The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..

中文翻译：

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在精神病学中使用机器学习：建立建立可信赖性的框架的需求。

人工智能在精神病学领域的迅速普及具有当下“狂野西部”的味道。一种跨学科的方法，该方法非常技术性和复杂性，但似乎产生了令人共鸣的发现。这些研究很难进行审查，因为这些方法通常是不透明的，并且很难找到合适的审查者组合。如果没有严格的框架来评估此类研究并因此培养可信赖性，那么这个问题只会变得更加复杂。因此，本文讨论了该领域迫切需要开发一个框架来评估复杂方法的紧迫性，以便诚实，公正，科学和准确地完成该过程。但是，评估是一个复杂的过程，因此我们专注于三个问题，即可解释性，透明性和可概括性，这些对于建立在精神病学中使用人工智能的可行性至关重要。我们讨论了定义这三个问题如何帮助建立确保可信度的框架，但显示了定义的难度，因为这些术语在医学，计算机科学和法律中具有不同的含义。我们得出结论，重要的是开始讨论，以便对此有政策的呼吁，并且社区在审查此类模型的临床应用时要格外小心。