Understanding Gender Bias in Bangla Clinical Patient Narratives: Analyzing Fairness and Reasoning in LLM Judgments

BiasMedNarrative-BN is a synthetic counterfactual dataset of Bangla clinical patient narratives designed to evaluate gender bias in large language models. It contains 1,050 narratives constructed from 525 paired clinical scenarios, with each pair consisting of one male and one female version. The dataset covers 22 symptom types grouped into four major clinical categories, with severity levels ranging from Mild to Critical. Data was sourced from publicly available platforms including Facebook, Reddit, Bangla healthcare websites, and newspapers to capture both informal and formal patient expressions. These real-world symptom descriptions were first structured into clinically coherent scenarios, after which an LLM was used to generate natural, patient-style narrative versions. All data underwent preprocessing to remove personal identifiers and ensure linguistic and clinical consistency. The generated narratives were further validated by clinical experts to ensure realism and strict counterfactual equivalence.