Risk perception and intended behavior change after uninformative genetic results for adult-onset hereditary conditions in unselected patients.
Nandana D Rao et al. Eur J Hum Genet 2023 9
From the abstract: "Overall, 2761 people received uninformative results and 1352 (49%) completed survey items. Respondents averaged 41 years old, 62% were female, and 56% were Non-Hispanic Asian. Results from the FACToR instrument showed mean (SD) scores of 0.92 (1.34), 7.63 (3.95), 1.65 (2.23), and 0.77 (1.50) for negative emotions, positive emotions, uncertainty, and privacy concerns, respectively, suggesting minimal psychosocial harms from genetic screening. Overall, 12.2% and 9.6% of survey respondents believed that their risk of cancer or heart disease, respectively, had changed after receiving their uninformative genetic screening results. Further, 8.5% of respondents planned to make healthcare changes and 9.1% other behavior changes. "
AI in Public Health
J Pina, ASTHO Blog, August 2023
Generative Artificial Intelligence (AI) tools have become increasingly available and accessible in recent years, empowering individuals and organizations to harness the potential of AI and machine learning. These newly available resources have sparked great curiosity within the public health community, and ASTHO members are considering the value of these tools in practice. Through ASTHO’s work in public health data modernization, and broadly in population health innovation, we’ve received many requests to address, recognize, and expound on the value and potential of AI in our field. However, as with any disruptive technology, responsible and ethical use is essential to ensure that these tools are employed in a manner that respects privacy, avoids misinformation, minimizes bias and inequities, and upholds societal well-being.
AI and Medical Education — A 21st-Century Pandora’s Box
A Cooper et al, NEJM, August 3, 2023
Many valid concerns have been raised about AI’s effects on medicine, including the propensity for AI to make up information that it then presents as fact (termed a “hallucination”), its implications for patient privacy, and the risk of biases being baked into source data. But we worry that the focus on these immediate challenges obscures many of the broader implications that AI could have for medical education — in particular, the ways in which this technology could affect the thought structures and practice patterns of medical trainees and physicians for generations to come.
Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer.
James Casaletto et al. Annu Rev Genomics Hum Genet 2023 5
Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to the data—providing the technical and legal capability to analyze the data within their secure home environment rather than transferring the data to another institution for analysis.