What does 'Explainability' in AI mean?

'Explainability' in AI refers to the capacity to understand how and why AI systems arrive at specific decisions or outputs. This concept is crucial in fields like dentistry because it builds trust and transparency between AI technologies and practitioners. When users can comprehend the rationale behind AI-driven recommendations or diagnoses, they are more likely to integrate these tools into their practice effectively.

In the context of AI applications in dentistry, explainability allows clinicians to validate the recommendations made by AI systems, ensuring that these align with clinical guidelines and the unique needs of individual patients. This understanding fosters collaboration between human professionals and AI technologies, enhancing overall patient care and decision-making processes. Therefore, the definition of explainability highlights the importance of interpretability in AI, particularly when it comes to user interface and trust, which are vital in a field that directly impacts patient health.