Explainable AI: Definition and attributes of a good explanation for health AI

Accuracy alone is insufficient for useful AI in healthcare: The paper emphasizes that even highly accurate AI predictions are not enough—doctors and patients also need to understand why the AI made a particular recommendation, which requires transparent explanations of the system's reasoning.

There is no universal standard for "good" explanations in health-AI: Despite growing demand for explainable AI in medicine, the paper found that definitions of what makes an explanation adequate remain inconsistent and ad hoc across the field, highlighting a critical gap in how we evaluate AI systems in healthcare.

Complexity creates a transparency problem: As AI models become more sophisticated and accurate, they often become "black boxes" that are harder for users to understand, which poses safety and accountability risks in healthcare settings where decisions directly affect patient care.

Expert consensus identifies specific attributes of good explanations: Through a Delphi study with experts, the research developed a comprehensive list of characteristics that distinguish good explanations in health-AI, providing a framework that can guide future development of more trustworthy AI systems.

Explainability is essential for safety-critical applications: The paper stresses that in healthcare—where AI recommendations influence treatment decisions—the ability to explain AI reasoning is not optional but fundamental to ensuring accountability and protecting patient safety.