ArticleCJC open2025
Echoing Intelligence: Assessing the Capabilities of a Novel Generative AI With a Large Language Model in Cardiology Education.
Article in CJC open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Accuracy of ChatGPT and DeepSeek in answering clinical questions from the 2025 Society for Cardiovascular Angiography & Interventions/Heart Rhythm Society left atrial appendage occlusion guidelines.The Journal of international medical research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed 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: Trainees scored similarly on AI-generated and human-generated questions (median 8/10 vs 8/10; 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.