Evidence map›Paper›PMID 41514224›Full record

ArticleBMC emergency medicine2026

ChatGPT-4o assists emergency physicians in enhancing diagnostic accuracy for fever of unknown origin: retrospective analysis.

Hui Long, Guoqing Huang, Xinbo Yin, Xiaojie Zheng, Sijia Cao, Nan Wang, Xiangmin Li, Xiaokai Wang

Abstract read
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Article in BMC emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Hui LongDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Guoqing HuangDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xinbo YinDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiaojie ZhengDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Sijia CaoDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Nan WangDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiangmin LiDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiaokai WangDepartment of Emergency Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. xiaokaiwang@csu.edu.cn.

Funding

Peking Union Medical Foundation Ruiyi Emergency Medical Research Fund 22322012016
6 · The paper itself

Abstract

objectiveTo evaluate ChatGPT-4o’s diagnostic accuracy for fever of unknown origin (FUO) compared to emergency physicians and assess its utility as an adjunctive diagnostic tool.

methodsThis retrospective analysis included 60 adult patients presenting to the emergency department (ED) with FUO (fever ≥ 38.3℃ for ≥ 3 weeks without diagnosis after initial evaluation). Only patients with a confirmed final discharge diagnosis were included; FUO cases remaining undiagnosed at discharge were excluded. ChatGPT-4o and emergency medicine (EM) physicians independently generated preliminary diagnoses and comprehensive diagnoses. Diagnostic accuracy was compared against final discharge diagnoses. EM physicians subsequently revised their diagnoses after reviewing ChatGPT-4o’s output. Statistical analysis employed Welch’s ANOVA.

resultsChatGPT-4o significantly outperformed EM residents in both preliminary (70.0% vs. 46.11%; 95% CI, 57.6%-79.9% vs. 34.4%-59.3%, P = 0.008) and comprehensive diagnoses (75.0% vs. 55.0%, 95% CI, 62.9%-84.1% vs. 42.5%-66.9%, P = 0.002). While numerically higher than EM specialists in both preliminary (70.0% vs. 57.78%) and comprehensive diagnoses (75.0% vs. 68.89%), these differences did not demonstrate consistent statistical superiority. Incorporating ChatGPT-4o’s suggestions significantly improved accuracy for both EM residents (preliminary: 67.78% vs. 46.11%, 95% CI, 55.8%-78.6% vs. 34.4%-59.3%, P = 0.002; comprehensive: 78.33% vs. 55.0%, 95% CI, 66.8%-86.6% vs. 42.5%-66.9%, P = 0.01) and specialists (preliminary: 70.22% vs. 57.78%, P = 0.023), though the improvement in specialists’ comprehensive diagnoses remained non-significant (80.89% vs. 66.67%, P = 0.076). Stratified analysis showed that ChatGPT-4o significantly improved diagnostic accuracy across major etiologic categories, including infectious (76.0% vs. 59.4%, P = 0.003) and cancer-related causes (72.2% vs. 50.0%, P < 0.001).

conclusionsChatGPT-4o demonstrates potential to augment FUO diagnosis, particularly aiding less experienced clinicians. While this study highlights AI’s complementary value, prospective trials are needed to validate its impact on clinical efficiency.

Indexed as

Emergency MedicineFever of Unknown OriginAdultAgedEmergency Service, HospitalFemaleHumansMaleMiddle AgedRetrospective StudiesArtificial intelligenceChatGPT-4oEmergency medicineFever of unknown origin

Identifiers

PMID41514224
PMCPMC12882406

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