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Multilingual Evaluation of a Large Language Model-Based Primary Care Chatbot.

Pei-Lun Chen, Amogh Ananda Rao, Sydney Pugh

    Medrxiv : the Preprint Server for Health Sciences
    |June 5, 2026
    PubMed
    Summary

    Large language model (LLM) chatbots show promise for pre-visit planning. While Hindi interactions matched English, Mandarin and Spanish showed significant quality gaps, highlighting the need for multilingual validation in clinical AI.

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    Area of Science:

    • Clinical Informatics
    • Artificial Intelligence in Healthcare
    • Human-Computer Interaction

    Background:

    • Pre-visit planning can reduce EHR burden and improve care.
    • Large language model (LLM) chatbots offer potential for clinical support.
    • English-centric LLM development raises concerns for multilingual clinical settings.

    Purpose of the Study:

    • To evaluate the multilingual capabilities of PCP-Bot, an LLM-based clinical chatbot.
    • To assess performance disparities in Hindi, Mandarin, and Spanish compared to English.
    • To understand user experience and identify areas for improvement in non-English interactions.

    Main Methods:

    • Mixed-methods study involving 31 bilingual participants (Hindi, Mandarin, Spanish).
    • Participants interacted with PCP-Bot across five synthetic cases in English and their second language.

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  • Evaluated usability, conversation quality, summary quality, trust, and workload via surveys and qualitative feedback.

    • Main Results:

    • Hindi interactions achieved parity with English in usability and conversation quality.
    • Mandarin showed usability parity but a conversation quality gap; Spanish had deficits in both.
    • Qualitative feedback noted issues like repetition and transcription errors in non-English interactions.

    Conclusions:

    • LLM translation capabilities can support deployment beyond English with validation.
    • Performance varies across languages, necessitating careful evaluation for equitable clinical AI.
    • Addressing language-specific challenges is crucial for effective multilingual chatbot implementation.