Chronic disease management requires regular adherence feedback to prevent avoidable hospitalizations, yet clinicians lack time to produce personalized patient communications. Manual authoring preserves clinical accuracy but does not scale; AI generation scales but can undermine trust in patient-facing contexts. We present a clinician-in-the-loop interface that constrains AI to data organization and preserves physician oversight through recognition-based review. A single-page editor pairs AI-generated section drafts with time-aligned visualizations, enabling inline editing with visual evidence for each claim. This division of labor (AI organizes, clinician decides) targets both efficiency and accountability. In a pilot with three physicians reviewing 24 cases, AI successfully generated clinically personalized drafts matching physicians' manual authoring practice (overall mean 4.86/10 vs. 5.0/10 baseline), requiring minimal physician editing (mean 8.3\% content modification) with zero safety-critical issues, demonstrating effective automation of content generation. However, review time remained comparable to manual practice, revealing an accountability paradox: in high-stakes clinical contexts, professional responsibility requires complete verification regardless of AI accuracy. We contribute three interaction patterns for clinical AI collaboration: bounded generation with recognition-based review via chart-text pairing, automated urgency flagging that analyzes vital trends and adherence patterns with fail-safe escalation for missed critical monitoring tasks, and progressive disclosure controls that reduce cognitive load while maintaining oversight. These patterns indicate that clinical AI efficiency requires not only accurate models, but also mechanisms for selective verification that preserve accountability.

翻译：慢性疾病管理需要定期提供依从性反馈以预防可避免的住院治疗，但临床医生缺乏时间制作个性化的患者沟通材料。人工撰写能保持临床准确性但无法规模化；人工智能生成虽可规模化，但在面向患者的场景中可能削弱信任度。我们提出一种临床医生参与循环的交互界面，通过基于认知的审核机制将AI约束在数据组织层面并保留医师监督权。单页编辑器将AI生成的章节草稿与时间对齐的可视化图表配对，支持结合可视化证据进行逐项声明内联编辑。这种分工模式（AI组织数据，临床医生决策）同时瞄准效率与责任归属。在三位医师审核24个病例的试点研究中，AI成功生成了符合医师人工撰写实践的临床个性化草稿（总体均值4.86/10对比基线5.0/10），仅需极少的医师编辑（平均8.3%内容修改）且零安全关键问题，证明了内容生成自动化的有效性。然而，审核时间仍与人工实践相当，揭示出责任悖论：在高风险临床场景中，职业责任要求进行完整核查，与AI准确度无关。我们贡献了三种临床AI协作的交互模式：通过图表-文本配对实现基于认知审核的边界生成、通过分析生命体征趋势与依从性模式并配备关键监测任务遗漏时的故障保护升级机制的自动紧急标记系统，以及能在保持监督的同时降低认知负荷的渐进式披露控制机制。这些模式表明，临床AI效率不仅需要精确模型，更需要能保持责任归属的选择性验证机制。