arXiv:2604. 26991v2 Announce Type: replace-cross Abstract: Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources.
arXiv:2607. 09102v1 Announce Type: cross Abstract: Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing.
arXiv:2504. 20908v3 Announce Type: replace Abstract: Current subgroup identification methods typically follow a two-step approach: first estimate conditional average treatment effects and then apply thresholding or rule-based procedures to define subgroups.
Benchmarking competitions are central to AI development in medical imaging, but it is unclear if they provide representative, accessible, and reusable data for clinical relevance. This study systematically examined 249 challenges (458 tasks) across 19 imaging modalities, finding limited geographic, modality, and problem-type representation. Additionally, many datasets suffer from restrictive access, ambiguous licensing, and poor documentation, hindering reproducibility and long-term reuse.
The paper investigates intersectional biases in multimodal clinical predictions using Electronic Healthcare Records (EHR). It introduces datasets MIMIC-Eye1 and MIMIC-IV ED, applies unified text representations from pre‑trained clinical language models, and benchmarks bias mitigation at the intersectional subgroup level. Results show that subgroup‑specific mitigation is robust across datasets, subgroups, and embeddings, effectively addressing intersectional biases in multimodal settings.
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
The paper introduces FRAME, a two‑step framework for auditing fairness claims in medical imaging. First, it derives a fair‑model reference distribution that captures the portion of subgroup performance differences attributable to sampling variation. Second, it tests the remaining difference using operators in representation space to assess whether demographic information or disease entanglement drives the bias. Across a large dataset of 702,206 images and 36 encoders, the reference explains a substantial median share of race and age differences, while interventions such as injecting demographic decodability or entangling disease direction have limited impact on the residual bias.
arXiv:2604.12411v2 Announce Type: replace Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit ove...
arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.
arXiv:2608. 19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing.
arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.
Subgroup performance differences are the standard evidence for fairness bias in medical imaging, and the usual response removes the demographic information that a model encodes. Here we introduce Fair...