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        <title>The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models</title>
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    <name type="personal">
        <namePart type="given">Liliya</namePart>
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        <namePart type="given">Giancarlo</namePart>
        <namePart type="given">Dondoni</namePart>
        <namePart type="family">Salton</namePart>
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        <namePart type="given">Robert</namePart>
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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Maria</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marie</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>Understanding medical terminology is critical for effective patient-doctor communication, yet many patients struggle with complex jargon. This study compares Machine Learning (ML) models and Large Language Models (LLMs) in predicting medical term complexity as a means of improving doctor-patient communication. Using survey data from 252 participants rating 1,000 words along with various lexical features, we measured the accuracy of both model types. The results show that LLMs outperform traditional lexical-feature-based models, suggesting their potential to identify complex medical terms and lay the groundwork for personalised patient-doctor communication.</abstract>
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%0 Conference Proceedings
%T The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models
%A Makhmutova, Liliya
%A Salton, Giancarlo Dondoni
%A Perez-Tellez, Fernando
%A Ross, Robert J.
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F makhmutova-etal-2025-evaluation
%X Understanding medical terminology is critical for effective patient-doctor communication, yet many patients struggle with complex jargon. This study compares Machine Learning (ML) models and Large Language Models (LLMs) in predicting medical term complexity as a means of improving doctor-patient communication. Using survey data from 252 participants rating 1,000 words along with various lexical features, we measured the accuracy of both model types. The results show that LLMs outperform traditional lexical-feature-based models, suggesting their potential to identify complex medical terms and lay the groundwork for personalised patient-doctor communication.
%U https://aclanthology.org/2025.ranlp-1.79/
%P 682-693

Markdown (Informal)

[The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models](https://aclanthology.org/2025.ranlp-1.79/) (Makhmutova et al., RANLP 2025)

• The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models (Makhmutova et al., RANLP 2025)

ACL

• Liliya Makhmutova, Giancarlo Dondoni Salton, Fernando Perez-Tellez, and Robert J. Ross. 2025. The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 682–693, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.