Interpretable Artificial Intelligence (XAI) in Biomedicine: Status, Challenges and Interdisciplinary Solutions

The integration of Big Data and Artificial Intelligence (AI) has brought about a paradigm shift in the biomedical field. offering innovative new healthcare solutions. Nevertheless, the opacity of the AI decision-making process greatly limits its clinical application in the biomedical field. Explainable Artificial Intelligence (XAI) emerges as a crucial remedy to this predicament. This paper comprehensively explores the applications, advantages, limitations, and potential solutions of XAI in the biomedical domain. X-Analytical Intelligence (XAI) is utilized in diagnostic imaging, drug development, and precision medicine to enhance transparency and accountability in clinical decision-making. It simplifies complex biomedical data, builds trust, meets regulatory requirements, and integrates polyomics data. However, XAI faces challenges like inconsistent interpretations, high computational resource requirements, data quality reliance, lack of standardized assessment methods, integration difficulties, ethical concerns, and limited generalisability. Solutions include enhanced model transparency, improved communication with clinical users, rigorous algorithm validation, and a robust data management framework. For the successful implementation of XAI in biomedicine, close interdisciplinary collaboration between AI developers and healthcare professionals is essential.