A Practical guide on Explainable AI Techniques applied on Biomedical use case applications

Deep neural networks make accurate predictions but lack transparency: The paper identifies a critical problem where opaque machine learning models like DNNs are increasingly used for important decisions in critical environments (such as healthcare) without providing justifiable explanations for their outputs, creating potential legitimacy and accountability issues.

Explainable AI (XAI) techniques are essential for responsible deployment: XAI methods can verify and certify model outputs while enhancing them with key responsible AI principles including trustworthiness, accountability, transparency, and fairness—making AI systems more suitable for high-stakes biomedical applications.

Practical guidance and tools are needed for implementation: The paper addresses a gap in the field by providing a practical handbook with specific XAI techniques, real biomedical use cases, and executable Python code examples, making explainability accessible to practitioners beyond just theoretical researchers.

Different data types require different explanation approaches: The paper includes a flowchart to help practitioners select appropriate XAI methods based on their specific data types and use cases, recognizing that no single explanation technique works universally across all applications.

Biomedical applications demonstrate real-world importance: By focusing on biomedical use cases, the paper emphasizes that explainability is particularly critical in healthcare settings where decisions directly impact patient outcomes and clinical legitimacy is paramount.