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.

BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations.

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e.

An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning.

The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, while interpretable models do not have competitive classification accuracy. In this paper, we introduce a new deep-learning diagnosis framework, called InterNRL, that is designed to be highly accurate and interpretable.

Australian women's judgements about using artificial intelligence to read mammograms in breast cancer screening.

Objective: Mammographic screening for breast cancer is an early use case for artificial intelligence (AI) in healthcare. This is an active area of research, mostly focused on the development and evaluation of individual algorithms. A growing normative literature argues that AI systems should reflect human values, but it is unclear what this requires in specific AI implementation scenarios.

ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.

Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.

Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills.

Background: Alongside the promise of improving clinical work, advances in healthcare artificial intelligence (AI) raise concerns about the risk of deskilling clinicians. This purpose of this study is to examine the issue of deskilling from the perspective of diverse group of professional stakeholders with knowledge and/or experiences in the development, deployment and regulation of healthcare AI.

Methods: We conducted qualitative, semi-structured interviews with 72 professionals with AI expertise and/or professional or clinical expertise who were involved in development, deployment and/or regulation of healthcare AI.

Exploration of potential indicators of burnout, psychological distress and post-traumatic stress disorder, among Australian female first responders.

There is limited research on the psychological wellbeing of female first responders (FRs) and therefore we explore potential indicators of burnout, psychological distress and post-traumatic stress disorder among Australian female FRs. We conducted an online health survey among Australian female FRs (fire, police, paramedical, aeromedical, remote area and other e.g.

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.

Factors influencing the recruitment and retention of female first responders: a mixed methods systematic review protocol.

Objective: The objective of this review is to produce a set of integrated findings of quantitative and qualitative evidence regarding workplace recruitment and retention factors (including departure) of female first responders to inform recommendations for policy and practice.

Introduction: Historically, first responder workforces such as police officers, firefighters, search and rescue personnel, medical technicians, and paramedics have been largely male dominated. Over the past few decades, however, there has been a steady increase in the number of women entering this field.

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility.

Improvement of Cancer Detection on Mammograms via BREAST Test Sets.

Background: Breast Screen Reader Assessment Strategy (BREAST) is an innovative training and research program for radiologists in Australia and New Zealand. The aim of this study is to evaluate the efficacy of BREAST test sets in improving readers' performance in detecting cancers on mammograms.

Materials And Methods: Between 2011 and 2018, 50 radiologists (40 fellows, 10 registrars) completed three BREAST test sets and 17 radiologists completed four test sets.