Before the Clinic: Transparent and Operable Design Principles for Healthcare AI

Transparent Design requires more than just explaining AI decisions: The paper emphasizes that healthcare AI systems need interpretability artifacts that allow clinicians to understand individual case-level reasoning and trace how the system arrived at specific recommendations—not just general explanations of how the AI works. This addresses a critical gap where existing explainable AI theory doesn't match what clinicians actually need to use AI safely in practice.

Uncertainty and calibration are as important as accuracy: Operable Design principles require development teams to explicitly measure and communicate how confident the AI system is in its predictions and ensure those confidence levels are reliable. The paper argues this is essential for predictable system behavior in real-world clinical conditions, yet is often overlooked during development before clinical evaluation.

Pre-clinical preparation can reduce friction in getting AI into hospitals: By establishing clear design principles and a "playbook" that development teams can follow before clinical trials, the paper aims to streamline the translation of AI from research to actual clinical use. This addresses a documented bottleneck where many promising AI systems never reach patients due to unclear requirements.

A shared vocabulary between technologists, clinicians, and regulators is missing: The paper identifies that AI researchers, healthcare practitioners, and governance bodies currently lack common language and expectations for what "explainability" means in practice, creating unnecessary delays and misalignment during clinical deployment.

Validated explainability requires caution about claims: The authors emphasize that while these design principles can be built and verified before clinical deployment, this pre-clinical work should not be confused with validated, deployed explainability—a distinction important for responsible claims about AI safety in healthcare.