Benchmarking LLM Summaries of Multimodal Clinical Time Series for Remote Monitoring
Large language models (LLMs) can generate fluent clinical summaries of remote therapeutic monitoring time series. However, it remains unclear whether these narratives faithfully capture clinically significant events, such as sustained abnormalities. Existing evaluation metrics primarily focus on semantic similarity and linguistic quality, leaving event-level correctness largely unmeasured. To address this gap, we introduce an event-based evaluation framework for multimodal time-series summarization using the Technology-Integrated Health Management (TIHM)-1.5 dementia monitoring dataset. Clinically grounded daily events are derived through rule-based abnormal thresholds and temporal persis...
| Authors | Aditya Shukla, Yining Yuan, Ben Tamo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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