Harvard Medical Toxicology Program, Department of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA.
Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.
J Med Toxicol. 2020 Oct;16(4):458-464. doi: 10.1007/s13181-020-00769-5. Epub 2020 Mar 25.
Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias from wearables. A key promise of AI is its potential to apply logical reasoning at the scale of data too vast for the human mind to comprehend. This scaling up of logical reasoning may allow clinicians to bring the entire breadth of current medical knowledge to bear on each patient in real time. It may also unearth otherwise unreachable knowledge in the attempt to integrate knowledge and research across disciplines. In this review, we discuss two complementary aspects of artificial intelligence: deep learning and knowledge representation. Deep learning recognizes and predicts patterns. Knowledge representation structures and interprets those patterns or predictions. We frame this review around how deep learning and knowledge representation might expand the reach of Poison Control Centers and enhance syndromic surveillance from social media.
人工智能（AI）是指能够处理信息并像有感知的生物一样与世界互动的机器或软件。医学领域中的 AI 应用包括自动读取 X 光片和从可穿戴设备中检测心律失常。人工智能的一个关键承诺是，它有可能在人类思维无法理解的大规模数据上应用逻辑推理。这种逻辑推理的扩展可以使临床医生实时将当前医学知识的全部广度应用于每个患者。它还可以挖掘到原本无法企及的知识，以尝试整合不同学科的知识和研究。在这篇综述中，我们讨论了人工智能的两个互补方面：深度学习和知识表示。深度学习识别和预测模式。知识表示结构并解释这些模式或预测。我们围绕深度学习和知识表示如何扩大中毒控制中心的覆盖范围以及如何从社交媒体增强综合征监测来组织这篇综述。
