Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.
BACKGROUND Communication is at the center of every healthcare profession, rendering the training of communication skills in these fields most important. Technological advances, such as artificial intelligence (AI) and particularly machine learning (ML), may support this cause and might provide students with an always-available, low-threshold communication training possibility. OBJECTIVE This scoping review aims to summarize the current status of the utilization of AI or ML in the acquisition of communication skills within academic healthcare professions. METHODS We conducted a comprehensive literature search across the databases PubMed, Scopus, Cochrane Library, Web of Science Core Collection and Cumulative Index to Nursing and Allied Health (CINAHL) to identify articles that deal with the application of AI or ML to the training of communication for undergraduate students of healthcare professions. Additionally, articles that incorporate potential AI- or ML-based ways of improvements for communication training for caretaker-patient-encounters were included. Using an inductive approach, the included studies were organized into three distinct categorical themes. Study characteristics, ways of AI or ML applications and main outcomes of included studies were evaluated and compared. Furthermore, hindering factors of the application of AI/ML in healthcare communication training were depicted. RESULTS 339 titles and abstracts were identified, of which we examined 23 papers for full-text review. 13 studies met the inclusion criteria. The three distinct themes that were agreed upon comprised the specific utilization of AI/ML, the combination of AI/ML and virtual reality and the combination of AI/ML and virtual patients – each within the frame of academic healthcare communication training. The implementation of AI/ML could roughly be distinguished between two themes: the usage of virtual patients and the analysis of communication and feedback-provision. Reported barriers in the application of AI/ML in communication training revolved around the lack of authenticity and restricted natural flow of language of the utilized AI/ML-based virtual patient systems. Furthermore, the usage of educational AI/ML-based systems in healthcare communication training is currently limited to only a few cases, topics and clinical domains. CONCLUSIONS The utilization of AI and ML in health professional communication trainings clearly is a growing and promising field with a potential to render trainings more cost-effective, less time-consuming and to serve learners as an individualized, always-available practice method. In most cases, however, the depicted applications and technical solutions are limited in matters of access or targeted scenarios as well as regarding natural conversation flows and authenticity. These issues still stand in the way of any widespread implementation ambitions.
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