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Last Posted: Feb 27, 2024
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Deep learning models across the range of skin disease.
Kaushik P Venkatesh et al. NPJ Digit Med 2024 2 (1) 32

From the abstract: "We explore the evolving landscape of diagnostic artificial intelligence (AI) in dermatology, particularly focusing on deep learning models for a wide array of skin diseases beyond skin cancer. We critically analyze the current state of AI in dermatology, its potential in enhancing diagnostic accuracy, and the challenges it faces in terms of bias, applicability, and therapeutic recommendations. "

Transparency of artificial intelligence/machine learning-enabled medical devices.
Aubrey A Shick et al. NPJ Digit Med 2024 1 (1) 21

From the article: " The United States Food and Drug Administration (FDA) is reviewing an increasing number of applications for AI/ML devices, with the number receiving FDA marketing authorization nearing seven hundred as of October 2023. AI/ML devices have unique considerations during their development and use, including those for usability, equity of access, management of performance bias, the potential for continuous learning, and stakeholder (manufacturer, patient, caregiver, healthcare provider, etc.) accountability. These considerations impact not only the responsible development and use of AI/ML devices but also the regulation of such devices"

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.
Varsha Gupta et al. NPJ Digit Med 2023 12 (1) 239

From the abstract: " Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters"

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population
KD Jacobsen et al, Comm Med, December 20, 2023

From the abstract: "Here, in a cohort of 88,818 individuals, including 37,482 with a confirmed SARS-CoV-2 infection, the RD of long-term sick-leave is 3.3% (95% CI 3.1% to 3.6%). We observe a high degree of effect heterogeneity, with conditional RDs ranging from -3.4% to 13.7%. Age, high BMI, depression, and sex are the most important variables explaining heterogeneity. "

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.