excel in identifying and segmenting lesions, achieving a mean DSC of 88.97%.
  • Feature extraction methods using AI improve classification performance, with an accuracy of 89.4%.
  • ProFound AI for DBT increases reader sensitivity by 8%.
  • ProFound AI for DBT boosts reader specificity by 6.9%.
  • ProFound AI for DBT reduces reading time by up to 52%.
  • Genius AI Detection 2.0, Hologic’s new AI technology reduces false-positive markings by over 70%, enhancing breast cancer detection accuracy.

Emerging Trends

  • Augmentative AI over Autonomous Systems: The trend has shifted from developing autonomous AI systems to augmentative ones, assisting radiologists rather than replacing them. AI is now focused on improving workflow efficiency, such as summarizing medical histories, communicating findings, and scheduling follow-ups, thereby enhancing the quality of care​.
  • Responsible AI Implementation: There’s a growing emphasis on developing transparent, trustworthy AI systems in radiology. Guidelines for AI integration into clinical practice are being established, promoting the disclosure of study designs, sample sizes, and algorithm intents. This ensures that AI tools are reliable and beneficial for clinical use​.
  • Multimodal, Patient-Centric Data: AI is facilitating the creation of comprehensive radiology data repositories. These collections of pre-labeled, multimodal imaging data support clinical trial enrollment decisions and offer clinicians detailed insights into patient histories, significantly advancing personalized care​.
  • Deep Learning in Breast Cancer Analysis: Deep learning techniques are revolutionizing breast cancer image analysis across various modalities, including mammography, ultrasound, and MRI. These methods excel in tasks such as classification, detection, and segmentation, offering superior accuracy over traditional techniques​.
  • Digital Breast Tomosynthesis (DBT) Adoption: DBT is becoming the standard for breast imaging due to its effectiveness in detecting more cancers and reducing false positives. Advances in AI are critical for managing the larger data volumes produced by DBT, helping to streamline the analysis and interpretation process​.
  • Supplemental Imaging for Dense Breasts: Women with dense breasts may benefit from supplemental imaging technologies, such as ultrasound or MRI, to improve cancer detection rates. AI plays a crucial role in identifying candidates for these additional screenings and interpreting the results​.
  • Role of AI in Mammography: FDA-cleared AI algorithms are improving breast density assessment and the detection of areas of interest that may suggest cancer. This automation aids radiologists by prioritizing exams and refining detection accuracy, particularly for dense breast tissue​.
  • Radiomics and Advanced AI Applications: The potential of AI to analyze medical imaging in ways beyond human capability, known as radiomics, offers exciting prospects for personalized medicine. AI’s ability to detect complex patterns could significantly impact risk assessment and treatment planning​.

Use Cases

  • Risk Assessment and Early Detection: AI algorithms are pivotal in assessing the risk and aiding in the early detection of breast cancer. By analyzing mammography data, AI can identify subtle patterns not easily detectable by human eyes, potentially leading to earlier and more accurate diagnoses. Studies have highlighted AI’s role in improving detection rates, especially in dense breast tissues where traditional mammography might fall short​.
  • Diagnostic Accuracy: AI’s application extends to enhancing diagnostic accuracy. Deep learning models, trained on vast datasets of breast imaging, assist radiologists by highlighting areas of concern and providing a second opinion. This collaborative approach between AI and human expertise aims to reduce false negatives and increase the confidence in diagnosis​.
  • Breast Density Quantification: Breast density is a significant factor in assessing breast cancer risk. AI tools can automatically quantify breast density from mammograms, providing consistent and objective measurements. This aids radiologists in patient risk stratification and in making informed decisions regarding the necessity for additional imaging or surveillance strategies​.
  • Workflow Efficiency and Triage: AI applications offer the promise of enhancing workflow efficiency by automating the triage of mammograms. By quickly identifying normal scans, AI can significantly reduce the workload on radiologists, allowing them to concentrate on more complex cases. This could lead to faster diagnosis times and potentially improve patient throughput in diagnostic settings​.
  • Treatment Response Evaluation: AI algorithms are being developed to assess the response to neoadjuvant chemotherapy, providing valuable insights into treatment efficacy. By analyzing pre- and post-treatment images, AI can quantify changes in tumor characteristics, helping clinicians tailor treatment plans to individual patient responses​.
  • Quality Improvement and Decision Support: Beyond diagnosis and risk assessment, AI is instrumental in quality improvement and decision support. It offers a consistent and objective analysis of mammograms, reducing variability and enhancing the quality of breast imaging services. AI tools also support radiologists’ decision-making processes by providing probabilistic assessments of malignancy, thereby augmenting the diagnostic workflow​.

