Table of Contents
Introduction
Global AI In Breast Imaging Market size is expected to be worth around USD 5944.3 Million by 2033, from USD 451.6 Million in 2023, growing at a CAGR of 29.4% during the forecast period from 2024 to 2033. In 2023, North America reaching a revenue total of US$ 205.4 Million.
Significant advancements in AI technology are driving this market growth, substantially improving diagnostic accuracy, enabling earlier detection of abnormalities, and enhancing overall healthcare efficiency. AI’s role in breast imaging has become increasingly critical, equipping healthcare professionals with tools for precise and timely decision-making, ultimately improving patient outcomes.
Despite these advancements, the market faces several challenges. Regulatory barriers and the high costs associated with cancer treatment remain significant obstacles. Stringent compliance requirements related to patient confidentiality and data privacy add complexity to the development and integration of AI algorithms, as they require exhaustive validation processes. Additionally, the financial burden of breast cancer treatment limits patient access to advanced diagnostic and therapeutic technologies, thereby hindering market growth.
Geographically, North America dominates the AI in Breast Imaging Market due to its high breast cancer incidence rates and advanced healthcare infrastructure. However, the Asia-Pacific region is expected to witness the fastest growth, driven by rising breast cancer prevalence, increased healthcare investments, and technological progress in imaging. These regional dynamics highlight the market’s evolving nature and its potential to transform global healthcare systems.
Recent developments in the industry underscore a focus on collaborations and innovations to refine breast imaging technologies. For instance, strategic alliances such as the partnership between Google Health and iCAD demonstrate the potential of AI to improve the accuracy of breast cancer screenings and risk assessments. These partnerships merge medical expertise with cutting-edge technology, accelerating market expansion.
Key advancements include Hologic, Inc.’s launch 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 ability to enhance breast cancer detection while reducing false positives significantly.
Similarly, GE Healthcare’s introduction of the MyBreastAI Suite in November 2023 represents a major innovation. This comprehensive AI platform supports clinicians in detecting breast cancer and optimizing workflow efficiency, emphasizing the market’s commitment to improving patient care through technological advancements.
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 Statistics
- Breast cancer is the leading cause of cancer deaths among women globally, with 684,996 deaths reported in 2020.
- Mammograms, a critical breast imaging tool, have reduced breast cancer mortality by 30% since 1989.
- Women undergoing regular mammogram screenings experience a 41% decrease in mortality and a 25% lower risk of advanced breast cancer.
- Mammogram sensitivity ranges between 68% and 90% for women aged 50 and approximately 62% for women aged 40–49.
- Sensitivity drops to 57% for women with dense breast tissue but increases to 93% for those with high adipose tissue.
- AI achieves mass detection and classification accuracy exceeding 98.96%, significantly improving diagnostic precision for radiologists.
- Microcalcification detection using convolutional neural networks (CNN) reaches a classification precision of 89.32%.
- Breast mass segmentation with AI yields an average true positive rate of 91.12%, enhancing detection rates.
- AI-based breast density assessment achieves an area under the curve (AUC) of 0.94–0.98, minimizing inter-radiologist variability.
- Deep learning models for breast density assessment show good agreement with radiologists, with a kappa value of 0.67.
- AI predicts breast cancer risk more accurately than traditional methods, enabling earlier interventions.
- Image quality enhancement techniques improve mammogram clarity, achieving up to 93.45% accuracy.
- AI systems for breast ultrasound achieve a mean Dice similarity coefficient (DSC) of 88.97% in lesion identification and segmentation.
- Feature extraction methods using AI improve classification performance, reaching 89.4% accuracy.
- ProFound AI for digital breast tomosynthesis (DBT) increases reader sensitivity by 8%.
- ProFound AI for DBT improves reader specificity by 6.9%.
- ProFound AI for DBT reduces reading time by up to 52%, streamlining workflows.
- Genius AI Detection 2.0 from Hologic reduces false-positive markings by over 70%, enhancing detection accuracy for breast cancer.
