PDF(Pubmed)
Abstract：
Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training.
This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions.
We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined.
The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains.
The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.
This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions.
We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined.
The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains.
The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.
摘要：
背景：沟通是每个医疗保健行业的关键要素，在所有医疗保健行业中进行沟通技能培训非常重要。人工智能（AI），特别是机器学习（ML）等技术进步可能会支持这一原因：它可能为学生提供易于访问和随时可用的交流培训的机会。
目的：本范围审查旨在总结在学术医疗保健专业中使用AI或ML获取沟通技能的现状。
方法：我们在PubMed进行了全面的文献检索，Scopus,科克伦图书馆,WebofScience核心合集,和CINAHL数据库，以确定涵盖在追求医疗保健专业教育的本科生的沟通技能培训中使用AI或ML的文章。使用归纳法，纳入的研究分为不同的类别.研究的具体特点，AI或ML应用程序使用的方法和技术，并对研究的主要结局进行了评估。此外，概述了使用AI和ML进行医疗保健专业人员沟通技能培训的支持和阻碍因素。
结果：确定了385项研究的标题和摘要，其中29人(7.5%)接受了全文审查。在29项研究中，根据纳入和排除标准，包括12（3.1％）。这些研究分为3个不同的类别：使用AI和ML进行文本分析和信息提取的研究，使用AI、ML和虚拟现实的研究，以及使用AI和ML以及虚拟患者模拟的研究，每个医疗专业人员的沟通技巧的学术培训。在这些主题领域中，人工智能也用于提供反馈。参与人员的动机在实施过程中起着重要作用。据报道，在沟通技能培训中使用AI和ML的障碍围绕着基于AI和ML的虚拟患者系统缺乏真实性和有限的语言自然流动。此外，目前，在医疗保健专业人员的沟通技能培训中使用基于教育AI和ML的系统仅限于少数情况，主题,和临床领域。
结论：在医疗保健专业人员的沟通技能培训中使用AI和ML显然是一个不断发展和有前途的领域，有可能使培训更具成本效益和更少的时间。此外，它可以作为一种个性化和容易获得的锻炼方法。然而,在大多数情况下,概述的应用程序和技术解决方案在访问方面受到限制，可能的场景，谈话的自然流动，和真实性。这些问题仍然阻碍任何广泛的实施雄心。
目的：本范围审查旨在总结在学术医疗保健专业中使用AI或ML获取沟通技能的现状。
方法：我们在PubMed进行了全面的文献检索，Scopus,科克伦图书馆,WebofScience核心合集,和CINAHL数据库，以确定涵盖在追求医疗保健专业教育的本科生的沟通技能培训中使用AI或ML的文章。使用归纳法，纳入的研究分为不同的类别.研究的具体特点，AI或ML应用程序使用的方法和技术，并对研究的主要结局进行了评估。此外，概述了使用AI和ML进行医疗保健专业人员沟通技能培训的支持和阻碍因素。
结果：确定了385项研究的标题和摘要，其中29人(7.5%)接受了全文审查。在29项研究中，根据纳入和排除标准，包括12（3.1％）。这些研究分为3个不同的类别：使用AI和ML进行文本分析和信息提取的研究，使用AI、ML和虚拟现实的研究，以及使用AI和ML以及虚拟患者模拟的研究，每个医疗专业人员的沟通技巧的学术培训。在这些主题领域中，人工智能也用于提供反馈。参与人员的动机在实施过程中起着重要作用。据报道，在沟通技能培训中使用AI和ML的障碍围绕着基于AI和ML的虚拟患者系统缺乏真实性和有限的语言自然流动。此外，目前，在医疗保健专业人员的沟通技能培训中使用基于教育AI和ML的系统仅限于少数情况，主题,和临床领域。
结论：在医疗保健专业人员的沟通技能培训中使用AI和ML显然是一个不断发展和有前途的领域，有可能使培训更具成本效益和更少的时间。此外，它可以作为一种个性化和容易获得的锻炼方法。然而,在大多数情况下,概述的应用程序和技术解决方案在访问方面受到限制，可能的场景，谈话的自然流动，和真实性。这些问题仍然阻碍任何广泛的实施雄心。
