Exploring explainable AI in pharmaceutical decision-making : Bridging the gap between black box models and clinical insights

Abstract

This study explores the crucial nexus between artificial intelligence (AI) and clinical decision-making in pharmaceuticals, highlighting the need to close the growing gap between black box models and clinical insights. The opaqueness of black box models creates questions about regulatory compliance, interpretability, and transparency as AI becomes more and more integrated into clinical applications, drug development, and discovery processes. Acknowledging the importance of Explainable AI (XAI) in this regard, we thoroughly examine XAI methods, focusing on their use in medical environments. The review highlights the need for responsible AI solutions by taking ethical and regulatory factors into account. We highlight effective XAI implementations in clinical decision support systems and drug development through case studies and evaluations. The study highlights adoption and technical barriers and makes suggestions to improve model interpretability without sacrificing effectiveness. By providing insights into the complex world of XAI in the pharmaceutical sector, this research opens the door for ethically sound, transparent, and well-informed AI applications.