Institute of Family Medicine, University Hospital Schleswig-Holstein Luebeck Campus, Luebeck, Germany.
J Med Internet Res. 2023 Jun 19;25:e43311. doi: 10.2196/43311.
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.
沟通是每一个医疗保健职业的关键要素，因此在所有医疗保健职业中进行沟通技巧培训显得尤为重要。人工智能（AI），尤其是机器学习（ML）等技术进步可能会为此提供支持：它可能为学生提供一个方便、随时可用的沟通培训机会。
本范围综述旨在总结 AI 或 ML 在获取学术医疗保健专业学生沟通技能方面的应用现状。
我们在 PubMed、Scopus、Cochrane 图书馆、Web of Science 核心合集和 CINAHL 数据库中进行了全面的文献检索，以确定涵盖本科医学生使用 AI 或 ML 进行沟通技能培训的文章。使用归纳方法，将纳入的研究分为不同类别。评估了研究的具体特征、AI 或 ML 应用程序使用的方法和技术以及研究的主要结果。此外，还概述了在医疗保健专业人员沟通技能培训中使用 AI 和 ML 的支持和阻碍因素。
确定了 385 项研究的标题和摘要，其中 29 项（7.5%）进行了全文审查。根据纳入和排除标准，29 项研究中有 12 项（3.1%）被纳入。这些研究分为 3 个不同类别：使用 AI 和 ML 进行文本分析和信息提取的研究、使用 AI 和 ML 和虚拟现实的研究、以及使用 AI 和 ML 模拟虚拟患者的研究，每个研究都属于医疗保健专业人员沟通技能的学术培训。在这些主题领域内，AI 还用于提供反馈。参与代理的动机在实施过程中起着重要作用。阻碍 AI 和 ML 在沟通技能培训中应用的因素主要涉及 AI 和基于 ML 的虚拟患者系统缺乏真实性和语言表达自然流畅度。此外，医疗保健专业人员沟通技能培训中使用教育性 AI 和基于 ML 的系统目前仅局限于少数案例、主题和临床领域。
AI 和 ML 在医疗保健专业人员沟通技能培训中的应用显然是一个不断发展和充满前景的领域，具有提高培训成本效益和减少培训时间的潜力。此外，它可以为学习者提供一种个性化和随时可用的练习方法。然而，在大多数情况下，所概述的应用程序和技术解决方案在可访问性、可能的场景、对话的自然流畅度和真实性方面都受到限制。这些问题仍然是任何广泛实施目标的障碍。
