医疗保健领域的人工智能

Julia M. Puaschunder, Dieter Feierabend
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引用次数: 15

摘要

计算机正在支持人类输入、决策和提供数据,其程度是医学史上前所未有的。在当今的医疗保健行业和医疗行业,人工智能、算法、机器人和大数据被用于推断,以监测大规模的医疗趋势,基于数据驱动的估计来检测和衡量个人的风险和机会。像医疗保健行业这样的知识密集型行业高度依赖于数据和分析来改进治疗和实践。近年来,收集的医疗信息范围有了巨大的增长,包括临床、遗传、行为和环境数据。每天,医疗保健专业人员、生物医学研究人员和患者都会从一系列设备中产生大量数据。这些包括电子健康记录(EHRs)、基因组测序机、高分辨率医学成像、智能手机应用程序和无处不在的传感,以及监测患者健康的物联网(IoT)设备(OECD 2015)。通过机器学习算法和前所未有的数据存储和计算能力,人工智能技术具有最先进的获取信息、处理信息并向最终用户提供明确定义的输出的能力。因此,日常监测有助于创建大数据,以识别行为模式与健康状况的关系,以便基于捕获大规模样本的大数据创建具有最高数学精度的预测。因此,人工智能有助于分析在诊断、治疗、药物开发和监测、个性化医疗、患者控制和护理等各个阶段的预防和治疗与患者预后之间的关系。先进的医院正在研究人工智能解决方案,以支持和执行提高精度和成本效益的运营计划。机器人已被用于残疾人和病人护理援助。通过预测分析和一般医疗保健管理技术支持医疗决策。网络连接允许以经济有效的方式在全球范围内获得负担得起的医疗保健。
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Artificial Intelligence in the Healthcare Sector
To an extent as never before in the history of medicine, computers are supporting human input, decision making and provision of data. In today’s healthcare sector and medical profession, AI, algorithms, robotics and big data are used to derive inferences for monitoring large-scale medical trends, detecting and measuring individual risks and chances based on data-driven estimations. A knowledge-intensive industry like the healthcare profession highly depends on data and analytics to improve therapies and practices. In recent years, there has been tremendous growth in the range of medical information collected, including clinical, genetic, behavioral and environmental data. Every day, healthcare professionals, biomedical researchers and patients produce vast amounts of data from an array of devices. These include electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, smartphone applications and ubiquitous sensing, as well as Internet of Things (IoT) devices that monitor patient health (OECD 2015). Through machine learning algorithms and unprecedented data storage and computational power, AI technologies have most advanced abilities to gain information, process it and give a well-defined output to the end-user. Daily monitoring thereby aids to create big data to recognize behavioral patterns’ relation to health status in order to create predictions with highest mathematical precision based on big data capturing large-scale samples. AI thereby enlightens to analyze the relation between prevention and treatment and patient outcomes in all stages of diagnosis, treatment, drug development and monitoring, personalized medicine, patient control and care. Advanced hospitals are looking into AI solutions to support and perform operational initiatives that increase precision and cost effectiveness. Robotics have been used for disabled and patient care assistance. Medical decision making has been supported through predictive analytics and general healthcare management technology. Network connectivity allows access to affordable healthcare around the globe in a cost-effective way.
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