Implementing XAI in life sciences: Key challenges and pathways to solutions

Abstract

The growing adoption of artificial intelligence (AI) in life sciences has been paralleled by growing concerns regarding transparency, interpretability, and trustworthiness of predictive models. While explainable artificial intelligence (XAI) has emerged as a powerful framework to bridge this gap, its practical deployment continues to face substantial technical, ethical, and regulatory barriers. This review provides a comprehensive overview of the challenges associated with implementing XAI in life science applications—including data complexity, model heterogeneity, computational costs, clinical integration, and ethical considerations—and discusses potential solutions and strategies to address them. By mapping recent advances in methodological approaches, regulatory frameworks, and interdisciplinary collaborations, we highlight a roadmap for embedding explainability into the AI lifecycle. The paper concludes with future perspectives on harmonizing interpretability with predictive performance in critical domains such as drug discovery, medical diagnostics, and bioinformatics.