Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations

Inconsistent terminology is a major barrier to clinical adoption: The paper identifies fragmented XAI literature with overlapping methodologies and ad hoc validation as key obstacles. By establishing a pathology-centric vocabulary of seven core terms and a formal taxonomy, the authors demonstrate that standardized language is essential for clinicians and regulators to evaluate and trust AI systems in high-stakes medical settings.

Different clinical questions require different explanation methods: The paper establishes a task-driven framework mapping five specific clinical questions (such as diagnosis support or treatment prediction) to recommended XAI methods. This means a one-size-fits-all approach to AI explainability won't work—the type of explanation needed depends on what clinicians actually need to know to make decisions.

Five critical gaps exist between current XAI research and real-world clinical use: The paper identifies specific gaps between what XAI methods can currently do and what's needed for deployment in hospitals and clinics. Addressing these gaps—which likely include validation standards, regulatory clarity, and practical integration challenges—is necessary before computational pathology AI can be widely adopted in clinical practice.

Explainability is crucial for building accountability in AI-assisted diagnosis: The paper emphasizes that XAI systems enable verification and build trust, which are essential for safety and regulatory oversight. In computational pathology, where AI analyzes complex medical images to inform patient care, the ability to explain why an AI system made a particular recommendation is fundamental to clinical accountability.