临床应用之前：医疗人工智能的透明与可操作设计原则
Before the Clinic: Transparent and Operable Design Principles for Healthcare AI
- Clemson University（克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对医疗AI临床前缺乏实用指导的问题，提出透明设计与可操作设计两项基础原则，将临床前技术要求付诸实践，为开发团队提供可操作指南，加速临床评估并建立跨领域共享词汇。
AI中文摘要：
将人工智能（AI）系统应用于临床实践，需要弥合可解释AI理论、临床医生期望与治理要求之间的根本差距。尽管概念框架定义了可解释AI（XAI）的构成要素，定性研究也明确了临床医生的需求，但在临床评估前，开发团队在准备AI系统方面几乎没有获得实用指导。我们提出了两项基础设计原则——透明设计与可操作设计，将医疗AI的临床前技术要求付诸实践。透明设计包含可解释性与可理解性产物，以支持案例级推理和系统可追溯性。可操作设计包含校准、不确定性和鲁棒性，以确保在真实世界条件下系统行为可靠且可预测。我们将这些原则植根于既有的XAI框架，将其映射到已记录的临床医生需求，并展示它们与新兴治理要求的一致性。这份临床前指南为开发团队提供了可操作的指导，加速了临床评估进程，并建立了一个连接AI研究人员、医疗从业者和监管利益相关者的共享词汇表。通过明确界定在临床部署前可以构建和验证的内容，我们旨在减少临床AI转化中的摩擦，同时对已验证、已部署的可解释性构成保持谨慎。
英文摘要：
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.