Emerging Trends
- Shift to Augmentative AI: The focus in AI development has moved from creating autonomous systems to augmentative ones that support radiologists. These systems enhance workflow by summarizing medical histories, communicating findings, and scheduling follow-ups, improving overall care quality.
- Responsible AI Integration: Emphasis on transparency and trust in AI systems is growing. New guidelines promote disclosure of study designs, sample sizes, and algorithm purposes, ensuring that AI tools are reliable and effective in clinical practice.
- Multimodal, Patient-Centric Data: AI is enabling the creation of robust radiology data repositories. These repositories support clinical trial enrollments and provide clinicians with detailed patient histories, advancing personalized care.
- Deep Learning in Breast Imaging: Deep learning is transforming breast cancer imaging across mammography, ultrasound, and MRI. It excels in classification, detection, and segmentation tasks, surpassing traditional methods in accuracy.
- Digital Breast Tomosynthesis (DBT) Adoption: DBT is becoming the standard in breast imaging due to its ability to detect more cancers and reduce false positives. AI advancements streamline the analysis of large data volumes generated by DBT.
- Supplemental Imaging for Dense Breasts: AI supports identifying women with dense breast tissue for supplemental imaging such as ultrasound or MRI, improving detection rates and aiding in accurate interpretations.
- AI’s Role in Mammography: FDA-cleared AI algorithms improve breast density assessments and cancer detection. Automation prioritizes cases for radiologists and enhances accuracy, particularly for dense breast tissue.
- Radiomics and Advanced AI: AI’s ability to analyze imaging data beyond human capabilities, known as radiomics, holds potential for personalized medicine. It can detect complex patterns for improved risk assessment and treatment planning.
Use Cases
- Risk Assessment and Early Detection: AI algorithms analyze mammograms to identify subtle patterns undetectable by humans, improving early diagnosis rates, particularly in dense breast tissues.
- Enhanced Diagnostic Accuracy: AI supports radiologists by highlighting areas of concern and offering second opinions, reducing false negatives and increasing diagnostic confidence.
- Breast Density Quantification: AI tools provide consistent, objective breast density measurements from mammograms, helping stratify patient risk and guide additional imaging decisions.
- Workflow Efficiency: AI triages mammograms by quickly identifying normal scans, reducing radiologists’ workloads and enabling faster diagnosis of complex cases, improving patient throughput.
- Treatment Response Evaluation: AI evaluates tumor response to treatments such as neoadjuvant chemotherapy by analyzing pre- and post-treatment images, helping tailor patient-specific plans.
- Quality Improvement and Decision Support: AI enhances imaging quality through consistent analysis and supports radiologists with probabilistic malignancy assessments, reducing variability and improving diagnostic workflows.
Key Players Analysis
- GE Healthcare: The MyBreastAI Suite integrates AI tools like ProFound AI for DBT, SecondLook for 2D Mammography, and PowerLook Density Assessment. These tools improve breast cancer detection, streamline workflows, and address challenges like physician burnout and variability, enhancing patient outcomes globally.
- Hologic Inc.: The Genius AI Detection 2.0 reduces false positives by over 70%, improving radiologists’ confidence and accuracy. Showcased at RSNA 2023, it highlights Hologic’s commitment to advancing breast health through AI innovations.
- Gamma Medica, Inc.: Its FDA-cleared LumaGEM MBI system detects tumors as small as 5 mm, achieving over 90% sensitivity and specificity. This technology significantly improves detection rates, particularly in dense breast tissue, while reducing false positives and biopsies.
- Siemens Healthineers: Innovations include the AI Breast Coil for enhanced MRI image quality and the syngo. Breast Care platform, which uses deep learning for improved mammogram interpretation. These advancements aim to enhance diagnostic accuracy and reduce radiologists’ workload.
- Fujifilm Holdings Corp.: Through its Synapse AI Platform and ASPIRE Cristalle system with Transpara AI, Fujifilm improves breast cancer detection and workflow efficiency. Integration with Synapse PACS further optimizes imaging processes and patient care.
