可解释性
医学
启发式
临床决策支持系统
决策分析
证据推理法
标准化
人工智能
风险分析(工程)
计算机科学
决策支持系统
商业决策图
数学
统计
操作系统
作者
Tyler J. Loftus,Patrick Tighe,Amanda C. Filiberto,Philip A. Efron,Scott C. Brakenridge,Alicia M. Mohr,Parisa Rashidi,Gilbert R. Upchurch,Azra Bïhorac
出处
期刊:JAMA Surgery
[American Medical Association]
日期:2020-02-01
卷期号:155 (2): 148-148
被引量:209
标识
DOI:10.1001/jamasurg.2019.4917
摘要
Importance
Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making. Observations
Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process. Conclusions and Relevance
Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
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