Journal of Economics & Management Research

Artificial intelligence (AI) is rapidly transforming healthcare, offering the potential to improve diagnostic accuracy, personalize treatment plans, and optimize resource allocation. However, the increasing complexity and opacity of AI algorithms, particularly in deep learning models, pose a significant challenge to trust, accountability, and ultimately, patient safety. This research investigates the transparency gap in AI-driven healthcare, exploring the perceptions of healthcare professionals and patients regarding the explainability and interpretability of AI-based diagnostic and treatment recommendations. Through surveys, we examine the factors contributing to the transparency gap, the impact on trust and adoption, and potential strategies for bridging this gap through explainable AI (XAI) techniques and improved communication. The findings highlight the urgent need for enhanced transparency in AI-driven healthcare to ensure responsible and ethical deployment of these powerful technologies.