Explainable AI: Current Status and Future Potential in Radiology | Original Article

ABSTRACT:

ABSTRACT

Explainable Artificial Intelligence (XAI) is rapidly evolving and holds significant promise for revolutionizing radiology practice. This paper presents an overview of the current status and future potential of XAI in radiology. We discuss the importance of XAI in enhancing transparency, interpretability, and trustworthiness in AI-driven decision-making processes within the radiological domain. Various XAI techniques, including visual explanations, textual descriptions, and example-based explanations, are examined in the context of their applications and limitations in radiology. Moreover, we explore emerging trends such as the integration of biological explanations and causal relationships into XAI frameworks. The paper underscores the need for collaborative efforts to define evaluation criteria and prioritize aspects crucial for the development of personalized XAI solutions tailored to the needs of clinicians, radiologists, and patients, while adhering to regulatory standards. By actively engaging in the direction of XAI, the radiology community can shape a future where AI-driven technologies optimize diagnostic accuracy, improve patient outcomes, and facilitate informed clinical decision-making.