In many cases, the problem isn’t that “AI is bad.” The problem is how an AI or automation output was used inside a real clinical workflow.
In a community like Huron—where patients may move between providers, clinics, and facilities—diagnostic errors can emerge when:
- Risk scores or decision-support recommendations steer attention away from alternative diagnoses.
- Automated triage routes a patient to the wrong level of urgency or follow-up.
- Imaging or lab results are interpreted with automation assistance but not reconciled with the full clinical picture.
- Documentation tools summarize symptoms in a way that doesn’t match what was actually reported.
Legally, the question is whether care fell below the South Dakota standard of care—including how clinicians should verify automated outputs and act when objective findings conflict with a tool’s suggestion.


