Peer-reviewable research by Ahmed Taha on trustworthy medical AI — fairness, demographic sensitivity, and documentation integrity in vision–language and LLM-agent systems. All papers are preprints with public code and datasets.
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A paired counterfactual benchmark that audits whether nine frozen vision–language models change their spinal-radiology reports when apparent age and sex are edited while the target pathology is preserved.
A benchmark and simulated electronic-health-record environment that measures whether medical LLM agents preserve documentation integrity when institutional context rewards shortcuts or omissions.
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