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Last Posted: Apr 17, 2024
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Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
F Kolbinger et al, Comm Med, April 11, 2024

From the abstract: "AI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages. Our analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. "

Utilizing geospatial artificial intelligence to map cancer disparities across health regions
A Fadiel et al, Sci Report, April 2, 2024

From the abstract: "We have developed an innovative tool, the Intelligent Catchment Analysis Tool (iCAT), designed to identify and address healthcare disparities across specific regions. Powered by Artificial Intelligence and Machine Learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities. iCAT allows users to query publicly available data sources, health system data, and treatment data, offering insights into gaps and disparities in diagnosis and treatment paradigms. "

Deep learning in cancer genomics and histopathology
M Unger et al, Genome Medicine, March 27, 2024

From the abstract: " Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. "

How to support the transition to AI-powered healthcare.
et al. Nat Med 2024 3 (3) 609-610

From the article: "To make health systems more sustainable in the long-term, incentivize artificial intelligence (AI) and digital technologies that are grounded on careful testing and real-world validation. We are confident that this transition to ‘AI-powered’ healthcare will occur and that it has the potential to bring widespread public good. At the same time, we believe that these benefits will realize more steadily and more quickly with carefully designed clinical studies and evidence-based implementation of AI algorithms and devices in the real world."

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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