Clinical decision support systems (CDSS) augmented with artificial intelligence (AI) models are emerging as potentially valuable tools in healthcare. Despite their promise, the development and implementation of these systems typically encounter several barriers, hindering the potential for widespread adoption. Here we present a case study of a recently developed AI-CDSS, Aifred Health, aimed at supporting the selection and management of treatment in major depressive disorder. We consider both the principles espoused during development and testing of this AI-CDSS, as well as the practical solutions developed to facilitate implementation. We also propose recommendations to consider throughout the building, validation, training, and implementation process of an AI-CDSS. These recommendations include: identifying the key problem, selecting the type of machine learning approach based on this problem, determining the type of data required, determining the format required for a CDSS to provide clinical utility, gathering physician and patient feedback, and validating the tool across multiple settings. Finally, we explore the potential benefits of widespread adoption of these systems, while balancing these against implementation challenges such as ensuring systems do not disrupt the clinical workflow, and designing systems in a manner that engenders trust on the part of end users.

翻译：临床决策支持系统（CDSS）结合人工智能（AI）模型，正逐渐成为医疗领域中具有潜在价值的工具。尽管前景广阔，但这些系统的开发与实施通常面临多重障碍，阻碍其广泛推广的潜力。本文以近期开发的AI-CDSS——Aifred Health为案例，该系统旨在支持重度抑郁症的治疗选择与管理。我们探讨了该AI-CDSS在开发与测试过程中秉持的原则，以及为促进实施而制定的实用解决方案。同时，我们提出了在AI-CDSS构建、验证、培训及实施全过程中需考虑的建议，包括：识别关键问题、基于问题选择机器学习方法、确定所需数据类型、确定CDSS提供临床效用的格式要求、收集医生与患者反馈，以及在多场景下验证工具。最后，我们探讨了广泛采用这些系统的潜在益处，同时权衡其实施挑战，例如确保系统不干扰临床工作流程，并以能赢得最终用户信任的方式设计系统。