In many cases, the problem isn’t that an “AI” directly makes a diagnosis. Instead, AI or algorithm-driven tools may influence parts of the process, such as:
- triage and risk scoring (which determines urgency)
- imaging or lab workflow flags (which can affect what gets reviewed first)
- clinical decision support prompts (which can steer testing or diagnosis)
- documentation assistance or summarization that changes what clinicians see
If a tool suggested a likely condition, a clinician still has to verify it against the patient’s symptoms, vital signs, objective findings, and test results. A legally relevant error can occur when the care team:
- over-relies on an automated output without adequate confirmation
- fails to follow up when results conflict with the tool’s direction
- misses abnormal findings during handoffs between providers/facilities


