Conversational agents for depression screening: A systematic review

Otero-González, Iván; Pacheco-Lorenzo, Moisés R.; Fernández-Iglesias, Manuel J.; Anido-Rifón, Luis E.

doi:10.1016/j.ijmedinf.2023.105272

Cited by 8 publications

(9 citation statements)

References 63 publications

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“…The agent provides proactive functions, sending messages to help elderly users educate and relax in general health, taking medication, diet, family relationships, exercise, rest, and sleep. Reminiscence strategies are considered challenging when related to design interfaces, conversation logic, and meaningful metrics directed at elderly adults, which benefit from screening and educating them (Kusal et al, 2022;Moreno, Sánchez-Anguix, Alberola, Julián, & Botti, 2024;Otero-González, Pacheco-Lorenzo, Fernández-Iglesias, & Anido-Rifón, 2024). Having enhanced the technological experience of the elderly, they can interact directly with Ai-aun confidently and successfully receive responses related to promoting their health conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial Intelligence (AI) has been incorporated into many sciences and arts fields, including healthcare and support systems ( Kusal et al , 2022 ). Furthermore, AI technologies have been used in hospitals as well as health and well-being sectors for mental health screening ( Otero-González, Pacheco-Lorenzo, Fernández-Iglesias, & Anido-Rifón, 2024 ). The implementation of AI in healthcare can provide cost-effective solutions for improving the health of people in modern society ( Väänänen, Haataja, Vehviläinen-Julkunen and Toivanen, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Validation of a Thai artificial chatmate designed for cheering up the elderly during the COVID-19 pandemic

Deepaisarn,

Imkome,

Wongpatikaseree

et al. 2024

F1000Res

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Background The COVID-19 pandemic severely affected populations of all age groups. The elderly are a high-risk group and are highly vulnerable to COVID-19. Assistive software chatbots can enhance the mental health status of the elderly by providing support and companionship. The objective of this study was to validate a Thai artificial chatmate for the elderly during the COVID-19 pandemic and floods. Methods Chatbot design includes the establishment of a dataset and emotional word vectors in which data consisting of emotional sentences were converted into the word vector form using a pre-trained word2vec model. A word vector was then input into a convolutional neural network (CNN) and trained until the model converges using sentence embedding and similarity word segmentation. Sentence vectors were generated by averaging each word vector using an averaged vector method. For approximate similarity matching, the Annoy library was used to create the indices in tree sorting. Data were collected from 22 elderly and assessed by the Post-Study System Usability Questionnaire (PSSUQ). Results The study revealed that 72.73% of the respondents found the chatbot easy to learn and use, 63.64% of the respondents found the chatbot can autonomously determine the next course of action, and 59.09% of the respondents believed that troubleshooting guidelines were provided for overcoming errors. The accuracy of the chatbot providing a reasonable response is 56.20±13.99%. Conclusions Most users were satisfied with the chatbot system. The proposed chatbot provided considerable essential insights into the development of assistance systems for the elderly during the coronavirus pandemic (COVID-19) and during the period of national disasters. The model can be expanded to other applications in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a Thai artificial chatmate designed for cheering up the elderly during the COVID-19 pandemic

Deepaisarn,

Imkome,

Wongpatikaseree

et al. 2024

F1000Res

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“…This might signal an evolution in more recent studies. A related evolution might be the use of tools specialized in building chatbots rather than designing them from the ground up, as several recent reviews (e.g., Otero-González et al, 2024;Pacheco-Lorenzo et al, 2021) mention the use of such technology. Textual interaction appeared to be the most frequent mode, with less chatbots relying primarily on voice.…”

Section: Synthesismentioning

confidence: 99%

“…Jabir et al (2023) noted that the frequent use of pen and paper to collect data on outcomes might be burdensome to participants and suggested the use of the chatbots themselves or smartphone sensing to alleviate this burden. Two reviews focused specifically on the use of chatbots for the detection of symptomatology (Otero-González et al, 2024;depression) and Pachenco-Lorenzo et al (2021;mental and neurological disorders), highlighting the possibilities for such assessements, but Vaidyam et al (2020) report less optimistic results from one study.…”

Section: Synthesismentioning

confidence: 99%

“…For chatbots to be most useful, the systems should include a process allowing the assessment of the mental health of users and of their progress, yet in most studies pen and paper measures seems to be the data collection method for such outcomes (Jabir et al, 2023). The reviews by Otero-González et al (2024) and Pachenco-Lorenzo et al, (2021) seem to indicate that automated assessments through user interactions with chatbots are technically possible. This constitutes, in our opinion, a necessary evolution of the field.…”

Section: What Are Users' Perceptions About Chatbots For Mental Health?mentioning

confidence: 99%

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Chatbots and mental health: A scoping review of reviews

Mayor

2024

Preprint

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The majority of individuals presenting with mental disorders do not receive mental health services. The use of chatbots for mental health is now frequently discussed as a means to increase access to mental health resources. For this scoping review of reviews on chatbots for mental health, we performed a systematic search of the literature on Scopus, Web of Science, Pubmed and Dimensions.ai and identified 14 relevant reviews published in scientific journals which identified publications following a systematic search on two or more databases. Three additional relevant reviews were included following forward and backward reference examination. The 17 included reviews are: eight systematic reviews (three with meta-analysis), seven scoping reviews and two unlabeled reviews. We have summarized the scope of the reviews as well as their findings. Most reviews included at least one study with 15 participants or less. Few reviews report an important proportion of RCTs and some original publications most of the time report on piloting studies. Overall, the reviews examined the opinions of users of chatbots (generally good user’s perceptions although some find deterrents), the features of chatbots, the outcomes and measures in studies relying on chatbots, the effectiveness of chatbots (particularly the meta-analyses) for the alleviation of mental disorder symptomatology (three meta-analyses find effectiveness for depression, whereas other clinical targets might need further research) as well as their potential for the assessment of mental health. The use of machine learning and natural language processing frameworks (Dialogflow, RASA) seems to drive an increasing number of studies of chatbots for mental health. Overall, we conclude that there is potential for the use of chatbots for mental health, but that progress is necessary in following areas affecting interaction dynamics: reduction of the frequent lack of empathy and repetitiveness, inclusion of user assessment within the chatbot system, inclusion of a memory of past interactions. Large language models might be capable of reducing part of these issues and playing an increasingly important role in the field. Whether data privacy can be guaranteed to users is also an important question, which seems to be overlooked in the field.

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