Data Visualization Support for Interdisciplinary Team Treatment Planning in Clinical Oncology: Scoping Review

Background: Complex and expanding datasets in clinical oncology applications require flexible and interactive visualization of patient data to provide physicians and other medical professionals with maximum amount of information. In particular, interdisciplinary tumor conferences profit from customized tools to integrate, link, and visualize relevant data from all professions involved. Objective: Our objective was to identify […]

Artificial Intelligence–Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis

Background: Artificial intelligence (AI) techniques, particularly those employing machine learning (ML) and deep learning (DL) to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE). Objective: This meta-analysis explores the diagnostic performance of artificial intelligence-enabled imaging techniques versus radiologists for predicting preoperative EPE in prostate cancer (PCa). Methods: […]

Women’s Insights on Extended Adjuvant Endocrine Therapy for Breast Cancer: Qualitative Online Forums Study

Background: In France, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death among women. For around one third of women with hormone receptor-positive breast cancer, extending adjuvant endocrine therapy (AET) beyond the initial 5-year is now recommended to reduce the risk of recurrence and mortality. While weighing benefits against […]

Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study

Background: While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, U.S. healthcare providers and hospital administrators have increasingly embraced ML, particularly through its integration with electronic health record (EHR) systems. This evolving landscape underscores the need for empirical evidence on ML adoption and its determinants; however, the relationship […]

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