Papers by Khaled Saleh

1 papers
MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations (2026.findings-acl)

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Challenge: Existing or generated clinical text may contain inaccuracies that can lead to serious adverse outcomes.
Approach: They introduce a multilingual benchmark for error detection, localization and correction . they assessed the performance of a range of general-purpose, language-specific, and medical-domain language models .
Outcome: The proposed benchmark covers English, Arabic and Chinese, with natural medical cases annotated and reviewed by domain experts.

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