Legal questions regarding clinicians’ and technology manufacturers’ liability arise when algorithmic recommendations generated by the technology are hard to understand.

As we continue to substitute body parts with machinery, we wonder, “What makes us human?”

Clinical applications of AI prompt consideration of how responsibility should be distributed among professionals, tech companies, and others.

As machine learning becomes increasingly common in health care, these systems’ data, algorithms, and recommendations raise critical justice questions.

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.

Guidance for organizations using AI tools to diagnose and to develop treatment recommendations can also help clinicians consider risks and benefits.

Medical-legal partnerships can help detainees by motivating health, safety, and human rights standards in detention centers.

Clinicians’ disclosure of their immigration status could help or undermine patient-clinician relationships. Which factors matter most?

Going to so-called safety-net clinics could mean being subject to different standards of care than those in other health care delivery settings. Learners who understand social determinants of health might be able to help patients navigate the system and access community resources.

Undocumented patients can be subject to discrimination, reporting, detention, or deportation in violation of the Health Insurance Portability and Accountability Act, so what goes in the record is critical.