对精神病学人工智能的期望。
Expectations for Artificial Intelligence (AI) in Psychiatry.
机构信息
Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, 49684, USA.
ChronoRecord Association, Fullerton, CA, USA.
出版信息
Curr Psychiatry Rep. 2022 Nov;24(11):709-721. doi: 10.1007/s11920-022-01378-5. Epub 2022 Oct 10.
Abstract
PURPOSE OF REVIEW
Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.
RECENT FINDINGS
For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
摘要
目的综述
人工智能（AI）常被视为临床医学的变革性技术，尽管当前 AI 的技术成熟度较低。本叙述性综述的目的是描述导致技术成熟度低的复杂原因，并为 AI 在临床医学中的安全、常规使用设定切合实际的期望。
最近发现
为使 AI 在临床医学中具有生产力，需要解决许多导致其低成熟度的不同因素。这些因素包括数据质量、数据集转换、黑箱不透明性、验证和监管方面的技术问题，以及人工智能教育缺乏、工作流程变化、自动化偏差和技能减损等人为因素。随着 AI 的引入，还将产生新的和意想不到的安全风险。这些问题的解决方案很复杂，需要时间去发现、开发、验证和实施。然而，有条不紊地解决这些问题将加速 AI 的安全和有益使用，以增强精神病学中的医疗决策。
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