ArticleScandinavian journal of primary health care2026
Diagnostic accuracy of otitis media with and without a fictitious AI support among physicians in primary care and medical students.
Article in Scandinavian journal of primary health care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOtitis media (OM) in children is a common infection in primary care, contributing to a significant global health and economic burden. In high-income countries, diagnostic inaccuracy leads to over-diagnosis of acute OM (AOM) and over-prescribing of antibiotics, which may contribute to antibiotic resistance.
aimTo investigate the diagnostic accuracy and the influence of artificial intelligence (AI) in diagnosing OM among primary care physicians and medical students.
methodA diagnostic accuracy study in which primary care physicians and medical students diagnosed AOM, OM with effusion (OME), and normal eardrums using 21 high-quality digital images, both without and with a fictitious AI support. We estimated the technological impact of the fictitious AI support.
resultsOverall diagnostic accuracy was 64% without, and 75% with AI support. The most experienced physicians reached 69% without, and 80% with AI; the least experienced 61% without, and 73% with AI; medical students reached 64% without, and 74% with AI. Accuracy for AOM was 77% without and 86% with AI, and for OME 46% without and 66% with AI. Mean diagnostic confidence increased significantly with AI support. The technological impact was 1.4. Automation bias was 1.2 overall, 0.9 for the most experienced and 1.2 for the least experienced physicians.
conclusionWe report modest diagnostic accuracy for OM among primary care physicians and medical students. The fictitious AI support system improved both accuracy and diagnostic confidence and reduced over-diagnosis. The most experienced physicians achieved the highest accuracy, the less experienced were more often misled by the fictitious AI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.