Zeng-Treitler Qing, Nelson Stuart J
George Washington University, Washington, DC, DC, United States.
J Med Internet Res. 2019 Nov 27;21(11):e16272. doi: 10.2196/16272.
Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined "specialized" tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).
人工智能（AI），即计算机执行任务的能力，直到最近人们还认为这是人类智能的专属领域，但在过去十年中已取得了巨大进展。在游戏、自动驾驶汽车以及语音回应方面的能力都取得了显著成功。医学面临的挑战和机遇与这些挑战有何不同？我们如何才能最好地将这些数据驱动技术应用于患者护理和治疗结果？1980年发表在《新英格兰医学杂志》上的一篇论文指出，医疗保健中定义更明确的“专门”任务更适合计算机辅助，而定义问题和缩小问题空间所需的广泛方法则不然，甚至可能无法实现。另一方面，可以认为，使用数据驱动方法的现代版人工智能在处理临床医生和患者常常感到困难的结果预测等任务时将最有用。如今，收集关于单个个体的大量数据（例如通过可穿戴设备）以及积累关于接受医疗护理的多个人的大型数据集，有可能应用于个体护理。随着这些分析、枚举、汇总和呈现技术应用于医学领域，其在该领域的效用和适用性问题也随之而来。早期的决策支持努力被证明是有帮助的；随着系统的激增，后来的经验表明诸如警报疲劳和医生倦怠等问题变得更加普遍。数据驱动的预测会出现类似的情况吗？通过为患者提供从数据分析中获得的信息来增强他们的能力会有帮助吗？患者、医疗服务提供者、技术和政策制定者在医学人工智能的开发和利用中都发挥着作用。这里隐含的一些挑战、机遇和权衡以临床医生（SJN）和信息专家（QZT）之间的对话形式呈现。
