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

Alexander Bakumenko, Aaron J. Masino, Janine Hoelscher

TL;DR

The paper tackles the translation gap in healthcare AI by proposing two pre-clinical design pillars—Transparent Design (interpretability and understandability) and Operable Design (calibration, uncertainty, and robustness). It defines concrete artifacts, validation approaches, and crosswalks to established XAI frameworks and governance, aiming to equip development teams with actionable guidance before clinical evaluation. The authors argue these pre-clinical artifacts accelerate governance readiness and clinical testing while avoiding overclaiming explainability. The framework is deliberately flexible across implementations and positioned as a living guide that complements, rather than replaces, existing regulatory and clinical evaluation standards.

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.

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

TL;DR

The paper tackles the translation gap in healthcare AI by proposing two pre-clinical design pillars—Transparent Design (interpretability and understandability) and Operable Design (calibration, uncertainty, and robustness). It defines concrete artifacts, validation approaches, and crosswalks to established XAI frameworks and governance, aiming to equip development teams with actionable guidance before clinical evaluation. The authors argue these pre-clinical artifacts accelerate governance readiness and clinical testing while avoiding overclaiming explainability. The framework is deliberately flexible across implementations and positioned as a living guide that complements, rather than replaces, existing regulatory and clinical evaluation standards.

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.

Paper Structure

This paper contains 21 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Transparent Design: Interpretability and Understandability
  3. Conceptual Foundation
  4. Interpretability Artifacts: Case-Level Explanations
  5. Understandability Artifacts: System Traceability
  6. Validation of Transparent Design Artifacts
  7. Operable Design: Calibration, Uncertainty, and Robustness
  8. Conceptual Foundation
  9. Calibration: Aligning Predictions with Observed Frequencies
  10. Uncertainty
  11. Robustness: Predictable Behavior Under Distribution Shift
  12. Designing for Future Monitoring
  13. Connecting Transparent and Operable Design Principles to Established Frameworks
  14. Scope, Boundaries, and Handoffs
  15. Framework Scope and Boundaries
  16. ...and 6 more sections

Figures (1)

  • Figure 1: Pre-Clinical AI Design Principles. Transparent Design (Interpretability, Understandability) ensures cognitive clarity; Operable Design (Calibration, Uncertainty, Robustness) ensures technical integrity. The dashed rectangle marks the pre-clinical scope (work that can be built and verified prior to clinical evaluation). Connection lines denote component membership without causality or temporal order.