Camillo Lamanna, MMathPhil, MBBS and Lauren Byrne, MBBS
Perhaps machine learning systems trained on patients’ electronic health records and social media footprints could be used as decision aids when patients lack capacity or face overwhelming decisions.
AMA J Ethics. 2018;20(9):E902-910. doi:
10.1001/amajethics.2018.902.
Elizabeth Boskey, PhD, MPH, MSSW, Amir Taghinia, MD, and Oren Ganor, MD
Training should be implemented to respond to clinical staff members’ concerns about trans patients occupying sex-segregated spaces and to help mitigate anti-trans bias.
AMA J Ethics. 2018;20(11):E1067-1074. doi:
10.1001/amajethics.2018.1067.
Despite challenges of decision making for unrepresented patients, few laws or policy statements offer solutions. This article offers 5 key things to do.
AMA J Ethics. 2019;21(7):E582-586. doi:
10.1001/amajethics.2019.582.
Artist and researcher Dr Mark Gilbert joins Ethics Talk to discuss arts-based research: what it is, who it’s for, and why we should pay closer attention to it as a method of clinical inquiry.
Social and behavioral data contained in electronic health records are essential for studying health disparities. Can researchers avoid bias when collecting, analyzing, and using such data?
AMA J Ethics. 2018;20(9):E873-880. doi:
10.1001/amajethics.2018.873.