AI RESEARCH
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
arXiv CS.LG
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ArXi:2512.19735v3 Announce Type: replace Accurately predicting mortality risk in intensive care unit (ICU) patients is essential for clinical decision-making. Although large language models (LLMs) show strong potential in structured medical prediction tasks, their outputs may exhibit biases related to graphic attributes such as sex, age, and race, limiting their reliability in fairness-critical clinical settings. Existing debiasing methods often degrade predictive performance, making it difficult to balance fairness and accuracy.