Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
arXiv:2607.07717v1 Announce Type: new Abstract: In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deployment fairness problem as an audit question: after a long-tailed multi-label CXR model is converted from scores into decisions, who is missed? Across VinDr-CXR and MIMIC-CXR/CXR-LT, we use a diagnostic ladder to separate class-level long-tail losses, subgroup-aware weighting, group robustness, and threshold selection. On VinDr-CXR, group-tail weighting followed by tail-awar
The proliferation of AI in healthcare, particularly in diagnostic imaging, necessitates rigorous evaluation of fairness and equity as these systems move towards real-world deployment.
Ensuring AI diagnostic tools do not exacerbate existing healthcare disparities by underdiagnosing rare conditions or specific demographic groups is critical for responsible AI and public trust.
This research provides a framework for auditing AI models for 'subgroup underdiagnosis' in long-tailed medical datasets, indicating a maturing focus beyond aggregate performance to fine-grained fairness in real-world applications.
- · AI fairness researchers
- · Healthcare regulators
- · Underserved patient subgroups
- · AI developers ignoring fairness
- · Hospitals deploying un-audited AI
Further research and development will focus on methods to mitigate subgroup underdiagnosis in long-tailed medical AI models.
Regulatory bodies may begin to mandate specific fairness audits and reporting for AI systems used in high-stakes diagnostic contexts.
The pursuit of 'AI for all' in healthcare will increasingly involve transparent methodologies for identifying and rectifying biases, potentially slowing deployment but increasing societal benefit.
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Read at arXiv cs.LG