EXPLAINABLE ARTIFICIAL INTELLIGENCE IN EVIDENCE BASED MEDICAL STATISTICS EDUCATION

Explainable Artificial Intelligence (XAI) in evidence-based medical statistics education can be described as a revolutionary innovation. It helps medical students to acquire better understanding of medical statistics. Although, there have some current challenges in the educational environment of medical institution. Statistical education has largely focused on output from algorithms and the interpretation of numbers. Explainable Artificial Intelligence allows students to understand how predictive outputs are influenced by individual clinical variables. This capability promotes a more in-depth understanding of the fundamental principles of statistics. On the other hand, it promotes the orientation of future clinical decisions toward evidence-based medicine. Through interactive visualization, model explanation and case-based learning scenarios, students explore complex relationships in statistics. They also identify biases and assess model reliability. Applying XAI in medical education, students acquire different skills like questioning and interpreting AI-driven recommendations. Generally, XAI in medical statistics education fits perfectly in the chasm between computational approaches and clinical reasoning. So, it turns future healthcare professionals into a differently trained analytical expert.