Carriere Jay, Shafi Hareem, Brehon Katelyn, Pohar Manhas Kiran, Churchill Katie, Ho Chester, Tavakoli Mahdi
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
School of Public Health, University of Alberta, Edmonton, AB, Canada.
Front Artif Intell. 2021 Feb 12;4:613637. doi: 10.3389/frai.2021.613637. eCollection 2021.
The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.
新冠疫情对全球医疗系统和医疗服务产生了深远影响。政策制定者正在利用社交距离和隔离政策来降低新冠病毒传播和扩散的风险，同时抗病毒治疗和疫苗的研发及测试也在持续进行。作为这些隔离政策的一部分，面对面的医疗服务已减少或取消，以避免高危和脆弱人群，特别是患有合并症的人群感染新冠病毒的风险。传统上，临床医生、职业治疗师和物理治疗师依靠面对面诊断和治疗急性和慢性肌肉骨骼（MSK）及神经系统疾病。因此，在疫情期间，急性和慢性疾病患者的评估和康复受到了特别大的影响。本文探讨了如何利用人工智能和机器学习（AI/ML）技术，如自然语言处理（NLP），来辅助急性和慢性疾病的评估和康复。
