Knowledge Graph-Augmented Ambient AI for Clinical Note Generation
Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to substantially reduce clinician documentation burden. However, generated notes may omit clinically relevant information discussed during the encounter, creating information gaps that can affect downstream care. Knowledge graphs (KGs) constructed from encounter transcripts can provide a structured representation of what was discussed and enable systematic identification of missing information from generated notes that are critical for patient care. In this study, we introduce Coverage-Directed Revision (CDR), a model-agnostic framework that co...
| Authors | Jakir Hossain, Yi-Fei Zhao, Hongjian Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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