Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study

Park, SoHyun; Choi, Jeewon; Lee, Sung‐Woo; Oh, Changhoon; Kim, Changdai; Soohyun, La; Lee, Joonhwan; Suh, Bongwon

doi:10.2196/12231

Cited by 110 publications

(63 citation statements)

References 53 publications

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“…Many text understanding approaches can fall under pattern matching. The different pattern matching methods reported in the included studies are as follows: keyword matching or string matching, where specific keywords or strings of the user input are matched to scripts stored in the database, which was used in 11 studies [ 10 - 15 , 32 , 33 , 46 , 48 , 50 ], or Extensible Markup Language (XML) variants, such as Artificial Intelligence Markup Language (AIML), which were used in 7 studies [ 16 , 17 , 34 - 37 , 47 ]. The second most popular approach was the use of machine learning methods for text understanding, which was used in 6 studies.…”

Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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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.

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Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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“…Estudos completos incluídos para a síntese (n:25) Green et al, 2019;Hess, Fricker and Denecke, 2019;Joerin, Rauws and Ackerman, 2019;Kramer et al, 2019;Stephens et al, 2019;Fadhil, Schiavo and Wang, 2019;Goldenthal et al, 2019), e em países emergentes (Abashev et al, 2017;Chung and Park, 2018;Hernandez, 2019;Park et al, 2019a), tendo destaque (em quantidade)…”

Section: Intençõesunclassified

“…para Austrália (5 publicações Os estudos não passaram por triagem de tempo de publicação, e mesmo assim, um único artigo do ano de 2010 atendeu as correspondências da análise, (Crutzen et al, 2010), uma publicação de 2015, (Ireland et al, 2015), duas de 2016, (Ireland et al, 2016;Abashev et al, 2017), duas de 2017, (D'Alfonso et al, 2017;Ly, Ly and Andersson, 2017), oito publicações em 2018, (Chung and Park, 2018;Denecke et al, 2018;Fadhil, 2018;Fang, Bjering and Ginige, 2018;Fulmer et al, 2018;Ghosh, Bhatia and Bhatia, 2018;Ni and Liu, 2018;Srivastava et al, 2018), e 11 publicações até maio deste ano de 2019, (Denecke et al, 2018;Chaix et al, 2019;Park et al, 2019a;Stephens et al, 2019;Fadhil, Schiavo and Wang, 2019;Goldenthal et al, 2019;Green et al, (Fulmer et al, 2018;Park et al, 2019a;Stephens et al, 2019).…”

Section: Intençõesunclassified

“…Nos estudos analisados, os pesquisadores acreditam que apesar das limitações encontradas, os chatbots apresentam muita capacidade de assistir na área da saúde, de diversas formas. Em Goldenthal (2019), (Goldenthal et al, 2019) Seguindo a literatura, realizamos um levantamento das características desejáveis para o chatbot Goldenthal et al, 2019;Park et al, 2019b) Principais condutas que o chatbot deve ter de acordo com alguma queixa do paciente, ver figura 9:…”

Section: Doenças Crônicas Pacientes Com Doenças Crônicasunclassified

“…), Estados Unidos (5 publicações), Suíça(4 publicações) e Itália (3 publicações).As pesquisas analisadas abordaram como área de intervenção do chatbot em sua grande maioria a da saúde mental((D'Alfonso et al, 2017;Ly, Ly and Andersson, 2017;Fulmer et al, 2018;Green et al, 2019;Joerin, Rauws and Ackerman, 2019;Park et al, 2019b)), seguida pela promoção de saúde e bem-estar ((Fadhil, Schiavo and Wang, 2019;Kramer et al, 2019)) e medicina diagnóstica ((Denecke et al, 2018;Ghosh, Bhatia and Bhatia, 2018;Ni and Liu, 2018)). Um grande volume de pesquisas tivera enfoque a atenção primária à saúde, englobando cuidado diário com medicamentos, pacientes em recuperação cirúrgica ou no tratamento de alguma doença crônica ou de longa duração de tratamento e/ou que exige cuidados específicos((Abashev et al, 2017;Fadhil, 2018Fang, Bjering and Ginige, 2018;Chaix et al, 2019;Goldenthal et al, 2019;Hernandez, 2019;Hess, Fricker and Denecke, 2019;Stephens et al, 2019)).…”

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Assistente virtual mobile para o auxílio no tratamento da tuberculose

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A critical review of state‐of‐the‐art chatbot designs and applications

Luo

Lau

et al. 2021

WIREs Data Min & Knowl

129

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Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐of‐the‐art technologies and innovative applications of chatbots. In this review, we not only critically analyze the various computational approaches used to develop state‐of‐the‐art chatbots, but also thoroughly review the usability and applications of chatbots for various business sectors. We also identify gaps in chatbot‐related studies and propose new research directions to address the shortcomings of existing studies and applications. Our review advances both academic research and practical business applications of state‐of‐the‐art chatbots. We provide guidance for practitioners to fully realize the business value of chatbots and assist in making sensible decisions related to the development and deployment of chatbots in various business contexts. Researchers interested in the design and development of chatbots can also gain useful insights from our critical review and identify fruitful research topics and future research directions based on the research gaps discussed herein. This article is categorized under: Technologies > Machine Learning Application Areas > Business and Industry

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Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study

Cited by 110 publications

References 53 publications

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Assistente virtual mobile para o auxílio no tratamento da tuberculose

A critical review of state‐of‐the‐art chatbot designs and applications

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