Vol. 1 No. 1 (2026): Evaluating the Effectiveness of Explainable Artificial Intelligence for Improving User Trust, Transparency, and Decision-Making Accuracy in Healthcare Applications, july-september, 2026, pg 12-26

Abstract:

The implications of explainability AI in healthcare promises enhanced clinical effectiveness and diagnostic accuracy. The adoption of AI reduces the negative effects of black-box nature and prevents those tools to undermine patient trust and clinical enhancement. This study has integrated four themes to evaluate effectiveness of Explainable AI to enhance transparency and integrity. With major thematic analysis and evidence-based credibility analysis, this study has examined user interactions influencing human-AI collaboration. This study asserts that trust mediates the interaction between adoption intention and trust. The findings of this study expressed that XAI’s prototypes improve perceived transparency and increase trust when explanations are critically unreadable and under constructive.  However, extensive technical detailing minimised decision accuracy by causing cognitive overload. The major results of this study are that trust mediates the interconnection between adoption intention and explainability. This research also requires responsible AI deployment that proposes clinically-focused explainability models.

Keywords: Explainable AI, User Trust, Transparency, Healthcare Application, Adoption Intention, User Trust, Transparency