arXiv:2606.18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities. MCI detection from spontaneous speech is promising for scalable screening. However, learned models often exploit demographic cues correlated with labels, resulting in a large performance gap across subgroups. We present a multimodal framework that combines (i) cross-model fusion between modalities (speech, text, and image), and (ii) unlearning using gradient reversal that discourages the shared embedding from en
The increasing sophistication of multimodal AI models and growing concern over bias in medical AI applications are converging to drive research into fair and robust diagnostic tools.
This research advances the practical application of AI in healthcare, specifically addressing critical issues of fairness and demographic bias in medical diagnostics, which could improve access and accuracy.
The development of unlearning techniques for fair cognitive impairment detection could lead to more equitable and trustworthy AI-driven medical screening, reducing disparities in diagnosis and treatment.
- · Healthcare sector
- · Elderly populations
- · AI ethics researchers
- · Multimodal AI developers
- · Developers of biased diagnostic AI models
- · Populations underserved by current biased AI
Improved early and equitable detection of Mild Cognitive Impairment, potentially leading to better patient outcomes.
Increased trust and adoption of AI in sensitive medical diagnostics, fostering broader integration of AI into healthcare.
New regulatory frameworks and industry standards for fairness and bias mitigation in medical AI, influencing development across the entire AI landscape.
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Read at arXiv cs.LG