Echoing Intelligence: Assessing the Capabilities of a Novel Generative AI With a Large Language Model in Cardiology Education
Bibliographic record
Abstract
Background: Artificial intelligence (AI) holds promise for enhancing medical education, particularly in complex fields like cardiology. We assessed the ability of a large language model (LLM) to generate and evaluate educational material comparable to that created by human experts. Methods: We trained an AI model on cardiology-specific content using 80 lectures from the St. Michael's Hospital Virtual Echo Rounds. The AI generated 10 multiple-choice questions (MCQs), and experienced cardiologists crafted an additional 10 MCQs. Eleven postgraduate year 4-6 cardiology trainees answered all 20 questions and attempted to identify the source (AI or human) of each question. The AI also answered the same set of questions. We analyzed performance using the Wilcoxon signed-rank test and recognition ability. Results: > 0.05). The AI achieved 95% accuracy on AI-generated questions and 100% on human-generated questions. Conclusions: The AI-generated educational content was of comparable quality to that produced by human experts, and trainees could not reliably distinguish between the 2 sources. Our findings suggest that AI could significantly augment cardiology education by providing high-quality, scalable learning resources.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".