arXiv:2604.26991v2 Announce Type: replace 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. Prior work on AI fairness and human-AI cooperation, including learning to defer (L2D) and learning to complement (L2C), typically addresses these problems in isolation. We propose People-Centred Medical Image Analysis (PecMan), a framework for fairness-aware human-AI co-operative classification that jointly models subgroup-dependent
The increasing deployment of AI in sensitive fields like medicine necessitates robust solutions for fairness and human-AI collaboration as AI models become more integrated into critical decision-making processes.
A strategic reader should care because biased AI medical systems can lead to inequitable healthcare outcomes and erode public trust, requiring frameworks that ensure both efficacy and fairness in AI deployment.
This framework offers a method to jointly optimize AI performance and fairness in medical image analysis by explicitly modeling subgroup dependencies and enabling more effective human-AI cooperation.
- · Healthcare AI developers
- · Medical imaging diagnostics
- · Patients in diverse subgroups
- · Healthcare ethics organizations
- · Developers of unmoderated AI medical systems
- · Healthcare providers relying solely on black-box AI
Improved diagnostic accuracy and fairness in medical image analysis powered by AI.
Increased public and institutional trust in AI-driven medical tools, accelerating their adoption.
New regulatory standards and guidelines for fairness and explainability in high-stakes AI applications across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG