Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.

中文翻译：

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现代医学物理学中基于人工智能的临床决策支持：选择，验收，调试和质量保证。

随着临床数据可用性的提高，机器和深度学习的最新进展激发了人们对计算机化临床决策支持系统（CDSS）的新兴趣。CDSS具有改善医疗保健，提高患者安全性和降低成本的巨大潜力。但是，CDSS的使用并非没有陷阱，因为CDSS不足或有缺陷可能会导致医疗质量下降，并使患者处于危险之中。另外，采用CDSS可能会失败，因为其预期的用户由于缺乏信任，相关性或可操作性而忽略了CDSS的输出。