Australian healthcare workers' views on artificial intelligence in BreastScreen: Results of a mixed method survey study.

Introduction: The evolving role of Artificial Intelligence (AI) in medicine, particularly in radiology and population-based breast cancer screening programs, offers potential accuracy gains and efficiency improvements. However, successful implementation requires understanding of healthcare workers' views on AI, which this study aims to explore within the Australian BreastScreen program.

Methods: An online survey was distributed to clinical staff involved in breast imaging, collecting responses from November 2022 to April 2023.

Stromal lymphocytes are associated with upgrade of B3 breast lesions.

Various histopathological, clinical and imaging parameters have been evaluated to identify a subset of women diagnosed with lesions with uncertain malignant potential (B3 or BIRADS 3/4A lesions) who could safely be observed rather than being treated with surgical excision, with little impact on clinical practice. The primary reason for surgery is to rule out an upgrade to either ductal carcinoma in situ or invasive breast cancer, which occurs in up to 30% of patients. We hypothesised that the stromal immune microenvironment could indicate the presence of carcinoma associated with a ductal B3 lesion and that this could be detected in biopsies by counting lymphocytes as a predictive biomarker for upgrade.

Quality use of artificial intelligence in medical imaging: What do radiologists need to know?

The application of artificial intelligence, and in particular machine learning, to the practice of radiology, is already impacting the quality of imaging care. It will increasingly do so in the future. Radiologists need to be aware of factors that govern the quality of these tools at the development, regulatory and clinical implementation stages in order to make judicious decisions about their use in daily practice.

Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset.

Introduction: This study aims to evaluate deep learning (DL)-based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image.

Methods: We evaluated several DL-based AI techniques that employ different approaches and backbone DL models and tested the effect on performance of using different data-processing strategies on a set of digital mammographic images with annotations of pathologically proven breast cancer.

Results: Our evaluation uses the area under curve (AUC) and accuracy (ACC) for performance measurement.