Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Objectives: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Materials And Methods: We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52).

Medication monitoring and drug testing ethics project.

In 2012, Duke University initiated a research project, funded by an unrestricted research grant from Millennium Laboratories, a drug testing company. The project focused on assessing the frequency and nature of questionable, unethical, and illegal business practices in the clinical drug testing industry and assessing the potential for establishing a business code of ethics. Laboratory leaders, clinicians, industry attorneys, ethicists, and consultants participated in the survey, were interviewed, and attended two face-to-face meetings to discuss a way forward.