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

• URL: http://arxiv.org/abs/2511.01902v1
• Date: Fri, 31 Oct 2025 04:05:09 GMT
• Title: Before the Clinic: Transparent and Operable Design Principles for Healthcare AI
• Authors: Alexander Bakumenko, Aaron J. Masino, Janine Hoelscher,
• Abstract summary: We propose two foundational design principles to operationalize pre-clinical technical requirements for healthcare AI.<n>We ground these principles in established XAI frameworks, map them to documented clinician needs, and demonstrate their alignment with emerging governance requirements.<n>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.
• Score: 42.994619952353396
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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