HOCUS POCUS - Exploring the use of Artificial Intelligence in Point of Care Ultrasound Application in the Emergency Department
Bibliographic record
Abstract
The field of emergency medicine has changed since the first introduction of portable ultrasound fifty years ago. Smaller equipment and the ability to produce higher quality images has driven the wide adoption of Point-of-Care Ultrasound (PoCUS) in emergency departments. PoCUS is an integral tool for physicians to obtain images in real-time and rapidly diagnose critically ill patients for timely intervention when every minute matters. Recognizing Core applications of PoCUS has been highlighted by the Canadian Association of Emergency Physicians and training programs are within the core curriculum for EM residency. However, the main challenge of PoCUS is that the diagnostic accuracy and interpretability is dependent on operator expertise. With the resurgent interest in artificial intelligence (AI) in healthcare, its integration into PoCUS was promising. Several studies have looked at integrating AI analysis with PoCUS imaging to improve diagnostic accuracy, guide training models, and increase the accessibility of PoCUS for novice users. Given that PoCUS usage improves patient satisfaction and clinician confidence, increased PoCUS usage is a worthy and achievable goal in Canadian emergency departments. The promising adaptation of artificial intelligence with PoCUS assessments will serve to expand training and diagnostic confidence to improve patient outcomes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".