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Performance Assessment Strategies for Language Model Applications in Healthcare

arXiv 2509.08087 ↗First posted 9 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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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References · 30

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18 more
  1. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 244
  2. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environmentscited by 215
  3. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
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  12. 2024Adaptive Preference Scaling for Reinforcement Learning with Human Feedbackcited by 20
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  18. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7

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