This paper investigates multimodal affect recognition in AI-assisted Computerized Cognitive Training (CCT), comparing appraisal dimensions and categorical labels across young and older adult populations. It demonstrates that appraisal dimensions consistently outperform and generalize better than categorical labels, especially across different age groups.

This article proposes a practical framework for engineering measurable trust in clinical AI systems, moving beyond subjective impressions of model performance. It emphasizes integrating evidence, human supervision, and staged autonomy within a multi-layered architecture to ensure safety and accountability in healthcare applications.

This paper introduces a Multi-LLM Collaboration approach, guided by an "LLM Chemistry" framework, to enhance the reliability and trustworthiness of medication recommendations from clinical vignettes. The method aims to create effective, stable, and calibrated LLM ensembles by explicitly modeling interaction dynamics.