Currently accepted at: Journal of Medical Internet Research
Date Submitted: Nov 4, 2025
Date Accepted: May 5, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/86453
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
“Small” Large Language Models in the hospital: an evaluation study on real-world data in a resources-constrained setting
ABSTRACT
Background:
Large Language Models (LLMs) offer promise for healthcare but face challenges of scale, privacy, and limited evidence in non-English settings. Smaller, locally deployable LLMs remain underexplored.
Objective:
To assess the feasibility of small open-source LLMs (1–24B parameters) in French-language clinical tasks and provide a reproducible hospital-based evaluation framework.
Methods:
Six state-of-the-art small LLMs from the Mistral, Phi-4, Llama-3.1, Meditron-3, Falcon 3 model families were tested in a zero-shot setting on de-identified discharge letters across seven use cases, including information extraction, translation, summarization, and clinical decision support. Performance was measured with F1 scores, readability indices, embedding similarity, and structured clinician reviews.
Results:
The models achieved high recall in simple retrieval tasks (up to 99.6%) but showed poor performance in detection of protected health information, adverse-event extraction, summarization, and decision support. Translation quality varied, with general-purpose models outperforming medical-focused models.
Conclusions:
In localized resource-constrained deployments, small LLMs are suitable for basic tasks but insufficient for complex reasoning or clinical decision-making. Our framework supports context-specific evaluation for safe adoption in hospitals.
Citation
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Copyright
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