An Overview of the Use of Chatbots in Medical and Healthcare Education

Frangoudes, Fotos; Hadjiaros, Marios; Schiza, Eirini C.; Matsangidou, Maria; Tsivitanidou, Olia; Kleanthous, Kleanthis

doi:10.1007/978-3-030-77943-6_11

Cited by 26 publications

(8 citation statements)

References 30 publications

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“…Literature reviews on chatbots in health care are available in specific contexts, such as mental health management [ 19 - 21 ], prevention of COVID-19 [ 22 ], health behavior change [ 23 , 24 ], chronic conditions management [ 25 ], and medical education [ 26 ]. A systematic review by Laranjo et al [ 12 ] was completed in 2018 with a global scope, but it was aimed at patients and providers and focused only on verbal communication.…”

Section: Discussionmentioning

confidence: 99%

Interactive Conversational Agents for Health Promotion, Prevention, and Care: Protocol for a Mixed Methods Systematic Scoping Review

Sasseville¹,

S.²,

Yameogo³

et al. 2022

JMIR Res Protoc

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Background Interactive conversational agents, also known as “chatbots,” are computer programs that use natural language processing to engage in conversations with humans to provide or collect information. Although the literature on the development and use of chatbots for health interventions is growing, important knowledge gaps remain, such as identifying design aspects relevant to health care and functions to offer transparency in decision-making automation. Objective This paper presents the protocol for a scoping review that aims to identify and categorize the interactive conversational agents currently used in health care. Methods A mixed methods systematic scoping review will be conducted according to the Arksey and O’Malley framework and the guidance of Peters et al for systematic scoping reviews. A specific search strategy will be formulated for 5 of the most relevant databases to identify studies published in the last 20 years. Two reviewers will independently apply the inclusion criteria using the full texts and extract data. We will use structured narrative summaries of main themes to present a portrait of the current scope of available interactive conversational agents targeting health promotion, prevention, and care. We will also summarize the differences and similarities between these conversational agents. Results The search strategy and screening steps were completed in March 2022. Data extraction and analysis started in May 2022, and the results are expected to be published in October 2022. Conclusions This fundamental knowledge will be useful for the development of interactive conversational agents adapted to specific groups in vulnerable situations in health care and community settings. International Registered Report Identifier (IRRID) DERR1-10.2196/40265

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Section: Discussionmentioning

confidence: 99%

Interactive Conversational Agents for Health Promotion, Prevention, and Care: Protocol for a Mixed Methods Systematic Scoping Review

Sasseville¹,

S.²,

Yameogo³

et al. 2022

JMIR Res Protoc

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“…The use of chatbots in medical education offers equally promising opportunities. In particular, chatbots are of interest tool-wise in the area of virtual patients [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

Holderried,

Stegemann–Philipps,

Herschbach

et al. 2024

JMIR Med Educ

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Background Communication is a core competency of medical professionals and of utmost importance for patient safety. Although medical curricula emphasize communication training, traditional formats, such as real or simulated patient interactions, can present psychological stress and are limited in repetition. The recent emergence of large language models (LLMs), such as generative pretrained transformer (GPT), offers an opportunity to overcome these restrictions Objective The aim of this study was to explore the feasibility of a GPT-driven chatbot to practice history taking, one of the core competencies of communication. Methods We developed an interactive chatbot interface using GPT-3.5 and a specific prompt including a chatbot-optimized illness script and a behavioral component. Following a mixed methods approach, we invited medical students to voluntarily practice history taking. To determine whether GPT provides suitable answers as a simulated patient, the conversations were recorded and analyzed using quantitative and qualitative approaches. We analyzed the extent to which the questions and answers aligned with the provided script, as well as the medical plausibility of the answers. Finally, the students filled out the Chatbot Usability Questionnaire (CUQ). Results A total of 28 students practiced with our chatbot (mean age 23.4, SD 2.9 years). We recorded a total of 826 question-answer pairs (QAPs), with a median of 27.5 QAPs per conversation and 94.7% (n=782) pertaining to history taking. When questions were explicitly covered by the script (n=502, 60.3%), the GPT-provided answers were mostly based on explicit script information (n=471, 94.4%). For questions not covered by the script (n=195, 23.4%), the GPT answers used 56.4% (n=110) fictitious information. Regarding plausibility, 842 (97.9%) of 860 QAPs were rated as plausible. Of the 14 (2.1%) implausible answers, GPT provided answers rated as socially desirable, leaving role identity, ignoring script information, illogical reasoning, and calculation error. Despite these results, the CUQ revealed an overall positive user experience (77/100 points). Conclusions Our data showed that LLMs, such as GPT, can provide a simulated patient experience and yield a good user experience and a majority of plausible answers. Our analysis revealed that GPT-provided answers use either explicit script information or are based on available information, which can be understood as abductive reasoning. Although rare, the GPT-based chatbot provides implausible information in some instances, with the major tendency being socially desirable instead of medically plausible information.

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“…This approach has increased student engagement and motivation (Almodaires et al, 2019). Similarly, chatbots provide students with personalized support and facilitate collaboration, feedback, and assessment (Chen et al, 2021;Frangoudes et al, 2021;Kumar & Silva, 2020). These programs simulate human conversation and can provide students with a more engaging and interactive learning experience (Suhel et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration

Baskara

2023

IJORER Int. J. Recent Educ. Res.

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Objective: This paper explored the relationship between chatbots and flipped learning in an educational setting. It also identifies the benefits and drawbacks of using chatbots in a flipped learning context and the ethical and privacy concerns related to their use. Method: The study utilized a theoretical analysis approach, which included a comprehensive review of relevant literature from Scopus and World of Science databases. The data collected from the literature review was analyzed using a qualitative approach. Results: The study found that chatbots can potentially enhance student engagement and learning outcomes in a flipped learning context by providing personalized support, facilitating group discussions and collaborations, providing feedback and assessment on student work, supporting self-directed learning, and enhancing student engagement and motivation. However, using chatbots in a flipped learning context also raises ethical and privacy concerns, including data privacy, data security, and student anonymity. Novelty: Contributes to the existing research on using chatbots in education by providing insights into the potential benefits and drawbacks of using chatbots in a flipped learning context. The study highlights the importance of considering the ethical and privacy concerns and the future potential of chatbots in a flipped learning context and proposes future research directions.

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An Overview of the Use of Chatbots in Medical and Healthcare Education

Cited by 26 publications

References 30 publications

Interactive Conversational Agents for Health Promotion, Prevention, and Care: Protocol for a Mixed Methods Systematic Scoping Review

Interactive Conversational Agents for Health Promotion, Prevention, and Care: Protocol for a Mixed Methods Systematic Scoping Review

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

Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration

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