Enhancing privacy-preserving deployable large language models for perioperative complication detection: a targeted strategy with LoRA fine-tuning.

ABSTRACT: Perioperative complications are a major global concern, yet manual detection suffers from 27% under‑reporting and frequent misclassification. Clinical LLM deployment is constrained by data sovereignty, compute cost, and limited locally deployable model performance. We show targeted prompt engineering plus Low‑Rank Adaptation (LoRA) fine‑tuning converts smaller open‑source LLMs into expert‑level diagnostic tools. In dual‑center validation, we built a framework simultaneously identifying and grading 22 complication severities. State‑of‑the‑art models outperformed human experts; Chain‑of‑Thought prompting significantly improved general models (p < 0.001) while preserving reasoning models' performance. Across documentation length quartiles, AI models maintained F1 > 0.64, whereas human perform

SUBMITTER: Gao S 

PROVIDER: S-EPMC12717251 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature