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Last Posted: Jun 02, 2023
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The Current and Future State of AI Interpretation of Medical Images.
Pranav Rajpurkar et al. N Engl J Med 2023 5 (21) 1981-1990

The interpretation of medical images — a task that lies at the heart of the radiologist’s work — has involved the growing adoption of artificial intelligence (AI) applications in recent years. This article reviews progress, challenges, and opportunities in the development of radiologic AI models and their adoption in clinical practice.

Solving the explainable AI conundrum by bridging clinicians' needs and developers' goals.
Nadine Bienefeld et al. NPJ Digit Med 2023 5 (1) 94

Our study identifies three key differences between developer and clinician mental models of XAI, including opposing goals (model interpretability vs. clinical plausibility), different sources of truth (data vs. patient), and the role of exploring new vs. exploiting old knowledge. Based on our findings, we propose design solutions that can help address the XAI conundrum in healthcare.

An artificial intelligence based app for skin cancer detection evaluated in a population based setting.
Anna M Smak Gregoor et al. NPJ Digit Med 2023 5 (1) 90

Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is undocumented. In 2019, a large health insurance company offered 2.2 million adults free access to an mHealth app for skin cancer detection. To study the impact on dermatological healthcare consumption, we conducted a retrospective population-based pragmatic study. We matched 18,960 mHealth-users who completed at least one successful assessment with the app to 56,880 controls.

NIH launches largest precision nutrition research effort of its kind
NIH ALL of Us, May 2023 Brand

The National Institutes of Health is now enrolling participants in a landmark initiative to advance nutrition research. Nutrition for Precision Health, powered by the All of Us Research Program (NPH), is working with 14 sites across the United States to engage 10,000 participants from diverse backgrounds and learn more about how our bodies respond differently to food. NPH will use artificial intelligence (AI)-based approaches to analyze information provided by participants in order to develop algorithms that predict responses to dietary patterns. The study’s findings may one day allow healthcare providers to offer more customized nutritional guidance to improve overall health.

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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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