Evidence map›Paper›PMID 39416714›Full record

ArticleEpilepsy & behavior reports2024

The potential of large language model chatbots for application to epilepsy: Let's talk about physical exercise.

Rizia Rocha-Silva, Bráulio Evangelista de Lima, Geovana José, Douglas Farias Cordeiro, Ricardo Borges Viana, Marília Santos Andrade, Rodrigo Luiz Vancini, Thomas Rosemann, Katja Weiss, Beat Knechtle and 2 more

Abstract read
In one paragraph

Article in Epilepsy & behavior reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Rizia Rocha-SilvaFaculty of Physical Education and Dance, Federal University of Goiás, Goiânia, Brazil.
Bráulio Evangelista de LimaFaculty of Physical Education and Dance, Federal University of Goiás, Goiânia, Brazil.
Geovana JoséFaculty of Information and Communication, Federal University of Goiás, Goiânia, Brazil.
Douglas Farias CordeiroFaculty of Information and Communication, Federal University of Goiás, Goiânia, Brazil.
Ricardo Borges VianaInstitute of Physical Education and Sports, Federal University of Ceará, Fortaleza, Brazil.
Marília Santos AndradeDepartment of Physiology, Federal University of São Paulo, São Paulo, Brazil.
Rodrigo Luiz VanciniCenter for Physical Education and Sports, Federal University of Espírito Santo, Vitória, Brazil.
Thomas RosemannInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Katja WeissInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Beat KnechtleInstitute of Primary Care, University of Zurich, Zurich, Switzerland.
Ricardo Mario AridaDepartment of Physiology, Federal University of São Paulo, São Paulo, Brazil.
Claudio Andre Barbosa de LiraFaculty of Physical Education and Dance, Federal University of Goiás, Goiânia, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we discuss how artificial intelligence chatbots based on large-scale language models (LLMs) can be used to disseminate information about the benefits of physical exercise for individuals with epilepsy. LLMs have demonstrated the ability to generate increasingly detailed text and allow structured dialogs. These can be useful tools, providing guidance and advice to people with epilepsy on different forms of treatment as well as physical exercise. We also examine the limitations of LLMs, which include the need for human supervision and the risk of providing imprecise and unreliable information regarding specific or controversial aspects of the topic. Despite these challenges, LLM chatbots have demonstrated the potential to support the management of epilepsy and break down barriers to information access, particularly information on physical exercise.

Indexed as

Artificial intelligenceChatbotPhysical exercise

Identifiers

PMID39416714
PMCPMC11480856

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.