@inproceedings{hubbell-etal-2026-lay,
title = "Lay-Language and Cross-Lingual Disparities in {LLM} Medical Diagnosis: A Case Study in Rheumatological Skin Conditions",
author = "Hubbell, Grayson and
Zhang, Yifei and
Khatwani, Saksham and
Sankaranarayanan, Sriram and
Sriram, Indira and
Pacheco, Maria Leonor",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-short.50/",
pages = "789--806",
ISBN = "979-8-89176-440-8",
abstract = "Patients increasingly turn to large language models (LLMs) for medical self-diagnosis in both English and non-English, often in colloquial language. We evaluate LLM diagnostic performance on rheumatological skin conditions across lay descriptions in English, Spanish, Hindi, and Mandarin, collected from native speakers without medical training, and clinical descriptions in English. Lay descriptions substantially reduce diagnostic accuracy relative to clinical ones, and the gap widens further across non-English languages. A diagnostic-focused system prompt narrows the lay-vs-clinical gap but does little to close the cross-language gap. Current LLMs appear to reproduce, and in some settings amplify, communication-driven disparities documented in clinical medicine."
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<abstract>Patients increasingly turn to large language models (LLMs) for medical self-diagnosis in both English and non-English, often in colloquial language. We evaluate LLM diagnostic performance on rheumatological skin conditions across lay descriptions in English, Spanish, Hindi, and Mandarin, collected from native speakers without medical training, and clinical descriptions in English. Lay descriptions substantially reduce diagnostic accuracy relative to clinical ones, and the gap widens further across non-English languages. A diagnostic-focused system prompt narrows the lay-vs-clinical gap but does little to close the cross-language gap. Current LLMs appear to reproduce, and in some settings amplify, communication-driven disparities documented in clinical medicine.</abstract>
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%0 Conference Proceedings
%T Lay-Language and Cross-Lingual Disparities in LLM Medical Diagnosis: A Case Study in Rheumatological Skin Conditions
%A Hubbell, Grayson
%A Zhang, Yifei
%A Khatwani, Saksham
%A Sankaranarayanan, Sriram
%A Sriram, Indira
%A Pacheco, Maria Leonor
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-440-8
%F hubbell-etal-2026-lay
%X Patients increasingly turn to large language models (LLMs) for medical self-diagnosis in both English and non-English, often in colloquial language. We evaluate LLM diagnostic performance on rheumatological skin conditions across lay descriptions in English, Spanish, Hindi, and Mandarin, collected from native speakers without medical training, and clinical descriptions in English. Lay descriptions substantially reduce diagnostic accuracy relative to clinical ones, and the gap widens further across non-English languages. A diagnostic-focused system prompt narrows the lay-vs-clinical gap but does little to close the cross-language gap. Current LLMs appear to reproduce, and in some settings amplify, communication-driven disparities documented in clinical medicine.
%U https://aclanthology.org/2026.aacl-short.50/
%P 789-806
Markdown (Informal)
[Lay-Language and Cross-Lingual Disparities in LLM Medical Diagnosis: A Case Study in Rheumatological Skin Conditions](https://aclanthology.org/2026.aacl-short.50/) (Hubbell et al., AACL-IJCNLP 2026)
ACL