Performance of ChatGPT versus spine surgeons as an emergency department spine call consultant.

dc.contributor.author

Taka, Taha M

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Sebt, Seena

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Meng, Sarah

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Cabrera, Andrew

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Shin, David

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Yacoubian, Vahe

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Chao, Weyjuin

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Rossie, Daniel

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Xu, Zhengle

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Erickson, Melissa

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Rocos, Brett

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Than, Khoi

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Yu, Elizabeth

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Ahn, Nicholas

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Bono, Christopher

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Cheng, Wayne

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Danisa, Olumide

dc.date.accessioned

2026-01-29T14:09:24Z

dc.date.available

2026-01-29T14:09:24Z

dc.date.issued

2026-03

dc.description.abstract

Background

Large language models (LLMs) like ChatGPT are increasingly being recognized as credible tools for use across diverse healthcare settings. While artificial intelligence (AI) use has previously been evaluated in emergency medicine, its use in subspecialty care - particularly spine surgery - remains underexplored. This study evaluates the clinical accuracy, management appropriateness, completeness, helpfulness, and overall quality of ChatGPT responses compared to those of board-certified, spine surgeons in response to common emergency department (ED) consultations.

Methods

A 7-part questionnaire was developed based on common ED spine consultations (eg, Cauda Equina Syndrome, compression fracture in elderly patients, purulent drainage from surgical wound, acute lumbar disc herniation, incomplete spinal cord injury, epidural abscess, and metastatic spine disease). Each case included 3-4 questions pertaining to examination, diagnosis, management, and counseling. Responses from ChatGPT and 7 board-certified spine surgeons were restricted to 3-4 sentences per question. Three emergency medicine physicians rated each de-identified questionnaire response using a 5-point Likert scale. Statistical analysis was conducted using a 2-sample T-test with unequal variance. Inter-rater reliability was assessed using pairwise weighted Cohen's kappa coefficient (κ).

Results

When comparing AI responses versus spine surgeon responses to proposed ED consultations, AI responses were rated to be superior across all 5 metrics of clinical accuracy, management appropriateness, completeness, helpfulness, and overall quality (p<.05). Inter-rater reliability was assessed using the average pairwise weighted Cohen's kappa coefficient which showed substantial agreement (κ=0.76).

Conclusions

ChatGPT responses to emergency department spine consultations were rated as significantly higher compared to board-certified spine surgeons by emergency medicine providers. Though further improvement and validation is warranted, these findings suggest that ChatGPT can be a useful clinical adjunct for spine-related emergency department consultations.
dc.identifier

S2666-5484(25)00256-2

dc.identifier.issn

2666-5484

dc.identifier.issn

2666-5484

dc.identifier.uri

https://hdl.handle.net/10161/33965

dc.language

eng

dc.publisher

Elsevier BV

dc.relation.ispartof

North American Spine Society journal

dc.relation.isversionof

10.1016/j.xnsj.2025.100836

dc.rights.uri

https://creativecommons.org/licenses/by-nc/4.0

dc.subject

Artificial Intelligence

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Chatgpt

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Consult

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Emergency

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Spine

dc.title

Performance of ChatGPT versus spine surgeons as an emergency department spine call consultant.

dc.type

Journal article

duke.contributor.orcid

Rocos, Brett|0000-0002-0808-5585

duke.contributor.orcid

Danisa, Olumide|0000-0003-0173-7525

pubs.begin-page

100836

pubs.organisational-group

Duke

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School of Medicine

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Clinical Science Departments

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Orthopaedic Surgery

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Neurosurgery

pubs.publication-status

Published online

pubs.volume

25

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