The use of artificial intelligence in paediatric postgraduate medical education: A scoping review
Bibliographic record
Abstract
Background: Artificial intelligence (AI) encompasses a wide range of technologies that enable computers to mimic human intellect and is playing a significant role in healthcare education. Objectives: To review the current applications of AI in paediatric postgraduate medical education programs. Methods: A scoping review was conducted using a comprehensive literature search involving Ovid MEDLINE, Ovid Embase, and ERIC conducted from 1946 to May 27, 2024. Inclusion criteria involved articles that discussed AI in postgraduate paediatric education. Articles that addressed undergraduate education and other health professional education were excluded. Results: Nine articles met the inclusion criteria. Four studies were conducted in the United States, two in China, and one each in France, Korea, and Canada. The studies discussed the use of AI in general paediatrics, paediatric oncology, developmental paediatrics, and paediatric genetics. AI was used as a clinical decision support tool in postgraduate training in seven studies with mixed results on the accuracy of AI predictions. One study used AI models to assess residents' intubation competency, and another assessed the experiences and general perspectives of AI among paediatric residents and junior faculty. Conclusions: Amongst included studies, AI was largely used as a clinical decision support tool in paediatric postgraduate education and the accuracy of AI predictions are improved when large amounts of data are used to train and tune the AI model. As such, physicians should be trained in AI use and take an active role in training and tuning AI models on an ongoing basis to ensure appropriate use of AI in healthcare.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".