Artificial Intelligence-Empowered Chatbot for Effective COVID-19 Information Delivery to Older Adults

Wang, Xin; Liang, Tianyi; Li, Juan; Roy, Souradip; Pandey, Vikram; Du, Yang; Kong, Jun

doi:10.4018/ijehmc.293285

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…Then, it uses named entity recognition [ 24 ] to extract important information such as the user's intent, entities (relevant keywords or phrases), and context from these components. We use machine learning algorithms (eg, our previous proposed algorithm [ 25 ]) to analyze the user’s input and match it with the most relevant intent. To train the model, we provide sample user inputs and assign them to specific intents.…”

Section: Methodsmentioning

confidence: 99%

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Wang,

Li,

Liang

et al. 2024

JMIR Aging

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Background Reminiscence, a therapy that uses stimulating materials such as old photos and videos to stimulate long-term memory, can improve the emotional well-being and life satisfaction of older adults, including those who are cognitively intact. However, providing personalized reminiscence therapy can be challenging for caregivers and family members. Objective This study aimed to achieve three objectives: (1) design and develop the GoodTimes app, an interactive multimodal photo album that uses artificial intelligence (AI) to engage users in personalized conversations and storytelling about their pictures, encompassing family, friends, and special moments; (2) examine the app’s functionalities in various scenarios using use-case studies and assess the app’s usability and user experience through the user study; and (3) investigate the app’s potential as a supplementary tool for reminiscence therapy among cognitively intact older adults, aiming to enhance their psychological well-being by facilitating the recollection of past experiences. Methods We used state-of-the-art AI technologies, including image recognition, natural language processing, knowledge graph, logic, and machine learning, to develop GoodTimes. First, we constructed a comprehensive knowledge graph that models the information required for effective communication, including photos, people, locations, time, and stories related to the photos. Next, we developed a voice assistant that interacts with users by leveraging the knowledge graph and machine learning techniques. Then, we created various use cases to examine the functions of the system in different scenarios. Finally, to evaluate GoodTimes’ usability, we conducted a study with older adults (N=13; age range 58-84, mean 65.8 years). The study period started from January to March 2023. Results The use-case tests demonstrated the performance of GoodTimes in handling a variety of scenarios, highlighting its versatility and adaptability. For the user study, the feedback from our participants was highly positive, with 92% (12/13) reporting a positive experience conversing with GoodTimes. All participants mentioned that the app invoked pleasant memories and aided in recollecting loved ones, resulting in a sense of happiness for the majority (11/13, 85%). Additionally, a significant majority found GoodTimes to be helpful (11/13, 85%) and user-friendly (12/13, 92%). Most participants (9/13, 69%) expressed a desire to use the app frequently, although some (4/13, 31%) indicated a need for technical support to navigate the system effectively. Conclusions Our AI-based interactive photo album, GoodTimes, was able to engage users in browsing their photos and conversing about them. Preliminary evidence supports GoodTimes’ usability and benefits cognitively intact older adults. Future work is needed to explore its potential positive effects among older adults with cognitive impairment.

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Section: Methodsmentioning

confidence: 99%

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Wang,

Li,

Liang

et al. 2024

JMIR Aging

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“…The characteristics of 17 chatbots are presented in Table 2. Fourteen chatbots (88.2%) were programmed to converse in English (7,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37). Four chatbots were bilingual and supported languages such as French, Italian, Vietnamese, and Indian in addition to English.…”

Section: Chatbots Characteristicsmentioning

confidence: 99%

“…Thirteen out of the 17 chatbots were developed for the general population, and four were designed for specific populations. Chatbots designed for specific audiences had a preventive role (25,36,38,39). Governments, large corporations (such as IBM and Amazon), and international organizations (such as WHO) produced the majority of AI chatbots aimed at the general public.…”

Section: Chatbots Characteristicsmentioning

confidence: 99%

“…The most common application of AI chatbots was disseminating information and providing education related to COVID-19 (13 cases) (7,25,27,29,30,(33)(34)(35)(36)(37)(38)(39)(40). These chatbots provide various information, such as symptoms, disease diagnosis, disease stages, virus transmission, and the availability of healthcare services and how to access them.…”

Section: Information Dissemination and Educationmentioning

confidence: 99%

“…Nine AI chatbots were used for triaging and assessing users during the COVID-19 pandemic (7,26,28,30,31,33,34,36,40). These chatbots ask a series of questions to evaluate the likelihood of COVID-19 infection without…”

Section: Self-assessment and Screeningmentioning

confidence: 99%

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Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review

Mahdavi,

Amanzadeh,

Hamedan

et al. 2023

Shiraz E-Med J

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Context: Artificial intelligence (AI) Chatbots are computer programs that simulate human conversation and use artificial intelligence, including machine learning and natural language processing, to interact with users via natural language. With the outbreak of the COVID-19 pandemic, the use of digital health technologies such as chatbots has accelerated. Objectives: This study aims to investigate the application of AI chatbots in combating the COVID-19 pandemic and explore their features. Methods: We reviewed the literature on health chatbots during the COVID-19 pandemic. PubMed, Scopus, Web of Science, and Google Scholar were searched using relevant keywords such as “chatbot”, “conversational agent,” and “artificial intelligence”. To select the relevant articles, we conducted title, abstract, and full-text screening based on inclusion and exclusion criteria. Chatbots, their applications, and design features were extracted from the selected articles. Results: Out of 673 articles initially identified, 17 articles were eligible for inclusion. We categorized the selected AI chatbots based on their roles, applications, and design characteristics. Around 70% of chatbots were designed to play a preventive role. Our review identified 8 key applications of the AI chatbots during the COVID-19 pandemic, which include (1) information dissemination and education, (2) self-assessment and screening, (3) connecting to health centers, (4) combating misinformation and fake news, (5) patient tracking and service delivery (6) mental health (7) monitoring exposure (8) vaccine information and scheduling. AI chatbots were deployed on various platforms, including mobile apps, web, and social media. Mobile-based chatbots were the most frequent. All chatbots used Natural Language Understanding (NLU) methods to understand natural language input and act on the user’s request. More than 50% of AI chatbots used NLU platforms, including Google Dialogflow, Rasa framework, and IBM Watson. Conclusions: AI chatbots can play an effective role in combating the COVID-19 pandemic. Increasing people’s awareness, optimizing the use of health resources, and reducing unnecessary encounters are some of the advantages of using AI chatbots during the COVID-19 outbreak. Using NLU platforms can be a suitable solution for developing AI chatbots in the healthcare domain. With advancements in the field of artificial intelligence, it seems that AI chatbots will have a promising future in healthcare, particularly in public health, chronic disease management, and mental health.

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Human-chatbot interaction studies through the lens of bibliometric analysis

Chen,

Guo,

Ren

et al. 2023

Univ Access Inf Soc

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Artificial Intelligence-Empowered Chatbot for Effective COVID-19 Information Delivery to Older Adults

Cited by 10 publications

References 38 publications

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review

Human-chatbot interaction studies through the lens of bibliometric analysis

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