AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Open LLMs can extract clinical findings from Finnish pediatric records with accuracy comparable to published English benchmarks, and uncertainty-based triage substantially reduces required expert workload.
The findings support the feasibility of applying LLM-based natural language processing tools in resource-limited, non-English healthcare settings and should assess emerging high-parameter models and explore additional clinical domains.
This is the first study to benchmark SLMs on Italian EHRs and investigate the role of clinical expertise in prompt engineering, offering valuable insights for the future integration of SLMs into real-world clinical workflows.
Current LLMs do not achieve inter-rater reliability levels comparable to medical professionals in clinical information extraction from ENT documentation, suggesting they are best suited for initial extraction with human verification rather than autonomous operation.
The findings support the feasibility of AI-assisted abstraction workflows, although further validation across larger and more diverse datasets is needed.
An LLM leaderboard showing how open-source LLMs perform at entity extraction on unseen clinical notes is developed, showing that large language models are already available that can perform entity extraction well enough to be considered in place of some administrative data.
A reproducible estimate of diagnostic retrieval accuracy across four widely used model configurations is provided to establish a baseline for further clinical validation and establish a baseline for further clinical validation.
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