- Toshiba Corporation: With its focus on AI and technology innovation post-reorganization, Toshiba is poised to enhance medical imaging techniques, leveraging its expertise in anomaly detection and deep learning for potential healthcare advancements.
- Aurora Imaging Technology, Inc.: The Aurora 1.5T Dedicated Breast MRI System and AuroraSPECTROSCOPY enhance breast cancer detection and treatment precision, showcasing Aurora’s commitment to advancing breast imaging technology.
Recent Developments in AI in Breast Imaging
- April 2024: GE Healthcare launched an AI platform integrated into its mobile mammography screening truck, showcased at SBI2024. This platform enhances breast cancer detection and provides precise, individualized patient care through advanced screening processes.
- April 2024: Siemens Healthineers received FDA clearance for the Mammomat B.brilliant system. This mammography system offers improved ergonomics, automated positioning, and enhanced patient comfort, simplifying alignment during exams.
- April 2024: Fujifilm announced its “VISION2030” management plan, targeting record-high sales and substantial investments in growth areas like Bio-CDMO and Semiconductor Materials, potentially supporting advancements in AI-driven breast imaging.
- March 2024: Toshiba Corporation introduced an AI-enhanced breast imaging solution with deep learning algorithms to improve diagnostic accuracy and early cancer detection.
- January 2024: Gamma Medica launched an upgraded LumaGEM Molecular Breast Imaging (MBI) system. The new version includes enhanced AI algorithms and comfort features, improving early detection for women with dense breast tissue.
- December 2023: Toshiba Corporation announced a strategic collaboration with an AI technology firm to integrate AI tools for risk assessment and personalized patient care, advancing precision medicine in breast imaging.
- November 2023: GE Healthcare launched the MyBreastAI suite at RSNA 2023, featuring tools like ProFound AI for DBT, SecondLook for 2D Mammography, and PowerLook Density Assessment, aimed at reducing radiologists’ workloads and improving accuracy.
- November 2023: Hologic unveiled Genius AI Detection 2.0 at RSNA 2023. This technology reduces false-positive markings by over 70%, enhancing radiologists’ confidence and improving detection accuracy.
- November 2023: Aurora was recognized for its contributions to AI-driven breast imaging, which has significantly improved diagnostic accuracy and streamlined imaging workflows for better patient outcomes.
- October 2023: Fujifilm acquired Entegris’ semiconductor process chemicals business for $700 million, expanding its capabilities in materials innovation and potentially supporting AI imaging advancements.
- September 2023: Hologic partnered with Bayer to offer contrast-enhanced mammography (CEM) solutions. This collaboration combines Hologic’s mammography systems with Bayer’s injection systems, improving breast cancer detection in multiple regions.
- September 2023: Siemens Healthineers introduced the Mammomat B.brilliant at a European congress. This system combines a wide-angle X-ray tube with AI-powered reconstruction software for high-resolution 3D imaging in five seconds.
- March 2023: Gamma Medica partnered with healthcare providers to integrate its AI-driven LumaGEM® MBI system into clinical use, expanding its role in improving breast cancer screening efficiency.
- February 2023: Aurora Healthcare appointed Dr. Li Yong as Chief Medical Officer for its Chengdu Aurora Breast Clinic in China, advancing its mission to lead in patient-centered breast cancer care.
Conclusion
In conclusion, the AI in breast imaging market is poised for significant growth, driven by advancements in AI technologies that enhance diagnostic accuracy, improve early detection, and optimize healthcare efficiency. While challenges such as regulatory barriers, high treatment costs, and data privacy concerns persist, the market’s potential remains strong, particularly with the increasing demand for personalized medicine and the rising prevalence of breast cancer globally.
North America continues to lead the market, though the Asia-Pacific region is expected to experience the fastest growth. Innovations such as the Genius AI Detection 2.0 by Hologic and MyBreastAI Suite by GE Healthcare are further accelerating market development, showcasing the role of strategic collaborations and advanced technology in improving breast cancer detection and patient care. As AI systems evolve, their integration into clinical workflows will likely redefine breast cancer diagnosis and treatment, offering promising solutions to enhance patient outcomes and healthcare efficiency.
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