@inproceedings{mohamed-etal-2026-medroundsqa,
title = "{M}ed{R}ounds{QA}: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations",
author = "Mohamed, Youssef and
Heakl, Ahmed and
Cui, Qinrong and
Liang, Junhong and
Ali, Rafiq and
Babillie, Bdour and
Dunbayeva, Nazira and
Gao, Lang and
Hussein, Omar and
Nada, Ahmed and
Mohamed, Ahmed Mohamed Magdy and
Liu, Jinghui and
Khan, Salman and
Razzak, Imran and
Wang, Yuxia and
Chen, Xiuying",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.103/",
pages = "2433--2458",
ISBN = "979-8-89176-459-0",
abstract = "Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively, and patient communication varies widely. We introduce MEDROUNDSQA, a multi-turn diagnostic benchmark derived from 1,387 clinical cases across 17 specialties. Each case is converted into a structured 24-slot clinical record and then instantiated as controlled doctor{--}patient dual-agent dialogues under varying patient personas, with the underlying clinical content held fixed. We further classify cases by difficulty using model-based uncertainty to enable easy-to-hard analysis. Evaluations of fifteen LLM doctor agents show that (i) moving from a single-turn diagnosis on the standardized records to multi-turn consultations causes large degradations of roughly 13{--}39 points; (ii) more turns reliably improve question relevance, but diagnostic accuracy exhibits diminishing returns and typically plateaus after 6{--}12 turns; and (iii) patient persona differences can shift diagnosis accuracy by about 7{--}8 points from the lowest to highest education levels, highlighting equity risks that single-turn benchmarks miss."
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<abstract>Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively, and patient communication varies widely. We introduce MEDROUNDSQA, a multi-turn diagnostic benchmark derived from 1,387 clinical cases across 17 specialties. Each case is converted into a structured 24-slot clinical record and then instantiated as controlled doctor–patient dual-agent dialogues under varying patient personas, with the underlying clinical content held fixed. We further classify cases by difficulty using model-based uncertainty to enable easy-to-hard analysis. Evaluations of fifteen LLM doctor agents show that (i) moving from a single-turn diagnosis on the standardized records to multi-turn consultations causes large degradations of roughly 13–39 points; (ii) more turns reliably improve question relevance, but diagnostic accuracy exhibits diminishing returns and typically plateaus after 6–12 turns; and (iii) patient persona differences can shift diagnosis accuracy by about 7–8 points from the lowest to highest education levels, highlighting equity risks that single-turn benchmarks miss.</abstract>
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%0 Conference Proceedings
%T MedRoundsQA: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations
%A Mohamed, Youssef
%A Heakl, Ahmed
%A Cui, Qinrong
%A Liang, Junhong
%A Ali, Rafiq
%A Babillie, Bdour
%A Dunbayeva, Nazira
%A Gao, Lang
%A Hussein, Omar
%A Nada, Ahmed
%A Mohamed, Ahmed Mohamed Magdy
%A Liu, Jinghui
%A Khan, Salman
%A Razzak, Imran
%A Wang, Yuxia
%A Chen, Xiuying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F mohamed-etal-2026-medroundsqa
%X Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively, and patient communication varies widely. We introduce MEDROUNDSQA, a multi-turn diagnostic benchmark derived from 1,387 clinical cases across 17 specialties. Each case is converted into a structured 24-slot clinical record and then instantiated as controlled doctor–patient dual-agent dialogues under varying patient personas, with the underlying clinical content held fixed. We further classify cases by difficulty using model-based uncertainty to enable easy-to-hard analysis. Evaluations of fifteen LLM doctor agents show that (i) moving from a single-turn diagnosis on the standardized records to multi-turn consultations causes large degradations of roughly 13–39 points; (ii) more turns reliably improve question relevance, but diagnostic accuracy exhibits diminishing returns and typically plateaus after 6–12 turns; and (iii) patient persona differences can shift diagnosis accuracy by about 7–8 points from the lowest to highest education levels, highlighting equity risks that single-turn benchmarks miss.
%U https://aclanthology.org/2026.emnlp-main.103/
%P 2433-2458
Markdown (Informal)
[MedRoundsQA: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations](https://aclanthology.org/2026.emnlp-main.103/) (Mohamed et al., EMNLP 2026)
ACL
- Youssef Mohamed, Ahmed Heakl, Qinrong Cui, Junhong Liang, Rafiq Ali, Bdour Babillie, Nazira Dunbayeva, Lang Gao, Omar Hussein, Ahmed Nada, Ahmed Mohamed Magdy Mohamed, Jinghui Liu, Salman Khan, Imran Razzak, Yuxia Wang, and Xiuying Chen. 2026. MedRoundsQA: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2433–2458, Budapest, Hungary. Association for Computational Linguistics.