Scott J. Schweikart joins Ethics Talk to discuss his article, coauthored with Annika J. Penzer: “Using Policy and Law to Help Reduce Endometriosis Diagnostic Delay.”

Two concerns (unknowability of how output is derived from input and overreliance on clinical decision support systems) are main sources of ethical questions about AI in health care.

Despite high incidence of endometriosis internationally and domestically, many patients wait a decade after symptom onset for an accurate diagnosis.

Coded data from patients’ health records are used in epidemiological research about incidence or prevalence of disease, drug safety monitoring, or long-term cohort tracking.

Like all humans, health professionals are subject to cognitive biases that can render diagnoses and treatment decisions vulnerable to error.

Diagnostic utility of weight and body mass index is widely overestimated, and their use as health and wellness measures can be sources of iatrogenic harm. 

Clinicians’ racial biases undermine the quality of Black persons’ health care experiences and pave a reliable path to health care-induced harm.

While transgender health care has moved beyond “gender identity disorder” and “gender dysphoria” as mental illnesses, gender incongruence continues to be a source of oppression.

The communication training program at University of Washington Medical Center aims to develop best practices for effective pathology communication.