ArticlePLOS digital health2026
Infectious disease diagnosis by artificial intelligence (AI): Differences in patient backgrounds and symptoms between antigen test positives and novel AI-powered pharyngeal endoscopy test positives.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A 3-Tier AI Model for COVID-19 Triage Using Pharyngeal Images: Algorithm Development and Validation.JMIR formative research · 2026Article
- Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.Current issues in molecular biology · 2026Review
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Authors and funding
4 authors.
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Abstract
This study aimed to identify differences in patient background and symptoms between individuals who tested positive using conventional rapid antigen (Ag) tests and those who tested positive using a novel artificial intelligence (AI)-powered pharyngeal endoscopy system. A total of 813 patients underwent both influenza/COVID-19 Ag testing and AI-powered endoscopic testing. We analyzed differences in patient characteristics and symptoms between the two test-positive groups. AI testing showed an overall percent agreement of 62% (95% confidence interval [CI] 58-66%) (442/713), a positive percent agreement of 47% (95% CI 40-53%) (125/269), and a negative percent agreement of 71% (95% CI 67-76%) (317/444) compared with Ag testing. Compared with Ag-positive cases, AI-positive cases exhibited a shorter interval from symptom onset to testing; median 18 hours (Interquartile range [IQR] 10-27) for AI+ and Ag-, 24 hours (IQR 18-41) for AI+ and Ag + , and 27 hours (IQR 17-47) for AI- and Ag+ (p < 0.001). In analyses comparing the AI+ and Ag- vs. AI- and Ag + , AI+ and Ag- were more frequently paediatric (<15 years old) (odds ratio [OR] 3.4 (95% CI 1.6-7.2), p = 0.001), tested earlier after symptom onset (<24 hours) (OR 2.6 (95% CI 1.3-4.9), p = 0.005), had contact with infected individuals (OR 4.6 (95% CI 2.2-9.3), p < 0.001), cough (OR 11 (95% CI 4.7-27), p < 0.001), and fever (≥38.0°C) (OR 5.6 (95% CI 2.8-11), p < 0.001), but showed lower frequencies of gastrointestinal symptoms (OR 0.2 (95% CI 0.05-0.9), p = 0.04). Notably, the AI system misdiagnosed 23% (23/99) of COVID-19-positive patients as influenza-positive, likely due to follicular lesions on the pharyngeal wall-a key feature used by the AI system for diagnosing influenza. These findings demonstrate the impact of differences in diagnostic methodologies between conventional Ag testing (which detects pathogen viral load) and novel AI testing (which assesses host immune response to viral infection) on the clinical characteristics of test-positive patients.
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