CSAIL, MIT, 32 Vassar St, Cambridge, MA 02139, USA.
Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada.
Clin Lab Med. 2023 Mar;43(1):29-46. doi: 10.1016/j.cll.2022.09.004. Epub 2022 Dec 13.
Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.
临床人工智能（AI）/机器学习（ML）有望在临床决策支持、诊断推理、精准医学、临床运营支持和临床研究方面提供新的能力，但需要谨慎关注，以确保这些技术在临床中有效运作。在这里，我们详细介绍了临床 ML/AI 的设计过程，确定了几个关键问题，并详细说明了 ML 工具中出现的几种常见问题形式，这些问题是由真实世界的例子引发的，以便临床医生和研究人员能够更好地预测并在自己使用 ML/AI 技术时纠正这些问题。
