Interactive Data Visualization Techniques for Enhancing AI Decision Transparency in Healthcare Analytics: A Comparative Analysis

Artificial intelligence (AI) systems in healthcare increasingly influence critical clinical decisions, yet their complex decision-making processes often remain opaque to practitioners. This paper presents a systematic comparative analysis of interactive data visualization techniques designed to enhance AI decision transparency in healthcare analytics. A multi-dimensional classification framework was developed to categorize visualization approaches based on data type compatibility, interaction modality, transparency mechanism, and implementation complexity. Eighteen distinct visualization techniques were evaluated using a comprehensive assessment methodology combining quantitative performance metrics and qualitative expert evaluations across diverse healthcare contexts. The analysis revealed that parallel data and information visualization approaches achieved the highest transparency scores (4.5/5), while temporal visualization techniques demonstrated superior performance for longitudinal clinical data interpretation. Stream-based visualizations with adaptive smoothing algorithms proved particularly effective for patient flow pattern analysis. Strong correlation (r=0.78, p<0.001) was identified between interaction depth and transparency effectiveness. The research establishes evidence-based guidelines for implementing visualization solutions in clinical environments, addressing technical infrastructure requirements, workflow integration considerations, and user training recommendations. These findings provide a foundation for developing more transparent, interpretable AI systems that can effectively support clinical decision-making while maintaining appropriate levels of user trust and engagement.