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Evidence record 6596 · automatically gathered

Fairboard: a quantitative framework for equity assessment of healthcare models

Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across patient subgroups -- are rare. Here, we evaluate the equity of 18 open-source brain tumour segmentation models across 648 glioma patients from two independent datasets (n = 11,664 model inferences) along distinct univariate, Bayesian multivariate, spatial, and representational dimensions. We find that patient identity consistently explains more perfo

Record details

Published: 30 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Healthcare

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arXiv · 27 March 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias

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Shared topic: Bias & fairness · Shared topic: Healthcare

AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study

arXiv · 26 March 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

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arXiv · 3 April 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

The Imbalanced User-AI Relationships as an Ethical Failure of Front-End Design in Healthcare AI

arXiv · 24 March 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

MIRAGE: The Illusion of Visual Understanding

arXiv · 23 March 2026

How to cite this record

ethics.ai (30 March 2026), “Fairboard: a quantitative framework for equity assessment of healthcare models,” evidence record 6596, https://ethics.ai/record/6596 (originally published by arXiv).

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