Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized.
We introduce MedConv, a multilingual dataset of 300 clinical encounters in English, Danish, and German.
And use it alongside the Ambient Clinical Intelligence benchmark (ACI-BENCH) to compare Corti, a clinical AI platform, with two leading, accessible ambient scribe software applications built on general-purpose AI.
We present a controlled clinical evaluation framework that combines entailment metrics with LLM-judged pairwise comparisons across eight dimensions adopted from PDSQI-9.
Results show that Corti's API-based text-generation infrastructure is on par with or outperforms leading commercial scribes.
We further show that Corti's configurable API provides the flexibility necessary to fine-tune quality dimensions for specific documentation use cases.
We present the evaluation methodology and release a dataset to support future reproducible comparison of ambient documentation systems.