Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT

Anuja Tayal, Devika Salunke, Barbara Di Eugenio, Paula Allen-Meares, Eulalia Puig Abril +3 more
4/24/2025
cs.CL

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Abstract

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.

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Cite this paper

@article{tayal2025conversational,
  title  = {Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT},
  author = {Anuja Tayal and Devika Salunke and Barbara Di Eugenio and Paula Allen-Meares and Eulalia Puig Abril and Olga Garcia and Carolyn Dickens and Andrew Boyd},
  year   = {2025},
  eprint = {2504.17753},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.17753v1}
}

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