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Last Posted: Sep 28, 2023
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Where Medical Statistics Meets Artificial Intelligence.
David J Hunter et al. N Engl J Med 2023 9 (13) 1211-1219

From the paper: " The very features that make AI a valuable tool for data analysis are the same ones that make it vulnerable from a statistical perspective. This paradox is particularly pertinent for medical science. Techniques that are adequate for targeted advertising to voters and consumers or that enhance weather prediction may not meet the rigorous demands of risk prediction or diagnosis in medicine.7,8 In this review article, we discuss the statistical challenges in applying AI to biomedical data analysis and the delicate balance that researchers face in wishing to learn as much as possible from data while ensuring that data-driven conclusions are accurate, robust, and reproducible. "

AI can help to speed up drug discovery - but only if we give it the right data.
Marissa Mock et al. Nature 2023 9 (7979) 467-470

From the paper: "Artificial-intelligence tools that enable companies to share data about drug candidates while keeping sensitive information safe can unleash the potential of machine learning and cutting-edge lab techniques, for the common good. "

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking
C Frascarelli et al, J Per Med, September 2023

From the abstract: "As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. "

A foundation model for generalizable disease detection from retinal images.
Yukun Zhou et al. Nature 2023 9

From the abstract: "Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. "


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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