A Comprehensive Literature Review on AI-Assisted Multimodal Triage Systems for Health Centers
Why this work is in the frame
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Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly recognized as a transformative tool in emergency department (ED) triage. Traditional triage methods, such as the Emergency Severity Index (ESI) and the Canadian Triage and Acuity Scale (CTAS), prioritize patient care based on acuity but face challenges, including subjectivity, overcrowding, and inefficient resource allocation. AI offers enhanced predictive accuracy, optimized patient prioritization, and reduced human error. This review examines the evolution of triage systems from conventional to AI-assisted models, highlighting advancements and limitations of AI in clinical practice. Recent findings underscore AI’s potential to improve diagnostic precision and streamline ED workflows. However, critical concerns include data dependency, ethical challenges, and variable performance across healthcare settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it