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

TL;DR

This paper introduces two foundational design principles, Transparent and Operable Design, to guide the development of healthcare AI systems before clinical deployment, ensuring they meet explainability, reliability, and governance needs.

Contribution

It provides a practical pre-clinical framework with actionable guidance for AI development teams, bridging explainability, clinician needs, and governance requirements.

Findings

Defines Transparent and Operable Design principles for healthcare AI

Aligns principles with clinician needs and governance standards

Offers a pre-clinical playbook to facilitate AI translation into clinical practice

Abstract

The translation of artificial intelligence (AI) systems into clinical practice requires bridging fundamental gaps between explainable AI theory, clinician expectations, and governance requirements. While conceptual frameworks define what constitutes explainable AI (XAI) and qualitative studies identify clinician needs, little practical guidance exists for development teams to prepare AI systems prior to clinical evaluation. We propose two foundational design principles, Transparent Design and Operable Design, that operationalize pre-clinical technical requirements for healthcare AI. Transparent Design encompasses interpretability and understandability artifacts that enable case-level reasoning and system traceability. Operable Design encompasses calibration, uncertainty, and robustness to ensure reliable, predictable system behavior under real-world conditions. We ground these principles in established XAI frameworks, map them to documented clinician needs, and demonstrate their alignment with emerging governance requirements. This pre-clinical playbook provides actionable guidance for development teams, accelerates the path to clinical evaluation, and establishes a shared vocabulary bridging AI researchers, healthcare practitioners, and regulatory stakeholders. By explicitly scoping what can be built and verified before clinical deployment, we aim to reduce friction in clinical AI translation while remaining cautious about what constitutes validated, deployed explainability.