Mapping Algorithmic Bias in AI-Powered Electrocardiogram Interpretation Across the AI Life Cycle: Protocol for a Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Artificial intelligence (AI)-powered analysis of electrocardiograms (ECGs) is reshaping cardiac diagnostics, offering faster and often more accurate detection of conditions such as arrhythmias and heart failure. However, growing evidence suggests that algorithmic bias, defined as performance disparities across patient subgroups, may undermine diagnostic equity. These biases can emerge at any stage of the AI life cycle, including data collection, model development, evaluation, deployment, and clinical use. If unaddressed, they risk exacerbating health disparities, particularly in underrepresented populations and low-resource settings. Early identification and mitigation of such bias are essential to ensuring diagnostic equity. OBJECTIVE: This scoping review protocol outlines a structured approach to mapping the evidence on algorithmic bias in AI-enabled ECG interpretation. Following the population-concept-context framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidance, the planned review will systematically identify and categorize reported sources and types of bias, examine their effects on diagnostic performance across demographic and geographic subgroups, and document mitigation strategies applied throughout the AI life cycle. By synthesizing how bias and fairness considerations are handled in this field, this review aims to clarify existing evidence, highlight key gaps, and inform future efforts toward equitable and clinically trustworthy application of AI in cardiology. METHODS: We will conduct a comprehensive literature search across 5 electronic databases (PubMed, Embase, Cochrane CENTRAL, CINAHL, and IEEE Xplore) and gray literature sources. Eligible studies will include original research (2015-2025) evaluating the performance of AI-based ECG models across different subgroups or reporting on bias mitigation strategies. Two reviewers will independently screen studies, extract data using a standardized form, and resolve disagreements through consensus. This review will follow the PRISMA-ScR reporting framework. RESULTS: At the time of submission, study identification and screening has been completed. Database searches conducted in August and September 2025 yielded 430 records, with an additional 18 records identified through other sources. After duplicates removal, 398 unique records remained. Title and abstract screening led to the exclusion of 250 records, and 148 articles proceeded to full-text review. Following full-text assessment, 110 articles were evaluated for eligibility, of which 38 studies met the inclusion criteria and were included in the qualitative synthesis. The study selection process is summarized in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Data extraction was conducted between November and December 2025. CONCLUSIONS: This review will be the first to comprehensively map the landscape of algorithmic bias in AI-powered ECG interpretation. By identifying patterns of inequity and evaluating proposed solutions, it will provide actionable insights for developers, clinicians, and policymakers aiming to promote fairness in AI-enabled cardiac care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/82486.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».