Aug 2026· Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care· Vol 15, pp. 62 - 66· 0 citations· 19 references
TL;DR
Results identify a "symmetry of modality": qualitative interviews correlate with written text explanations, while Think-Aloud protocols better assess cognitively demanding tools like SHAP values.
Abstract
While AI-enabled medical devices (AIeMD) are redefining healthcare, safety relies on human-AI interaction as much as algorithmic performance. This systematic review of 53 studies reveals that 58.2% of research omits Explainable AI (XAI) methods, despite transparency being a regulatory and safety necessity. Results identify a "symmetry of modality": qualitative interviews correlate with written text explanations, while Think-Aloud protocols better assess cognitively demanding tools like SHAP values. Currently, AI-specific risks like automation bias, driven by algorithmic opacity, are critically under-reported. To ensure safe adoption, practitioners must move beyond "user satisfaction" to treat transparency and XAI as safety-critical requirements for trust calibration.
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 r...
Mariana de Oliveira, Célia F. Cruz, Nuno Matela· Information Hiding· 0 citations
A scoping review of XAI applications in telemedicine is presented, examining 20 studies published between 2022 and 2026 to characterise the methods used, their evaluation approaches, and gaps between technical explainability and clinical usability.
Overall findings suggest that the effectiveness of decision-support systems depends not only on model performance and explanation quality but also on interaction design, and judicial protocols showed promise in mitigating automation bias and promoting active cognitive engagement in clinical decision support.
Samuele Pe, Laura Bergomi, G. Nicora et al.· Machine Learning and Knowled...· 0 citations
This mixed-methods evaluation suggests that a deliberately constrained, language-focused AI system can improve the accessibility of medical notes while preserving clinical accuracy and safety while extending into clinical interpretation.
Nicholas Lamb· Frontiers in Digital Health· 0 citations
It is argued that usability engineering should be a formative, safety-critical discipline integrated throughout the product lifecycle, including aftermarket deployment, and not merely a compliance task.
Preetha Moorthy, Till Nagel, Eva Hornecker· i-com· 0 citations
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