Artificial intelligence for the triage of COVID-19 patients at the emergency department: a systematic review
Bibliographic record
Abstract
The aim of this article is to systematically analyze the available literature on the efficacy and validity of artificial intelligence (AI) applied to medical imaging techniques in the triage of patients with suspected or confirmed coronavirus disease 2019 (COVID-19) in Emergency Departments (EDs). A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. Medline, Web of Science, and Scopus were searched to identify observational studies evaluating the efficacy of AI methods in the diagnosis and prognosis of COVID-19 using medical imaging. The main characteristics of the selected studies were extracted by two independent researchers and were formally assessed in terms of methodological quality using the Newcastle-Ottawa scale. A total of 11 studies, including 14,499 patients, met inclusion criteria. The quality of the studies was medium to high. Overall, the diagnostic yield of the AI techniques compared to a gold standard was high, with sensitivity and specificity values ranging from 79% to 98% and from 70%to 93%, respectively. The methodological approaches and imaging datasets were highly heterogeneous among studies. In conclusion, AI methods significantly boost the diagnostic yield of medical imaging in the triage of COVID-19 patients in the ED. However, there are significant limitations that should be overcome in future studies, particularly regarding the heterogeneity and limited amount of available data to train AI models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".