Artificial intelligence and computer simulation models in critical illness.
Artificial intelligence and computer simulation models in critical illness.
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DOI：
10.5492/wjccm.v9.i2.13
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发表时间：
2020-06-05
期刊：
影响因子：
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通讯作者：
Pickering, Brian
中科院分区：
文献类型：
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作者：
Lal, Amos;Pinevich, Yuliya;Pickering, Brian
电子健康记录的广泛实施导致人工智能（AI）和计算机建模在临床医学中的使用增加。危重疾病的早期识别和治疗对于取得良好的结果至关重要，但由于环境的复杂性和临床表现的非特异性等因素而变得困难。越来越多的人工智能应用程序被提议为忙碌或分心的临床医生提供决策支持，以应对这一挑战。数据驱动的“关联”人工智能模型是根据缺失数据和不精确时间的回顾性数据注册表构建的。联想人工智能模型缺乏透明度，经常忽略因果机制，虽然可能有助于改善预后，但迄今为止的临床适用性有限。为了在临床上发挥作用，人工智能工具需要为临床医生提供可操作的知识。明确解决因果机制不仅可以提高模型的有效性和可复制性，还可以增加透明度，并有助于获得床边临床医生对人工智能模型在教学和患者护理中的实际使用的信任。
Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.