Finzel, BettinaBettinaFinzel0000-0002-9415-62542026-08-182026-08-182026https://fis.uni-bamberg.de/handle/uniba/116862As artificial intelligence (AI) increasingly permeates high-stakes domains such as healthcare, transportation, and law enforcement, ensuring its trustworthiness has become a critical challenge. This article proposes an integrative Explainable AI (XAI) framework to address the challenges of interpretability, explainability, interactivity, and robustness. By combining XAI methods, incorporating human-AI interaction and using suitable evaluation techniques, the implementation of this framework serves as a holistic XAI approach. The article discusses the framework’s contribution to trustworthy AI and gives an outlook on open challenges related to interdisciplinary collaboration, AI generalization and AI evaluation.engtrustworthy AIexplainable AIEU AI actintegrative XAI frameworksXAI in medicinearticleurn:nbn:de:bvb:473-irb-116862x