Expert-annotated clinical consultation corpus of 207 sessions and 4,815 messages with four annotation dimensions¶
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Evidence · 1 claim (1 for) · 1 study (1 causal),q2· 0 of 1 report an effect size · 1 claim rests on one study
Description¶
Yang et al.'s dataset contains 207 medical-student clinical history-taking sessions and 4,815 human–AI dialogue turns annotated by clinical experts for message fidelity, dialogue intent, scaffolding need, and OSCE performance.
Components¶
Claims¶
Related Products and Programmes¶
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Key Sources¶
- Yang, L., Liu, H., Li, S., Jia, R., Xiao, Y., Chen, G., & Lu, L. (2026). Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training. https://arxiv.org/abs/2609.10939. doi:10.48550/arxiv.2609.10939
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
expert-annotated-clinical-consultation-corpus-of-207-sessions-and-4815-messages-with-four |
title |
Expert-annotated clinical consultation corpus of 207 sessions and 4,815 messages with four annotation dimensions |
description |
Yang et al.'s dataset contains 207 medical-student clinical history-taking sessions and 4,815 human–AI dialogue turns annotated by clinical experts for message fidelity, dialogue intent, scaffolding need, and OSCE performance. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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