Chatbots in the Field of Mental Health: Challenges and Opportunities

Viduani, Anna; Cosenza, Victor; Araújo, Ricardo M.; Kieling, Christian

doi:10.1007/978-3-031-10698-9_8

Cited by 7 publications

(5 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, along with fuzzy logic, Z-numbers facilitate the decision-making process when the information is imprecise and/or incomplete. Since health chatbots are still in the initial phases of development [12,39], there is a high level of uncertainty regarding the topic; therefore, this approach is excellent for addressing this issue.…”

Section: Addressing Uncertaintymentioning

confidence: 99%

“…The use of chatbots in healthcare has many practical applications, from seeking medical advice and booking medical appointments [9] to dealing with anxiety and depression [10] and even helping with cancer [11]. In addition, chatbots are said to be the solution to increasing mental healthcare access [12]. In particular, chatbots showed potential for fighting COVID-19 [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

Liou,

2024

Sustainability

View full text Add to dashboard Cite

Technology has revolutionized various industries; notably, chatbots impact healthcare through the efficient streamlining of medical procedures, cost reductions, and improved accessibility to healthcare services. Consequently, understanding how to promote the adoption of healthcare chatbots has become crucial for enhancing the healthcare industry and medical service quality. Despite numerous studies identifying factors influencing healthcare chatbot adoption, there is a notable lack of empirical verification regarding their interrelationships, leading to a significant knowledge gap. Therefore, this study aims to address this gap by developing a decision-making model to analyze the relationships among key factors regarding three dimensions: technology, user, and society. The model begins by filtering out insignificant factors based on experts’ opinions. Subsequently, it employs DEMATEL (Decision Making Trial and Evaluation Laboratory) to construct a causal relationships graph and the ISM (interpretive structural modeling) method to categorize these factors into a hierarchical order. To mitigate uncertainties stemming from the topic’s complexity, this study utilizes fuzzy sets and Z-number theory in the assessment process. The findings reveal a predominance of causal factors within the technological dimension. Notably, the quality of information provided by chatbots stands out as the most influential causal factor. The insights from this study suggest implications for both enterprises and governments to boost chatbot adoption in society.

show abstract

Section: Addressing Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

Liou,

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…There are promising clinical outcomes for AI chatbots providing relevant and continually accessible support [22,23] for depression in adults [24], anxiety in university students [25,26] and attention-deficit/hyperactivity symptoms for adults [27]. AI chatbots may help address the barriers to the help-seeking process for mental health issues by offering personalized, accessible, affordable and stigma-free assistance, promoting early intervention and generating valuable insights for research and policymaking [28][29][30][31]. AI chatbots may be particularly useful in monitoring, communication, memory assistance, screening and diagnosis, with the aim of understanding a patient's emotions and assisting in the analysis of large datasets.…”

Section: The Impact Of Ai Chatbots On Technology Integrationmentioning

confidence: 99%

“…The opportunities for AI chatbots should also be considered in terms of the challenges posed, such as a lack of human connection, a reliance on technology, the accuracy and reliability of information, ethical and privacy considerations as well as misdiagnosis and limited understanding [28][29][30][31].…”

Section: The Impact Of Ai Chatbots On Technology Integrationmentioning

confidence: 99%

AI Chatbots in Digital Mental Health

Balcombe

2023

Informatics

View full text Add to dashboard Cite

Artificial intelligence (AI) chatbots have gained prominence since 2022. Powered by big data, natural language processing (NLP) and machine learning (ML) algorithms, they offer the potential to expand capabilities, improve productivity and provide guidance and support in various domains. Human–Artificial Intelligence (HAI) is proposed to help with the integration of human values, empathy and ethical considerations into AI in order to address the limitations of AI chatbots and enhance their effectiveness. Mental health is a critical global concern, with a substantial impact on individuals, communities and economies. Digital mental health solutions, leveraging AI and ML, have emerged to address the challenges of access, stigma and cost in mental health care. Despite their potential, ethical and legal implications surrounding these technologies remain uncertain. This narrative literature review explores the potential of AI chatbots to revolutionize digital mental health while emphasizing the need for ethical, responsible and trustworthy AI algorithms. The review is guided by three key research questions: the impact of AI chatbots on technology integration, the balance between benefits and harms, and the mitigation of bias and prejudice in AI applications. Methodologically, the review involves extensive database and search engine searches, utilizing keywords related to AI chatbots and digital mental health. Peer-reviewed journal articles and media sources were purposively selected to address the research questions, resulting in a comprehensive analysis of the current state of knowledge on this evolving topic. In conclusion, AI chatbots hold promise in transforming digital mental health but must navigate complex ethical and practical challenges. The integration of HAI principles, responsible regulation and scoping reviews are crucial to maximizing their benefits while minimizing potential risks. Collaborative approaches and modern educational solutions may enhance responsible use and mitigate biases in AI applications, ensuring a more inclusive and effective digital mental health landscape.

show abstract

“…In this sense, we propose that using chatbots—digital systems that rely on a conversational interaction that mimics human conversation [ 5 ]—may be an alternative to using traditional assessment methods. Chatbots are capable of capturing real-time accounts of events (ie, at the moment the event is being experienced) [ 6 ] and thus may further our current understanding of time- and context-contingent associations among activities, moods, and experiences [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study

et al. 2023

Self Cite

View full text Add to dashboard Cite

Background Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. Objective This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents’ mood. Methods The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp’s default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. Results The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot’s final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). Conclusions The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development.

show abstract

Chatbots in the Field of Mental Health: Challenges and Opportunities

Cited by 7 publications

References 51 publications

Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

AI Chatbots in Digital Mental Health

Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study

Contact Info

Product

Resources

About