Evidence map›Paper›PMID 37727841›Full record

ArticleCureus2023

Efficacy of AI Chats to Determine an Emergency: A Comparison Between OpenAI's ChatGPT, Google Bard, and Microsoft Bing AI Chat.

Gabriel Zúñiga Salazar, Diego Zúñiga, Carlos L Vindel, Ana M Yoong, Sofia Hincapie, Ana B Zúñiga, Paula Zúñiga, Erin Salazar, Byron Zúñiga

Abstract read
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

9 authors.

Gabriel Zúñiga SalazarFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Diego ZúñigaFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Carlos L VindelFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Ana M YoongFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Sofia HincapieFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Ana B ZúñigaFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Paula ZúñigaFacultad de Ciencias Médicas, Universidad Católica de Santiago de Guayaquil, Guayaquil, ECU.
Erin SalazarNeurology, Hospital Luis Vernaza, Guayaquil, ECU.
Byron ZúñigaNephrology, Hospital Luis Vernaza, Guayaquil, ECU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background The escalating overload and saturation of emergency services, primarily caused by non-urgent cases overwhelming the system, have spurred a critical necessity for innovative solutions that can effectively differentiate genuine emergencies from situations that could be managed through alternative means, such as using AI chatbots. This study aims to evaluate and compare the accuracy in differentiating between a medical emergency and a non-emergency of three of the most popular AI chatbots at the moment. Methods In this study, patient questions from the online forum r/AskDocs on Reddit were collected to determine whether their clinical cases were emergencies. A total of 176 questions were reviewed by the authors, with 75 deemed emergencies and 101 non-emergencies. These questions were then posed to AI chatbots, including ChatGPT, Google Bard, and Microsoft Bing AI, with their responses evaluated against each other and the authors' responses. A criteria-based system categorized the AI chatbot answers as "yes," "no," or "cannot determine." The performance of each AI chatbot was compared in both emergency and non-emergency cases, and statistical analysis was conducted to assess the significance of differences in their performance. Results In general, AI chatbots considered around 12-15% more cases to be an emergency than reviewers, while they considered a very low number of cases as non-emergency compared to reviewers (around 35% fewer cases). Google Bard detected the most true emergency cases (87%) and true non-emergency cases (36%). However, no real difference in performance between the three AI chatbots was found in detecting true emergencies (p-value = 0.35) and non-emergency cases (p-value = 0.16).  Conclusions These AI systems require further refinement to identify emergency situations accurately, but they could potentially be an innovative tool for emergency care and improving patient outcomes. The integration of AI chatbots like ChatGPT, Google Bard, and Microsoft Bing Chat offers a promising avenue to mitigate ED strain and enhance emergency management.

Indexed as

ai & robotics in healthcareartificial intelligencechatbotchatgptemergencyemergency departmentemergency roomgoogle bardmicrosoft bing

Identifiers

PMID37727841
PMCPMC10506659

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.