European Journal of Physiotherapy 期刊官网-刊鹿论文编译

Artificial intelligence and physiotherapy – editorial

Abstract:

Digitalization is nowadays a top topic in popular and scientific debate. In physiotherapy (PT), we are participating in digitalisation on many levels, for example, applications for supporting physically active lifestyle and different systems for distance rehabilitation. Still, there is a lot of potential for development for PTs in this area. The Artificial intelligence, AI is quite broadly implemented in health care, for example, systems to help making faster diagnosis and giving basic medical feedback. AI can be designed and trained for a specific task, like Siri in an iPhone or AI can be designed for more general purposes covering different cognitive abilities that humans have, and thus, finding its own solutions for an unfamiliar task [1]. Four types of AI have been defined [1]; Reactive machines, Limited memory, Theory of mind and Self-awareness. The Reactive machines (programmes) are those that have no memory, but they can make predictions, that is, what will a consequence be when making a choice in some task. The Limited memory systems have some memory, and thus use experiences for future decisions, for example a selfdriving vehicle. The Theory of mind AI-system understands the existence of other’s beliefs and intentions, which then can influence the AI system’s decisions. The Self-awareness AI-system have consciousness of itself implying of understanding how to use AI’s own information in inferring with others’ feelings. The last two types of AI, Theory of mind and Self-awareness have not yet been developed. We physiotherapists, should be participating in development of these two not so far existing AI systems to improve health care. Human behaviour consists of physical, cognitive and emotional parts. PTs can easily identify the problems in activities in the physical part of behaviour, overt behaviour, by observing and testing an individual. The cognitive and emotional parts of behaviour, covert behaviours, are however harder to understand and interpret. Also, in PT, we struggle with trying to support our clients in adherence to different exercise regimens that in many cases are meant to be lifelong activities, and we do not know enough of how individuals’ beliefs and emotions effect the concept of adherence. To bear these in mind, it could be valuable to be part of teams that are developing Theory of mind and Self-awareness AI-systems. We could increase our understanding of human behaviour and thus be for example better coaches for healthier lifestyle and for supporting behaviour change. We should also be participating in development of AI systems to meet the demands from future PT-students. Young people are more and more digitalised in their lives as are the future patients and clients. AI is going to influence our lives in the future in great extend. If we educators do not meet the expectations of these students’, they will not seek to our education programmes. The AI could change the students’ way of learning, when, how and with whom they learn. Also, when talking about preparing students to be future health care professions whose daily work consists of maybe fully digitalised hospitals and other care units, we need to match the content of the education programmes with this future image as well. Old school is out, and the new school is in! There are of course AI-related ethical questions. There is a human bias in training the AI programme, there are hackers in trying to interfere, but also the possible problems coming up by AI finding its own problem solutions. However, if we are not participating in development of new AI systems, we cannot influence the ethical discussions either.

Author Listing： Anne Söderlund

Volume： 21

Pages： 1 - 1

DOI： 10.1080/21679169.2019.1569850

Language： English

Journal： European Journal of Physiotherapy

European Journal of Physiotherapy

EUR J PHYSIOTHER

影响因子：1.5

是否综述期刊：否

是否OA：否

是否预警：不在预警名单内

发行时间：-

ISSN：2167-9169

发刊频率：-

收录数据库：ESCI/Scopus收录

出版国家/地区：-

出版社：Taylor & Francis

期刊介绍

年发文量	37
国人发稿量	1
国人发文占比	2.7%
自引率	6.7%
平均录取率	-
平均审稿周期	-
版面费	-
偏重研究方向	REHABILITATION-
期刊官网	https://www.tandfonline.com/toc/iejp20/current
投稿链接	-

质量指标占比

研究类文章占比	OA被引用占比	撤稿占比	出版后修正文章占比
82.86%	30.71%	-	-

预警情况查看说明

时间	预警情况
2024年02月发布的2024版	不在预警名单中
2023年01月发布的2023版	不在预警名单中
2021年12月发布的2021版	不在预警名单中
2020年12月发布的2020版	不在预警名单中

JCR分区 WOS分区等级：Q3区

版本	按学科	分区
WOS期刊SCI分区 WOS期刊SCI分区是指SCI官方（Web of Science）为每个学科内的期刊按照IF数值排序，将期刊按照四等分的方法划分的Q1-Q4等级，Q1代表质量最高，即常说的1区期刊。（2021-2022年最新版）
	REHABILITATION	Q3

关于2019年中科院分区升级版（试行）

分区表升级版（试行）旨在解决期刊学科体系划分与学科发展以及融合趋势的不相容问题。由于学科交叉在当代科研活动的趋势愈发显著，学科体系构建容易引发争议。为了打破学科体系给期刊评价带来的桎梏，“升级版方案”首先构建了论文层级的主题体系，然后分别计算每篇论文在所属主题的影响力，最后汇总各期刊每篇论文分值，得到“期刊超越指数”，作为分区依据。

分区表升级版（试行）的优势：一是论文层级的主题体系既能体现学科交叉特点，又可以精准揭示期刊载文的多学科性；二是采用“期刊超越指数”替代影响因子指标，解决了影响因子数学性质缺陷对评价结果的干扰。整体而言，分区表升级版（试行）突破了期刊评价中学科体系构建、评价指标选择等瓶颈问题，能够更为全面地揭示学术期刊的影响力，为科研评价“去四唯”提供解决思路。相关研究成果经过国际同行的认可，已经发表在科学计量学领域国际重要期刊。

《2019年中国科学院文献情报中心期刊分区表升级版（试行）》首次将社会科学引文数据库（SSCI）期刊纳入到分区评估中。升级版分区表（试行）设置了包括自然科学和社会科学在内的18个大类学科。基础版和升级版（试行）将过渡共存三年时间，推测在此期间各大高校和科研院所仍可能会以基础版为考核参考标准。提示：中科院分区官方微信公众号“fenqubiao”仅提供基础版数据查询，暂无升级版数据，请注意区分。

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