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RAG-Enhanced Open SLMs for Hypertension Management Chatbots.

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Title: RAG-Enhanced Open SLMs for Hypertension Management Chatbots.
Authors: Aguzzi, Gianluca1 (AUTHOR) gianluca.aguzzi@unibo.it, Magnini, Matteo1 (AUTHOR) matteo.magnini@unibo.it, Farahmand, Aqila2 (AUTHOR) aqila.farahmand@uniurb.it, Ferretti, Stefano1 (AUTHOR) s.ferretti@unibo.it, Pengo, Martino Francesco3,4 (AUTHOR) martino.pengo@unimib.it, Montagna, Sara2 (AUTHOR) sara.montagna@uniurb.it
Source: Journal of Medical Systems. 11/13/2025, Vol. 49 Issue 1, p1-15. 15p.
Subjects: Effect sizes (Statistics), Self-management (Psychology), Research funding, Hypertension, Natural language processing, Data analytics, Descriptive statistics, Chi-squared test, Chronic diseases, Experimental design, Information retrieval, Software architecture, Chatbots, Algorithms
Abstract: Chronic disease management requires continuous monitoring, lifestyle modification and therapy adherence, thus requiring constant support from healthcare professionals. Chatbots have proven to be a promising approach for engaging patients in managing their health condition at home and for offering continuous assistance by being readily available to answer questions. While large language models offer an impressive solution for chatbot implementation, third-party systems raise privacy concerns, and computational requirements limit small-scale deployment. We address these challenges by developing a chatbot for hypertensive patients based on open-source small language models (SLMs), specifically designed for running on personal resource-constrained devices and for providing assistance in QA tasks. In order to guarantee comparable conversational performances with respect to larger language models, we exploited retrieval-augmented generation (RAG) with a local knowledge base. This ensures data privacy by deploying models locally while achieving competitive accuracy and maintaining low computational costs suitable for end-user devices. We experimented with eight SLMs, two prompt configurations, and different RAG strategies – both in the embedding and retrieval components – to identify the most effective solution. The evaluation of our solution grounds on both reference metrics and expert evaluation. Our findings suggest that RAG-enhanced SLMs can improve response clarity and content accuracy. However, our results also indicate that newer SLMs like Qwen3 demonstrate strong performance even without RAG, suggesting a potential shift in the necessity for complex retrieval mechanisms with rapidly evolving model architectures. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: RAG-Enhanced Open SLMs for Hypertension Management Chatbots.
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  Data: <searchLink fieldCode="AR" term="%22Aguzzi%2C+Gianluca%22">Aguzzi, Gianluca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gianluca.aguzzi@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Magnini%2C+Matteo%22">Magnini, Matteo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> matteo.magnini@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Farahmand%2C+Aqila%22">Farahmand, Aqila</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> aqila.farahmand@uniurb.it</i><br /><searchLink fieldCode="AR" term="%22Ferretti%2C+Stefano%22">Ferretti, Stefano</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.ferretti@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Pengo%2C+Martino+Francesco%22">Pengo, Martino Francesco</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> martino.pengo@unimib.it</i><br /><searchLink fieldCode="AR" term="%22Montagna%2C+Sara%22">Montagna, Sara</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sara.montagna@uniurb.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Medical+Systems%22">Journal of Medical Systems</searchLink>. 11/13/2025, Vol. 49 Issue 1, p1-15. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Effect+sizes+%28Statistics%29%22">Effect sizes (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Self-management+%28Psychology%29%22">Self-management (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Hypertension%22">Hypertension</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+diseases%22">Chronic diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Chronic disease management requires continuous monitoring, lifestyle modification and therapy adherence, thus requiring constant support from healthcare professionals. Chatbots have proven to be a promising approach for engaging patients in managing their health condition at home and for offering continuous assistance by being readily available to answer questions. While large language models offer an impressive solution for chatbot implementation, third-party systems raise privacy concerns, and computational requirements limit small-scale deployment. We address these challenges by developing a chatbot for hypertensive patients based on open-source small language models (SLMs), specifically designed for running on personal resource-constrained devices and for providing assistance in QA tasks. In order to guarantee comparable conversational performances with respect to larger language models, we exploited retrieval-augmented generation (RAG) with a local knowledge base. This ensures data privacy by deploying models locally while achieving competitive accuracy and maintaining low computational costs suitable for end-user devices. We experimented with eight SLMs, two prompt configurations, and different RAG strategies – both in the embedding and retrieval components – to identify the most effective solution. The evaluation of our solution grounds on both reference metrics and expert evaluation. Our findings suggest that RAG-enhanced SLMs can improve response clarity and content accuracy. However, our results also indicate that newer SLMs like Qwen3 demonstrate strong performance even without RAG, suggesting a potential shift in the necessity for complex retrieval mechanisms with rapidly evolving model architectures. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10916-025-02297-7
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      – Code: eng
        Text: English
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        PageCount: 15
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    Subjects:
      – SubjectFull: Effect sizes (Statistics)
        Type: general
      – SubjectFull: Self-management (Psychology)
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Hypertension
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Data analytics
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Chi-squared test
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      – SubjectFull: Chronic diseases
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      – SubjectFull: Experimental design
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      – SubjectFull: Information retrieval
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      – SubjectFull: Chatbots
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      – SubjectFull: Algorithms
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      – TitleFull: RAG-Enhanced Open SLMs for Hypertension Management Chatbots.
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              Text: 11/13/2025
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