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.
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.
Although poor communication is the root cause of medical malpractice claims, in cases of medical error, apologies reduce litigation and benefit patients.
AMA J Ethics. 2017;19(3):289-295. doi:
10.1001/journalofethics.2017.19.3.hlaw1-1703.
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.
Emily L. Evans, PhD, MPH and Danielle Whicher, PhD, MHS
Clinical decision support systems leverage data generated in the course of standard clinical care to improve clinical practice. They need to ensure privacy and quality of patients’ data, but must also allow queries of electronic health records.
AMA J Ethics. 2018;20(9):E857-863. doi:
10.1001/amajethics.2018.857.