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environments/hack0/doctor_agent/readme.md
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Persona-Aware MedQA Benchmarking
https://youtube.com/shorts/02GEURik0PQ
In this project, we reimagined medical QA evaluation by introducing a persona filter—a novel layer that simulates real-world variability in patient communication styles. Leveraging the MedQA dataset as our foundation, we infused each scenario with distinct personas generated via xAI’s language models:
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This mirrors how real doctors must interpret patient symptoms, which are often incomplete or colored by personality, emotion, or context.
Why this matters:
Most QA benchmarks assume a perfect narrator. But in the real world, AI systems in healthcare will need to make decisions with varying degrees of input clarity.
Most QA benchmarks assume a perfect narrator. But in the real world, AI systems in healthcare will need to make decisions with varying degrees of input clarity.
Our approach stress-tests reasoning models under more human-like variability, offering a path toward safer and more empathetic medical AI.