THE USE OF ARTIFICIAL INTELLIGENCE IN EMERGENCY CASE TRANSPORT, DIAGNOSIS, AND TREATMENT
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) is rapidly entering prehospital emergency care, where time-critical triage, transport, and early treatment decisions determine outcomes. We systematically reviewed original studies evaluating AI tools used before hospital arrival, focusing on prediction/triage, diagnostic support, and transport optimization, and synthesized insights from contemporary reviews to contextualize clinical adoption. Seven original studies met inclusion for quantitative results synthesis: an ensemble waveform-based triage model predicting lifesaving interventions in trauma; an AI-enhanced regional platform guiding hospital selection and first aid; two studies on prehospital ST-elevation myocardial infarction (STEMI) detection (mini-12-lead and smartphone capture); a randomized trial of AI dispatcher alerts for out-of-hospital cardiac arrest; a gradient-boosted model for dyspnea serious adverse events; and a deep-learning severity algorithm predicting need for critical care in EMS. Across studies, AI frequently achieved AUCs around or above 0.80, improved sensitivity or operational timeliness (faster ECG interpretation/feedback), and in specific subgroups reduced adverse outcomes (lower mortality when AI guided optimal hospital transfer). However, not all trials showed clinical recognition gains despite superior model sensitivity, underscoring implementation challenges. Current reviews emphasize the promise of AI alongside the need for rigorous prospective validation, workflow integration, transparency, and equity. AI can augment prehospital decision-making, but robust clinical pathways and governance remain essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".