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arXiv · 2509.08087

Performance Assessment Strategies for Language Model Applications in Healthcare

Victor Garcia·Mariia Sidulova·Aldo Badano
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Abstract

Language models (LMs) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. A comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments lays the foundation for the LM application assessment. Presently, a prevalent method for evaluating the performance of these generative models relies on quantitative benchmarks. Such benchmarks have limitations and may suffer from train-to-the-test overfitting, optimizing performance for a specified test set at the cost of generalizability across other tasks and data distributions. Evaluation strategies leveraging human expertise and utilizing cost-effective computational models as evaluators are gaining interest. We discuss current state-of-the-art methodologies for assessing the performance of LM applications in healthcare and medical devices.

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Victor Garcia, Mariia Sidulova, Aldo Badano. 2026-03-06. Performance Assessment Strategies for Language Model Applications in Healthcare. https://doi.org/10.1016/j.ailsci.2026.100162

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