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A practical review of generative AI in cardiac electrophysiology medical education

2025· review· en· W4407821436 on OpenAlexaff
Shaun A Hanycz, Pavel Antiperovitch

Bibliographic record

VenueJournal of Electrocardiology · 2025
Typereview
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsCardiac electrophysiologyGenerative grammarElectrophysiologyMedicineComputer scienceMedical physicsCardiologyInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Generative artificial intelligence (AI) is a component of artificial intelligence that creates synthetic multi-modal output in the form of text, images, and audio. Multiple approaches have been implemented into teaching surface ECG interpretation. However, learner performance remains poor. Generative AI in the form of Generative Adversarial Networks (GANs) is a novel AI model that has the potential to augment trainee ECG interpretation via creation of synthetic ECGs and anatomical depiction of conduction defects. Generative AI may be implemented in medical education to customize trainee surface ECG interpretation to improve learning and retention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.544
Teacher spread0.434 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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