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Last Posted: Dec 02, 2022
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Digital health technology-specific risks for medical malpractice liability
SP Rowland et al, NPJ Digital Medicine, October 20, 2022

Medical professionals are increasingly required to use digital technologies as part of care delivery and this may represent a risk for medical error and subsequent malpractice liability. For example, if there is a medical error, should the error be attributed to the clinician or the artificial intelligence-based clinical decision-making system? In this article, we identify and discuss digital health technology-specific risks for malpractice liability and offer practical advice for the mitigation of malpractice risk.

HIV Prevention: Digital Health Interventions to Improve Adherence to HIV Pre-Exposure Prophylaxis
The Community Guide, October 2022

The Community Preventive Services Task Force (CPSTF) recommends digital health interventions to increase adherence to HIV pre-exposure prophylaxis (PrEP). Systematic review evidence shows interventions improve both daily-use pill taking and retention in PrEP care. This improves health for population groups who are not infected with HIV and engage in behaviors that may increase their chances of getting HIV.

Translational gaps and opportunities for medical wearables in digital health.
Xu Shuai et al. Science translational medicine 2022 10 (666) eabn6036

Medical grade wearables—noninvasive, on-body sensors operating with clinical accuracy—will play an increasingly central role in medicine by providing continuous, cost-effective measurement and interpretation of physiological data relevant to patient status and disease trajectory, both inside and outside of established health care settings. Here, we review current digital health technologies and highlight critical gaps to clinical translation and adoption.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
CY Cheung et al, The Lancet Digital Health, September 30, 2022

12?949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia.


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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