Key Players Analysis

  • GE Healthcare has significantly contributed to the AI in the breast imaging sector through its launch of the MyBreastAI Suite. This suite is a comprehensive platform integrating artificial intelligence applications developed by iCAD to enhance breast cancer detection and streamline workflows for clinicians. It includes tools like ProFound AI for DBT, which aids in prioritizing caseloads and clinical decision-making, SecondLook for 2D Mammography to mark regions of interest, and PowerLook Density Assessment to standardize breast density assessments. These applications collectively aim to improve the early detection of breast cancer, reduce physician burnout, and enhance patient outcomes. The suite was introduced to address the growing global burden of breast cancer, which has surpassed lung cancer as the most commonly diagnosed cancer worldwide. By providing an all-in-one platform, GE Healthcare’s MyBreastAI Suite seeks to address challenges related to access, burnout, variability, equity, and cost in breast imaging. It demonstrates GE Healthcare’s commitment to leveraging AI to transform healthcare and improve mammography outcomes​.
  • Hologic Inc, has made significant strides in the AI breast imaging sector, demonstrating its leadership through the development of Genius AI Detection technology. Showcased at key industry events like ECR 2024 and RSNA 2023, this technology highlights Hologic’s innovative approach to enhancing breast cancer detection and improving the radiological workflow. Genius AI Detection 2.0, a deep-learning solution, significantly reduces false-positive markings by over 70% compared to its predecessors, thus bolstering radiologist confidence and promising more accurate early cancer detection. Beyond these achievements, Hologic is exploring further advancements in AI, including real-time image feedback and smart triage features, underlining the transformative potential of AI in breast health management and its commitment to advancing women’s health globally​.
  • Gamma Medica, Inc., based in Salem, NH, US, is revolutionizing breast imaging technology with its LumaGEM® Molecular Breast Imaging (MBI) system, the first FDA-cleared technology of its kind, designed to enhance early breast cancer detection in women with dense breast tissue. Achieving over 90% sensitivity and specificity, this fully solid-state digital imaging system significantly improves cancer detection rates, particularly benefitting those for whom traditional mammography is insufficient. The LumaGEM® MBI system utilizes advanced dual-head Digital Direct Conversion Gamma Imaging (DDCGI) technology to identify tumors as small as 5 mm, dramatically reducing false positives and enhancing detection. Highlighted at RSNA 2015 and backed by studies, it has been shown to nearly quadruple detection rates of invasive breast cancer alongside annual mammograms, reduce biopsy needs by half, and lower detection costs by 15% compared to mammography alone, marking a significant leap forward in breast cancer diagnostics.
  • Siemens Healthineers is at the forefront of integrating Artificial Intelligence (AI) into breast imaging, with advancements spanning hardware and software solutions. The 16-Channel AI Breast Coil enhances image quality for high-resolution 2D and 3D magnetic resonance breast imaging, offering reduced scan times and compatibility with Siemens’ MAGNETOM series MR systems. On the software side, the syngo. Breast Care platform incorporates AI-based decision support features to assist clinicians in interpreting mammograms more efficiently, particularly in breast cancer screening. Developed in collaboration with ScreenPoint Medical, these features utilize deep learning algorithms trained on over 1 million images, enabling automatic sorting and scoring of cases for prioritization. Siemens Healthineers also hosts discussions on AI’s role in breast imaging, emphasizing its potential to reduce workload and recall rates while addressing associated challenges and concerns, ultimately striving to enhance diagnostic accuracy and patient outcomes in breast cancer screening and diagnostics.
