Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Abstract

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations

“…Currently, there are many AI applications for breast imaging, but their adoption and utilization vary widely and are generally low ( Figure 1 ). 4 , 34 - 37 By distinguishing complex patterns in images, AI can transform image interpretation into a more measurable and objective procedure. In a screening environment, AI diminishes the time required for interpretation, lowers the rate of recalls, minimizes the necessity for biopsies, enhances lesion detection, and reduces interval cancers.…”

“…Among the various screening methods, mammography is the only modality that has demonstrated a conclusive reduction in mortality, contributing to the global implementation of screening programs centered around mammography. 4 …”

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

“…Such strategies hold the potential to provide clinicians with interventions—whether they involve disease modification or prevention—tailored to the specific traits of individuals. Prediction algorithms using AI approaches for cancer [ 20 , 21 ], CVD [ 22 ] and autoimmunity [ 23 – 25 ] have shown promising results. AI has also been applied in type 1 diabetes, for instance in optimising insulin pump settings [ 26 ], in potentially identifying predictive biomarkers [ 27 ] and for the detection of complications [ 28 ].…”

Radiologists’ Communicative Role in Breast Cancer Patient Management: Beyond Diagnosis

“…Indeed, a diagnosis of BC impacts the relationships and the sexual life of women and their partners, so it is crucial to address potential issues by discussing them with the couple, avoiding judgment, and putting them at ease [ 65 ]. However, couples often prefer to receive information via brochures or websites because of prejudice, taboos, and cultural stigma associated with sexuality, especially regarding women [ 66 ]. This holistic approach ensures that the patient and their support system receive the necessary information and emotional support during this difficult time.…”