Desired Clinical Applications of Artificial Intelligence in Emergency Medicine: An International e-Delphi Study
Bibliographic record
Abstract
Abstract Objective Artificial intelligence (AI) in emergency medicine has been increasingly studied over the past decade. However, the implementation of AI requires significant buy-in from end-users. This study explored desired clinical applications of AI by emergency physicians. Methods A 3-round Delphi process was undertaken using STAT59 software. An international expert panel was assembled through purposeful sampling to reflect a diversity in geography, age, time in practice, practice setting, role, and expertise. Items generated in Round 1 were collated by the study team and ranked in Rounds 2 and 3 on a 7-point linear numeric scale of importance. Consensus was defined as a standard deviation of 1.0 or less. Results Of 66 invited experts, 29 completed Round 1, 25 completed Round 2, and 23 completed Round 3. Three statements reached consensus in Round 2 and four statements reached consensus in Round 3, including safe prescribing, guiding choice of drug, adjusting drug doses, identifying risk or prognosis, and reporting/interpreting investigation results. Conclusions Many desired clinical applications of AI in emergency medicine have not yet been explored. Clinical and technological experts should co-create new applications to ensure buy-in from all stakeholders. Specialty organizations can lead the way by establishing local clinical priorities.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 teacher head, 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".