Supervised clinical section segmentation models perform strongly in-domain on MIMIC-III but degrade substantially out-of-domain on a new obstetrics dataset, whereas zero-shot LLMs show robust cross-domain performance after hallucination correction.
Our exper- iments reveal challenges (e.g., hallucinated section headers) as well as the potential ben- efits of zero-shot strategies, especially when annotated data are scarce
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Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics
Supervised clinical section segmentation models perform strongly in-domain on MIMIC-III but degrade substantially out-of-domain on a new obstetrics dataset, whereas zero-shot LLMs show robust cross-domain performance after hallucination correction.