> For the complete documentation index, see [llms.txt](https://docs.snomed.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snomed.org/snomed-ct-practical-guides/snomed-ct-mapping-guide/quality-assurance-of-maps-and-ai-considerations/considerations-for-ai-assisted-maps.md).

# Considerations for AI-Assisted Maps

The consistent view is that AI should be treated as an additional "mapper" rather than a replacement for QA processes. The same quality principles still apply, but AI introduces specific new considerations.

## AI as an Additional Mapper

* AI assistance is useful for speeding up the initial draft of maps.
* A human-in-the-loop remains essential when quality-assuring AI-generated maps.
* In SNOMED International projects like SNOMED-ICD-10 and MedDRA, QA remains fundamentally manual, and AI assistance has not yet been incorporated into these workflows.

## Quality Challenges and Mitigation

Experience with AI-generated maps has shown mixed quality:

* Issues observed include hallucinated concepts or SCTIDs that do not match their descriptions.
* Where the code was plausible, it was not always as precise as it could have been.
* Better prompting may improve results, but verification remains critical.

## Specific QA Mechanisms for AI Outputs

When AI is involved, the QA framework should explicitly include:

* Confidence scoring and risk-based review, with stronger review requirements for low-confidence or high-impact maps.
* Ensuring reproducibility, since AI-generated outputs can vary over time and across models.
* Clearly distinguishing between AI-suggested maps and human-approved maps throughout the dataset.


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