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

A research team from Kazakhstan, led by Altynbek Seitenov and Ainur Nurzhanova, has published a study demonstrating the critical role of Explainable Artificial Intelligence (XAI) in building clinician trust for AI-driven medical diagnosis. Published on arXiv (cs.HC), the paper addresses the core problem of 'black box' AI models in healthcare, where doctors cannot understand how a system arrives at a diagnosis. The researchers conducted a structured survey with 30 medical students to measure the impact of explanations on trust, clarity, and perceived safety.

The results were statistically significant: understanding XAI showed a positive correlation with trust (r = 0.48, p = 0.01) and an even stronger correlation with perceived usefulness (r = 0.60, p = 0.001). This means explanations directly increase a clinician's confidence in an AI's recommendation. The study concludes that explainability is a non-negotiable factor for the successful integration of AI into clinical decision support systems.

However, the research also reveals a crucial nuance: despite increased trust from transparency, participants consistently preferred AI to function as a support tool rather than replacing human clinical judgment. This highlights that the goal of XAI in medicine is not to create autonomous diagnosticians, but to build collaborative, transparent systems that augment a doctor's expertise. The findings provide concrete evidence for developers and healthcare institutions that investing in interpretability features is essential for real-world adoption.