Evidence map›Paper›PMID 42604920›Full record

ArticleCardiovascular ultrasound2026

Evaluating sex bias in artificial intelligence-based echocardiographic analysis: a retrospective study on individuals with normal cardiac function and patients with systolic and diastolic dysfunction.

Filippa Fredlund Blixt, Anna Bjällmark

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Article in Cardiovascular ultrasound, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

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2 authors.

Filippa Fredlund BlixtDepartment of Clinical Diagnostics, School of Health and Welfare, Jönköping University, Jönköping, Sweden. filippa.fredlundblixt@ju.se.
Anna BjällmarkDepartment of Clinical Diagnostics, School of Health and Welfare, Jönköping University, Jönköping, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to improve echocardiography. However, AI systems may exhibit sex bias, reproducing societal stereotypes and performing worse for certain groups, potentially leading to unequal healthcare outcomes. Therefore, the aim of this study was to investigate whether there are sex-related differences in the agreement between a fully automated AI-based analysis and routine echocardiographic report generated by the interpreting cardiologist in patients undergoing transthoracic echocardiography.

methodsThis retrospective study evaluated seven echocardiographic parameters in 95 women and 88 men by comparing AI-generated values with clinical assessments. Participants included individuals with normal cardiac function (34 women, 31 men) and patients with systolic (30 women, 29 men) or diastolic dysfunction (31 women, 28 men). Parameters included ejection fraction, left ventricular volume, left atrial volume, é velocities (septal and lateral), tricuspid valve regurgitation, and E/é ratio. Potential sex-based differences were assessed by comparing agreement between clinical and AI-generated values in women and men using correlations, absolute errors, Bland-Altman analyses and AUC values.

resultsCorrelation coefficients in women ranged from 0.67 to 0.93 and in men from 0.32 to 0.93. Absolute errors did not differ significantly between sexes for five of the seven evaluated parameters. However, men showed a higher absolute error for TR velocity (0.15 vs. 0.07, p = 0.001), whereas women showed a higher absolute error for the E/é ratio (0.76 vs. 0.39, p < 0.001). Bland-Altman analysis showed larger mean differences in men for five of seven parameters. The magnitude of the differences in AUC values between women and men was small, ranging from 0.017 (TR velocity) to 0.048 (left ventricular ejection fraction).

conclusionsThis study did not demonstrate systematic sex-related differences in AI performance, but the findings require confirmation in larger cohorts. Continued monitoring for bias remains important as AI integration in echocardiography expands.

Indexed as

Artificial IntelligenceEchocardiographySexismStroke VolumeVentricular Dysfunction, LeftVentricular Function, LeftAdultAgedDiastoleFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesSex FactorsArtificial intelligenceDiastolic functionEchocardiographyImaging systemSex differencesSystolic functionUltrasound

Identifiers

PMID42604920
PMCPMC13479603

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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.