  • Fujifilm Holdings Corp. is revolutionizing the AI in Breast Imaging sector through its innovative technologies that significantly enhance the efficiency and accuracy of breast cancer detection. Leveraging the Synapse AI Platform, notably implemented across University Radiology Group’s 37 facilities, Fujifilm has demonstrated a commitment to improving diagnostic decisions and workflow efficiency for healthcare providers and patients alike​. In collaboration with ScreenPoint Medical, Fujifilm’s integration of the FDA-cleared Transpara powered by Fusion AI into its ASPIRE Cristalle mammography system further exemplifies its strides in early cancer detection. This system is designed to aid radiologists by improving reading accuracy, reducing reading times, and optimizing workflow through features like Transpara Perception Aid and Score, which assist in prioritizing mammograms based on the likelihood of cancer​. Moreover, Fujifilm’s comprehensive approach extends to its Synapse AI Orchestrator, which integrates Fujifilm and third-party imaging algorithms within the Synapse PACS workflow. This not only aims to enhance data security and infrastructure efficiency but also underscores Fujifilm’s holistic strategy in harnessing AI to provide superior patient care across its medical imaging solutions​.
  • Toshiba Corporation, with its strategic reorganization into three standalone entities announced in November 2021, has showcased its agility and focus on enhancing shareholder value while aiming for market leadership in its diverse technological domains. This restructuring aligns Toshiba’s vast array of technologies—including AI-driven anomaly detection, behavior recognition, and deep learning applications for health and safety—with the goal of honing its competitive edge across various sectors. Though specific initiatives within the AI in Breast Imaging sector were not detailed in the available sources, Toshiba’s broad engagement in AI and technology innovation underscores its capacity to influence advancements in medical imaging. The firm’s commitment to leveraging its technological prowess post-reorganization indicates a solid foundation for potential contributions to healthcare, particularly in enhancing medical imaging techniques and applications​.
  • Aurora Imaging Technology, Inc., based in Danvers, Massachusetts, is at the forefront of the fight against breast cancer, leveraging advanced imaging technology. The company has achieved a significant milestone with the development of the Aurora® 1.5Tesla Dedicated Breast MRI System, the only FDA-cleared system designed specifically for 3-D bilateral breast imaging. This innovative system demonstrates Aurora’s commitment to improving breast cancer detection, diagnosis, and treatment through advanced technology. The addition of AuroraSPECTROSCOPY™ to its Breast MRI system, which has received FDA 501(k) clearance, underscores Aurora’s ongoing efforts to enhance the specificity of breast imaging. By enabling in vivo breast MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI), AuroraSPECTROSCOPY™ supplements the Breast MRI’s capabilities, furthering the potential for early detection and precise treatment planning in breast disease management​.


In conclusion, the AI in Breast Imaging Market is set to undergo a transformative expansion, driven by technological advancements and a growing recognition of its potential to enhance healthcare outcomes. Despite facing challenges such as regulatory hurdles and high treatment costs, the integration of AI into breast imaging represents a promising frontier for improving diagnostic accuracy, early detection, and patient care. Geographical regions like North America and Asia-Pacific are at the forefront of this growth, reflecting a global shift towards embracing these innovative solutions.

Recent collaborations between key industry players and groundbreaking technological developments underscore the market’s dynamic evolution. As AI technologies continue to advance, their application in breast imaging is expected to significantly impact the early detection and treatment of breast cancer, ultimately leading to better health outcomes for patients worldwide. This market’s progression signifies a pivotal moment in healthcare, highlighting the critical role of AI in shaping the future of breast imaging and cancer care.

Trishita Deb

Trishita Deb

Trishita has more than 7 years of experience in market research and consulting industry. She has worked in various domains including healthcare, consumer goods, and materials. Her expertise lies majorly in healthcare and has worked on more than 400 healthcare reports throughout her career.