Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Abstract

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.

A Generative Pretrained Transformer (GPT)–Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study

“…Owing to the costs and potential disturbances associated with the use of real or simulated patients in communication training [ 52 , 53 ], there has been great interest in the use of virtual simulated patients as chatbots for communication training [ 21 , 31 ]. In the past years, studies were published using chatbots to cover a wide range of conditions and domains [ 52 , 53 ].…”

“…However, in contrast to our study, most of these studies were performed before the broad accessibility of LLMs, such as GPT. These chatbots have thus been restricted in their authentic skills, capability of adoption (ie, in terms of personality, cases, etc), and ability to be transferred to different health care domains [ 31 ]. Although we also focused on 1 patient case, the ability of LLMs makes them theoretically capable of adapting to a given situation.…”

“…Chatbots have been used in medical education before the broader application of LLMs [ 31 ]. However, these virtual simulated patients did not reach human performance in terms of language expression and dynamics [ 31 ].…”

“…Chatbots have been used in medical education before the broader application of LLMs [ 31 ]. However, these virtual simulated patients did not reach human performance in terms of language expression and dynamics [ 31 ]. Although chatbots to practice history taking have been developed based on pre-LLM technology [ 32 ], it is unknown whether and how LLMs, such as GPT, can be used as a simulated patient to acquire communication skills.…”

Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study

“…VSPs have also been used in the health professions to practice critical communication skills [5][6][7]. VSPs that use natural language processing may provide a valuable educational experience for learners [8].…”