How Can Explainable Artificial Intelligence Improve Trust and Transparency in Medical Diagnosis Systems?
This addresses the need for transparency in AI-assisted medical tools for clinicians, but it is incremental as it builds on existing XAI proposals with a specific survey-based evaluation.
This study tackled the problem of low trust in AI-driven medical diagnosis systems by investigating how explainable AI (XAI) affects trust, finding that explanations significantly increase trust, clarity, and perceived safety, with knowledge of XAI positively correlating with trust (r = 0.48) and perceived usefulness (r = 0.60).
The growing adoption of artificial intelligence in healthcare has raised concerns about the transparency and trustworthiness of AI-driven medical diagnosis systems. Many existing models operate as black boxes, limiting clinicians' ability to understand how decisions are made. Explainable Artificial Intelligence (XAI) has been proposed as a solution to improve transparency, interpretability, and trust in AI-assisted medical tools. This study investigates the relationship between explainability and trust in AI-based diagnostic systems. A structured survey of 30 medical students was conducted to examine the influence of XAI understanding, confidence in AI decisions, perceived usefulness, and adoption intentions. The results indicate that explanations significantly increase trust, clarity, and perceived safety of AI recommendations. Knowledge of XAI showed a positive correlation with trust (r = 0.48, p = 0.01) and perceived usefulness (r = 0.60, p = 0.001). The findings suggest that explainability is a key factor for successful integration of AI in healthcare decision support systems. While AI explanations improve transparency and trust, participants still prefer AI to function as a support tool rather than replacing human clinical judgment.