Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT
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Abstract (arXiv)
Conversational assistants are becoming more and more popular, including in healthcare, partly because of the availability and capabilities of Large Language Models. There is a need for controlled, probing evaluations with real stakeholders which can highlight advantages and disadvantages of more traditional architectures and those based on generative AI. We present a within-group user study to compare two versions of a conversational assistant that allows heart failure patients to ask about salt content in food. One version of the system was developed in-house with a neurosymbolic architecture, and one is based on ChatGPT. The evaluation shows that the in-house system is more accurate, completes more tasks and is less verbose than the one based on ChatGPT; on the other hand, the one based on ChatGPT makes fewer speech errors and requires fewer clarifications to complete the task. Patients show no preference for one over the other.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 45 references that are in the map. First posted on arXiv 24 Apr 2025.
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11 of 45 references.
References · 11
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