Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology

Abstract

Abstract The integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the field, offering significant advancements in diagnostic accuracy and clinical efficiency. However, the complexity and opacity of AI models, particularly those involving machine learning (ML) and deep learning (DL), raise critical legal and ethical concerns due to their "black box" nature. This manuscript addresses these concerns by providing a comprehensive review of AI technologies in cardiovascular imaging, focusing on the challenges and implications of the black box phenomenon. We begin by outlining the foundational concepts of AI, including ML and DL, and their applications in cardiovascular imaging. The manuscript delves into the "black box" issue, highlighting the difficulty in understanding and explaining AI decision-making processes. This lack of transparency poses significant challenges for clinical acceptance and ethical deployment. The discussion then extends to the legal and ethical implications of AI's opacity. The need for explicable AI systems is underscored, with an emphasis on the ethical principles of beneficence and non-maleficence. The manuscript explores potential solutions such as explainable AI (XAI) techniques, which aim to provide insights into AI decision-making without sacrificing performance. Moreover, the impact of AI explainability on clinical decision-making and patient outcomes is examined. The manuscript argues for the development of hybrid models that combine interpretability with the advanced capabilities of black box systems. It also advocates for enhanced education and training programs for healthcare professionals to equip them with the necessary skills to utilize AI effectively. Patient involvement and informed consent are identified as critical components for the ethical deployment of AI in healthcare. Strategies for improving patient understanding and engagement with AI technologies are discussed, emphasizing the importance of transparent communication and education. Finally, the manuscript calls for the establishment of standardized regulatory frameworks and policies to address the unique challenges posed by AI in healthcare. By fostering interdisciplinary collaboration and continuous monitoring, the medical community can ensure the responsible integration of AI into cardiovascular imaging, ultimately enhancing patient care and clinical outcomes.

Keywords

• Artificial intelligence
• Cardiovascular imaging
• Machine learning
• Deep learning
• Black box phenomenon
• Explainable AI