Ali Faryal, Hamid Umair, Zaidat Osama, Bhatti Danish, Kalia Junaid Siddiq
Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan.
Department of Neurology, University of Illinois, College of Medicine, Peoria, IL, United States.
Front Neurol. 2020 Oct 7;11:559322. doi: 10.3389/fneur.2020.559322. eCollection 2020.
Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and alert programs can enhance efficiency of patient flow and improve outcomes. Automated data collection and analysis can make quality improvement and research projects quicker and much less burdensome. Despite current challenges, these synergistic technologies hold immense promise in enhancing the clinician experience, helping to reduce physician burnout while improving patient health outcomes at a lower cost. This brief overview discusses the multifaceted potential of AI use in telestroke.
远程神经病学使得人们能够获得神经学专业知识和先进的中风护理,而在以前这些是无法实现的。利用人工智能和机器学习来辅助远程中风护理可能具有革命性意义。这包括通过影像分析实现更快速、更可靠的诊断,以及对住院病程和3个月预后的预测。智能电子病历可以搜索自由文本,并通过分析患者病历提供决策辅助。语音识别已经发展到足够可靠且非常便捷的程度。智能情境感知通信和警报程序可以提高患者流程的效率并改善治疗结果。自动数据收集和分析可以使质量改进和研究项目更快且负担大大减轻。尽管目前存在挑战,但这些协同技术在提升临床医生体验、帮助减少医生职业倦怠的同时,以更低成本改善患者健康结果方面具有巨大潜力。本简要概述讨论了人工智能在远程中风领域应用的多方面潜力。