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AI In Breast Imaging Market Size 2025-2029

The AI in breast imaging market size is forecast to increase by USD 897 million, at a CAGR of 24.9% between 2024 and 2029.

The market is driven by the imperative for early detection amid the rising global burden of breast cancer. This need is fueling the adoption of advanced AI solutions in breast imaging, enabling more accurate and efficient diagnosis. A significant trend in the market is the evolution from standalone algorithms to integrated AI platforms and ecosystems. This shift allows for improved data analysis and better collaboration between healthcare professionals, enhancing overall patient care. However, challenges persist in the market, primarily in the form of data diversity, algorithmic bias, and generalizability. Advanced technologies like machine learning, computer-aided detection, and artificial intelligence (AI) are revolutionizing breast imaging, enabling remote diagnosis and improving diagnostic accuracy.
Ensuring algorithmic fairness and reducing bias is also crucial for accurate diagnosis and maintaining patient trust. Companies must address these challenges to capitalize on the market's potential and navigate the competitive landscape effectively. By focusing on data acquisition, algorithm development, and regulatory compliance, they can create innovative solutions that meet the evolving needs of healthcare providers and patients. The use of AI in breast imaging requires large, diverse datasets for effective training and implementation. Magnetic resonance imaging (MRI) and computer-aided detection are complementary technologies, providing additional diagnostic insights and reducing false positives.

What will be the Size of the AI In Breast Imaging Market during the forecast period?

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The market continues to evolve, with ongoing advancements in technology and applications across various sectors. Risk stratification, a key application of AI, enables more accurate identification and prioritization of high-risk cases, reducing false positives and improving overall diagnostic accuracy. User interface design plays a crucial role in ensuring ease of use and effective integration of AI solutions into clinical workflows. Validation metrics and cloud-based solutions facilitate efficient model training using large datasets, while radiomics features and deep learning models enable more accurate detection of microcalcifications and mass lesions. Image enhancement techniques and hardware acceleration optimize the performance of AI-powered screening, allowing for faster and more precise analysis. Hormone therapy and radiation therapy continue to be popular treatment options, while oncology drugs such as monoclonal antibodies, biosimilars, and vaccines are gaining popularity.
Predictive modeling and treatment response prediction enable personalized care, while feature extraction methods and 3D image reconstruction provide more comprehensive assessments. Clinical workflow integration, breast density assessment, and lesion characterization are other areas where AI is making a significant impact. The breast imaging market is expected to grow at a robust rate, with industry experts projecting a CAGR of over 15% in the coming years. For instance, a leading AI-powered CAD system has reported a 5% increase in detection accuracy compared to traditional methods. Data privacy concerns remain a significant challenge, but advancements in software infrastructure and regulatory frameworks are addressing these issues.

How is this AI In Breast Imaging Industry segmented?

The AI in breast imaging industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Screening
  Diagnostics
  Risk assessment
  Others


Technology

  Machine learning
  Computer aided detection
  Deep learning
  Natural language processing


End-user

  Hospitals and clinics
  Diagnostic imaging centers
  Breast care and specialty clinics
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The Screening segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth, with the screening segment leading the way. This segment focuses on analyzing images from asymptomatic populations to facilitate early detection of breast cancer, which is crucial for improving patient outcomes. AI algorithms are employed across primary screening modalities, such as 2D digital mammography, digital breast tomosynthesis (DBT), and automated breast

This dataset contains details of the 2015-2016 Charity Open Access Fund (COAF) open access spend. During this period the members of COAF were Wellcome Trust, Arthritis Research UK, Breast Cancer Now, British Heart Foundation, Bloodwise, Cancer Research UK and Parkinson’s UK. In addition to reporting spend, the data has been analysed through our “compliance monitoring” tool (developed for us by Cottage Labs) to help us determine programmatically whether the paper is in the Europe PMC repository and what licence (if any) is attached to the article.

The dataset includes information when an article processing charge (APC) was levied to the COAF fund. If an author has self-archived a paper, this information is not included in this dataset. Equally, data are not included in cases where a researcher (based at an institution not in receipt of a COAF block grant) received a supplement to their grant to cover OA publishing costs.

We hope that this data will be of use to help better understand the cost of OA publishing.

If through use of this data you identify errors or believe the status of an article to be incorrect please notify Hannah Hope (h.hope[at]welcome.ac.uk). We will endeavour to investigate these issues and correct errors where identified. Corrections to the dataset will be published as a new version of the dataset along with note explaining the changes.

Participant Demographic Summary Statistics.