Dataset Information

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review.

ABSTRACT:

Background

Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways.

Objective

This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work.

Methods

We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module.

Results

The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations.

Conclusions

Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning-based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

SUBMITTER: Safi Z

PROVIDER: S-EPMC7775817 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review.

Safi Zeineb Z Abd-Alrazaq Alaa A Khalifa Mohamed M Househ Mowafa M

Journal of medical Internet research 20201218 12

<h4>Background</h4>Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways.<h4>Ob ...[more]

PMID: 33337337

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Project description:BackgroundThe COVID-19 pandemic has had global impacts and caused some health systems to experience substantial pressure. The need for accurate health information has been felt widely. Chatbots have great potential to reach people with authoritative information, and a number of chatbots have been quickly developed to disseminate information about COVID-19. However, little is known about user experiences of and perspectives on these tools.ObjectiveThis study aimed to describe what is known about the user experience and user uptake of COVID-19 chatbots.MethodsA scoping review was carried out in June 2021 using keywords to cover the literature concerning chatbots, user engagement, and COVID-19. The search strategy included databases covering health, communication, marketing, and the COVID-19 pandemic specifically, including MEDLINE Ovid, Embase, CINAHL, ACM Digital Library, Emerald, and EBSCO. Studies that assessed the design, marketing, and user features of COVID-19 chatbots or those that explored user perspectives and experience were included. We excluded papers that were not related to COVID-19; did not include any reporting on user perspectives, experience, or the general use of chatbot features or marketing; or where a version was not available in English. The authors independently screened results for inclusion, using both backward and forward citation checking of the included papers. A thematic analysis was carried out with the included papers.ResultsA total of 517 papers were sourced from the literature, and 10 were included in the final review. Our scoping review identified a number of factors impacting adoption and engagement including content, trust, digital ability, and acceptability. The papers included discussions about chatbots developed for COVID-19 screening and general COVID-19 information, as well as studies investigating user perceptions and opinions on COVID-19 chatbots.ConclusionsThe COVID-19 pandemic presented a unique and specific challenge for digital health interventions. Design and implementation were required at a rapid speed as digital health service adoption accelerated globally. Chatbots for COVID-19 have been developed quickly as the pandemic has challenged health systems. There is a need for more comprehensive and routine reporting of factors impacting adoption and engagement. This paper has shown both the potential of chatbots to reach users in an emergency and the need to better understand how users engage and what they want.

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Project description:ObjectivesOver the last two decades, the number of scoping reviews in core medical education journals has increased by 4200%. Despite this growth, research on scoping reviews provides limited information about their nature, including how they are conducted or why medical educators undertake this knowledge synthesis type. This gap makes it difficult to know where the field stands and may hamper attempts to improve the conduct, reporting and utility of scoping reviews. Thus, this review characterises the nature of medical education scoping reviews to identify areas for improvement and highlight future research opportunities.MethodThe authors searched PubMed for scoping reviews published between 1/1999 and 4/2020 in 14 medical education journals. The authors extracted and summarised key bibliometric data, the rationales given for conducting a scoping review, the research questions and key reporting elements as described in the PRISMA-ScR. Rationales and research questions were mapped to Arksey and O'Malley's reasons for conducting a scoping review.ResultsOne hundred and one scoping reviews were included. On average, 10.1 scoping reviews (SD = 13.1, median = 4) were published annually with the most reviews published in 2019 (n = 42). Authors described multiple reasons for undertaking scoping reviews; the most prevalent being to summarise and disseminate research findings (n = 77). In 11 reviews, the rationales for the scoping review and the research questions aligned. No review addressed all elements of the PRISMA-ScR, with few authors publishing a protocol (n = 2) or including stakeholders (n = 20). Authors identified shortcomings of scoping reviews, including lack of critical appraisal.ConclusionsScoping reviews are increasingly conducted in medical education and published by most core journals. Scoping reviews aim to map the depth and breadth of emerging topics; as such, they have the potential to play a critical role in the practice, policy and research of medical education. However, these results suggest improvements are needed for this role to be fully realised.

Dataset Information

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review.

Background

Objective

Methods

Results

Conclusions

Publications

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review.

Similar Datasets

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