# Listening to Patients: A Framework of Detecting and Mitigating Patient Misreport for Medical Dialogue Generation

arXiv: https://arxiv.org/abs/2410.06094 · HTML: https://mlsanity.com/paper/2410.06094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models](https://mlsanity.com/paper/2605.20591/) (2026)

## Abstract (arXiv)

Medical Dialogue Systems aim to provide automated healthcare support through patient-agent conversations. Previous efforts typically regard patients as ideal users -- one who accurately and consistently reports their health conditions. However, in reality, patients often misreport their symptoms, leading to discrepancies between their reports and actual health conditions. Overlooking patient misreport will affect the quality of healthcare consultations provided by MDS. To address this issue, we argue that MDS should ''listen to patients'' and tackle two key challenges: how to detect and mitigate patient misreport effectively. In this work, we propose PaMis, a framework of detecting and mitigating Patient Misreport for medical dialogue generation. PaMis first constructs dialogue entity graphs, then detects patient misreport based on graph entropy, and mitigates patient misreport by formulating clarifying questions. Experiments indicate that PaMis effectively enhances medical response generation, enabling models like GPT-4 to detect and mitigate patient misreports, and provide high-quality healthcare assistance.