How Can Explainable Artificial Intelligence Improve Trust and Transparency in Medical Diagnosis Systems?

TL;DR

This study explores how explainable AI enhances trust and transparency in medical diagnosis systems, showing explanations increase perceived safety and trust among medical students.

Contribution

It provides empirical evidence that explainability significantly improves trust and perceived usefulness in AI healthcare tools.

Findings

Explanations increase trust, clarity, and perceived safety.

Knowledge of XAI correlates positively with trust and usefulness.

Participants prefer AI as a support tool, not a replacement.

Abstract

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.