What question did this study set out to answer?

The aim was to identify differences in patient characteristics and symptoms between those who tested positive via antigen tests and AI-powered testing.

February 12, 2026PLOS Digital Health0 citationsOpen Access

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

MMMasahiko Mori SYShinji Yoshinaga TMTadayoshi Moriyama

Key Points

The aim was to identify differences in patient characteristics and symptoms between those who tested positive via antigen tests and AI-powered testing.
Conducted a comparative analysis of patient backgrounds and symptoms from 813 individuals
Utilized both conventional rapid antigen tests and AI-powered pharyngeal endoscopic testing
Statistically analyzed percent agreements and odds ratios for symptoms and characteristics
AI testing showed 62% overall agreement with antigen testing
AI-positive cases reported a median symptom onset-to-testing interval of 18 hours
AI-positive individuals were more likely to be under 15 years old, tested within 24 hours, and present with cough and fever
AI misdiagnosed 23% of COVID-19 patients as influenza-positive

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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