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This paper introduces MedConv, a multilingual dataset of 300 clinical encounters, and employs the ACI-BENCH framework to benchmark Corti against two commercial ambient scribes using entailment metrics and LLM-judged pairwise comparisons across eight quality dimensions. Results indicate Corti's API-based generation infrastructure matches or surpasses leading scribes, highlighting its configurability for specific documentation needs. The authors release their evaluation methodology and dataset to foster reproducible comparisons in ambient documentation systems.
A leading clinical AI platform matches or exceeds commercial ambient scribes on note quality, demonstrating the power of configurable API-driven generation.
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.
