Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Simple Summary Diagnosing cancer at an early stage increases the chance of performing
effective treatment in many tumour groups. Key approaches include screening patients who …

The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently
contributing to improved diagnostic accuracy, optimized treatment planning, and improved …

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack
of objective outcomes and fidelity metrics. AI technologies and specifically Natural …

Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast
cancer in mammography screening practice. Design Systematic review of test accuracy …

Background We propose a decision-referral approach for integrating artificial intelligence
(AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref.). To
achieve earlier cancer detection, health organizations worldwide recommend screening …

Screening for breast cancer with mammography has been introduced in various countries
over the last 30 years, initially using analog screen-film-based systems and, over the last 20 …

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect
cancer and other breast diseases. Recent studies have shown that deep learning-based …