Publications by authors named "R N Matin"
Background: Cutaneous melanoma (CM) is the leading cause of skin cancer mortality with associated high healthcare costs. Up-to-date reporting of epidemiological trends for CM is required to project future trends, assess the burden of disease and aid evaluation of new diagnostic, therapeutic and preventative strategies.
Objectives: To describe the trends in CM mortality, incidence, mortality-to-incidence indices (MIIs) and disability-adjusted life years (DALYs) over the last three decades.
Article Synopsis
- There is a significant risk of reinforcing existing health inequalities in AI health technologies due to biases, primarily stemming from the datasets used.
- The STANDING Together recommendations focus on transparency in health datasets and proactive evaluation of their impacts on different population groups, informed by a comprehensive research process with over 350 global contributors.
- The 29 recommendations are divided into guidance for documenting health datasets and strategies for using them, aiming to identify and reduce algorithmic biases while promoting awareness of the inherent limitations in all datasets.
Objectives: Deep brain stimulation (DBS) is an established neuromodulatory technique for treating drug-resistant epilepsy. Despite its widespread use in carefully selected patients, the mechanisms underlying the antiseizure effects of DBS remain unclear. Herein, we provide a detailed overview of the current literature pertaining to experimental DBS in rodent models of epilepsy and identify relevant trends in this field.
View Article and Find Full Text PDFArticle Synopsis
- This review analyzes various mammography datasets used for AI development in breast cancer screening, focusing on their transparency, content, and accessibility.
- A search identified 254 datasets, with only 28 being accessible; most datasets came from Europe, East Asia, and North America, raising concerns over poor demographic representation.
- The findings highlight significant gaps in diversity within these datasets, underscoring the need for better documentation and inclusivity to enhance the effectiveness of AI technologies in breast cancer research.