aceLarge language models for multimodal clinical predictive modeling: a systematic review
The electronic health record (EHR) comprises structured and unstructured data, both of which are integral for comprehensively representing patient history. The emergence of large language models (LLMs) has enabled the incorporation of unstructured data modalities into machine learning models that leverage EHR data. In this systematic review, we characterize studies that utilize LLM-enabled multimodal predictive modeling with EHR data. Following a screening of 641 studies, 57 met inclusion criteria. We identified…
Across 57 included studies, combining unstructured EHR text with structured data through LLM-based strategies outperformed single-modality models by an average of 6.4%.
The review lists sample size, interpretability, and computational costs as considerations for LLM-enhanced predictive modeling, indicating unresolved practical constraints.
Evidence
- Peer-reviewednpj Digital Medicine2026-10-22
How should this claim be treated?
Truvace Impact Record TRV-2026-2237, v1: “Large language models for multimodal clinical predictive modeling: a systematic review.” Truvace, 2026-10-24. /record/TRV-2026-2237 (accessed at citation time). sha256 96e24ca95160e828…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2237 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.