Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence, supervision, and operational boundaries of AI autonomy. This article proposes a practical framework for trustworthy clinical AI built around three principles: evidence, supervision, and staged autonomy. Rather than replacing deterministic clinical logic wholesale with end-to-end black-box models, the proposed approach combines a deterministic core, a patient-specific AI assistant for contextual validation, a multi-tier model escalation mechanism, and a human supervision layer for verification, escalation, and risk control. We demonstrate that trust also depends on selective verification of clinically critical findings, bounded clinical context, disciplined prompt architecture, and careful evaluation on realistic cases. Classifier-driven modular prompting is examined as an incremental path to scaling clinical depth without sacrificing prompt performance and without waiting for complete rule-based coverage. To operationalize trust, a set of trust metrics is proposed, built on metrological principles -- measurement uncertainty, calibration, traceability -- enabling quantitative rather than subjective assessment of each architectural layer. In this perspective, trustworthy clinical AI emerges not as a property of an individual model, but as an architectural outcome of a system into which evidence trails, human oversight, tiered escalation, and graduated action rights are embedded from the outset.

翻译：临床人工智能（AI）的信任不能简化为模型准确性、生成流畅性或用户总体正面印象。在医学领域，信任必须被设计为一种可度量的系统属性，其基础在于证据、监督以及AI自主性的操作边界。本文提出一个面向可信临床AI的实用框架，围绕三个原则构建：证据、监督与分阶段自主性。该方法并非用端到端黑箱模型完全替代确定性临床逻辑，而是结合确定性核心、用于情境验证的患者特定AI助手、多层级模型升级机制，以及用于验证、升级与风险控制的人工监督层。我们论证，信任还取决于对临床关键发现的选择性验证、受限临床上下文、严谨的提示架构，以及在真实病例上的审慎评估。分类器驱动的模块化提示被视为一条渐进路径，可在不牺牲提示性能、无需等待规则完全覆盖的情况下扩展临床深度。为实现信任的可操作性，本文提出了一套基于计量学原则——测量不确定度、校准、可追溯性——构建的信任度量体系，从而能够对每个架构层进行定量而非主观的评估。在此视角下，可信临床AI并非个别模型的属性，而是一个系统架构的结果：在该系统中，证据路径、人类监督、分级升级与渐进式操作权限从设计之初便被嵌入。