We recently explored how ambiguity in clinical requests impacts downstream AI workflows and how we can address it with structured systems.
In practice, many workflows begin with loosely defined requests (e.g., “predict next-day pain spikes”), where key assumptions like targets, time windows, leakage constraints, and evaluation metrics are left implicit. This makes the translation from intent to task manual, iterative, and error-prone, and is a major source of failure in clinical AI.
To address this, we built an intent clarifier within a structured pipeline that:
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Extracts structured intent from unstructured input (including voice via MedASR)
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Identifies missing or ambiguous fields
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Asks targeted clarification questions
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Outputs a fully specified modeling task and reproducible workflow
We evaluated this using a pain prediction use case with wearable and survey data, focusing not just on model performance, but on whether the system could reliably produce valid, reproducible pipelines. This shifts the problem from “Can we build a good model?” to “Are we solving the right problem?”
Curious if others have worked on intent clarification, task structuring, or preventing mis-specification (e.g., leakage, inconsistent targets) in clinical workflows.
Blog post → https://www.nimblemind.ai/blog/lost-in-translation-building-reliable-ai-pipelines-for-clinical-data-science