6 results match your criteria: "Journal of the American College of Radiology.[Affiliation]"

Impact of Perceived Discrimination and Pandemic Attitudes on Cancer Screening Behaviors Among Asian American Women: A Sequential Explanatory Mixed-Methods Study.

J Am Coll Radiol

December 2024

Vice Chair for Radiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Co-Chair, RSNA Health Equity Committee; Associate Editor, Journal of the American College of Radiology.

Purpose: The aim of this study was to assess how pandemic-related health concerns and discrimination affected cancer screenings among Asian American women (AAW).

Methods: A two-phase explanatory mixed-methods study was conducted. In phase 1, a survey was distributed among AAW eligible for lung, breast, or colorectal cancer screening to assess delays during the pandemic, concerns about contracting coronavirus disease 2019 (COVID-19), barriers to care, and experiences of discrimination.

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Communicating Imaging Risks to Patients: Time to Gather Consensus and Standardize Best Practices.

J Am Coll Radiol

October 2024

Vice Chair for Clinical Research and John Westgate Hope Endowed Chair for Faculty Development, Department of Radiology, Children's Hospital of Philadelphia, Perelman Schol of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and Associate Editor, Journal of the American College of Radiology.

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Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence.

J Am Coll Radiol

February 2024

University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. Electronic address: https://twitter.com/vishwa_parekh.

Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades.

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Purpose: To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

Materials And Methods: A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors.

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