MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
Nature, pages 1–7
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Evaluation shows LLMs for healthcare are sensitive to prompt changes, leading to inconsistent and potentially harmful clinical outputs on MedMCQA.
APP is a multi-turn LLM framework for medical dialogue that combines empathetic questioning, Bayesian active learning, and guideline-based reasoning, outperforming baselines on a new simulated-patient benchmark in accuracy, uncertainty reduction, and user experience.
citing papers explorer
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MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors
MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
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When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations
Evaluation shows LLMs for healthcare are sensitive to prompt changes, leading to inconsistent and potentially harmful clinical outputs on MedMCQA.
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Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
APP is a multi-turn LLM framework for medical dialogue that combines empathetic questioning, Bayesian active learning, and guideline-based reasoning, outperforming baselines on a new simulated-patient benchmark in accuracy, uncertainty reduction, and user experience.