Performance of Large Language Models Under Input Variability in Health Care Applications: Dataset Development and Experimental Evaluation

Our findings highlight the need for health care applications powered by LLMs to be designed with input variability in mind. Robustness to noisy or imperfect inputs is essential for maintaining reliability in real-world clinical settings, where data quality can vary widely. By identifying specific vulnerabilities and strengths, this study provides actionable insights for improving model resilience and guiding the development of safer, more effective artificial intelligence tools in health care. The accompanying dataset offers a valuable resource for further research into LLM performance under diverse conditions.