Healthcare AI systems frequently combine structured records, clinical notes, laboratory results, and retrieved information to produce recommendations. However, these sources do not all have the same evidential status.
A documented observation, a model-generated inference, an unverified statement, and a missing data field should not be treated as equivalent.
Developers can address this by preserving source provenance, distinguishing extracted facts from generated interpretations, and identifying assumptions that materially affect a decision.
Verification requirements should be risk-sensitive. An uncertain detail with little clinical consequence may require a simple qualification. An uncertain allergy status or medication history may require verification before an action proceeds.
For agentic AI, critical assumptions should become explicit checkpoints in the workflow. When evidence is insufficient or conflicting, the system should pause, seek additional information, or escalate appropriately.
Explainability is valuable, but users also need to understand the evidential basis of an output.
Call to action: Healthcare AI developers and clinical informaticians, what technical approaches have you found effective for tracking evidence, assumptions, and verification status?
Top comments (0)