This project aims to establish robust methodologies for evaluating transparency and trust, integrating human factors and clinical performance into a multidimensional validation pipeline, ensuring that AI-enabled health solutions are clinically reliable, transparent, and compliant.
Mariana de Oliveira· Information Hiding· 0 citations
This position paper presents a manifesto for a longitudinal, three-fold methodological pivot in health human-AI interaction, and proposes moving beyond static satisfaction metrics towards relational metrics —Longitudinal Trust Calibration, Automation Bias Drift, and Error Recovery Velocity—that track the maturity and resilience of the human-AI partnership.
Mariana de Oliveira, Célia F. Cruz, Nuno Matela· Information Hiding· 0 citations
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