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.
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.
Two pediatric cases highlight risks of prolonging anesthetic exposure for training purposes and prompt questions about influences of surgical training on outcomes.
AMA J Ethics. 2020;22(4):E267-275. doi:
10.1001/amajethics.2020.267.
Proliferation of innovative procedures and treatments in surgery has led to novel and distinct ethical challenges. Medicine can learn from plastic surgeons’ approaches to informed consent and potentially harmful treatments.
AMA J Ethics. 2018;20(4):349-356. doi:
10.1001/journalofethics.2018.20.4.nlit1-1804.
Nicole Martinez-Martin, JD, PhD, Laura B. Dunn, MD, and Laura Weiss Roberts, MD, MA
Calibrating a machine learning model with data from a local setting is key to predicting psychosis outcomes. Clinicians also need to understand an algorithm’s limitations and disclose clinically and ethically relevant information to patients.
AMA J Ethics. 2018;20(9):E804-811. doi:
10.1001/amajethics.2018.804.