ArticleJMIR medical informatics2021
A Chatbot for Perinatal Women's and Partners' Obstetric and Mental Health Care: Development and Usability Evaluation Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Usability and User Experience Assessment of Health Care Conversational Agents Using Validated Subjective Instruments: Systematic Review and Comparative Analysis.JMIR human factors · 2026Pooled it
- An exploratory study of the use of artificial intelligence-based virtual patients to enhance dentist-patient communication training.BMC medical education · 2026Trial
- A behaviourally informed chatbot increases vaccination rates in Argentina more than a one-way reminder.Nature human behaviour · 2024Trial
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Perceived health and postpartum depression among perinatal women: a cross-sectional study.BMC pregnancy and childbirth · 2026Article
- Increasing Use of a Postpartum and Newborn Chatbot among Birthing Individuals and Caregivers: Development and Implementation Study.JMIR pediatrics and parenting · 2026Article
- Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review.Oxford open digital health · 2026Review
- Evaluating the reliability and clinical utility of artificial intelligence in first trimester prenatal screening and noninvasive prenatal testing.Scientific reports · 2025Article
- Article
- Charting the evolution of artificial intelligence mental health chatbots from rule-based systems to large language models: a systematic review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Father-inclusive chatbot-based prenatal education during COVID-19 pandemic enhances maternal-fetal attachment in Korean primigravida women across levels of partner support.Scientific reports · 2025Article
- Exploring the Requirements for an mHealth App to Prevent and Manage Perinatal Anxiety and Depression.Health science reports · 2025Article
- Implementing a chatbot to promote hereditary breast & ovarian cancer genetic screening in women's health: identifying barriers and facilitators to screening adoption.BMC public health · 2025Article
- Users' Needs for Mental Health Apps: Quality Evaluation Using the User Version of the Mobile Application Rating Scale.JMIR mHealth and uHealth · 2025Article
- Women's Preferences and Willingness to Pay for AI Chatbots in Women's Health: Discrete Choice Experiment Study.Journal of medical Internet research · 2025Article
- Revolutionizing Medicine: Chatbots as Catalysts for Improved Diagnosis, Treatment, and Patient Support.Cureus · 2025Review
- Interactive Conversational Agents for Perinatal Health: A Mixed Methods Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Exploring health professionals' views on the depiction of conversational agents as health professionals: a qualitative descriptive study.Frontiers in digital health · 2025Article
- Evaluating a retrieval-augmented pregnancy chatbot: a comprehensibility-accuracy-readability study of the DIAN AI assistant.Frontiers in artificial intelligence · 2025Article
- Development and Refinement of a Chatbot for Birthing Individuals and Newborn Caregivers: Mixed Methods Study.JMIR pediatrics and parenting · 2024Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo motivate people to adopt medical chatbots, the establishment of a specialized medical knowledge database that fits their personal interests is of great importance in developing a chatbot for perinatal care, particularly with the help of health professionals.
objectiveThe objectives of this study are to develop and evaluate a user-friendly question-and-answer (Q&A) knowledge database-based chatbot (Dr. Joy) for perinatal women's and their partners' obstetric and mental health care by applying a text-mining technique and implementing contextual usability testing (UT), respectively, thus determining whether this medical chatbot built on mobile instant messenger (KakaoTalk) can provide its male and female users with good user experience.
methodsTwo men aged 38 and 40 years and 13 women aged 27 to 43 years in pregnancy preparation or different pregnancy stages were enrolled. All participants completed the 7-day-long UT, during which they were given the daily tasks of asking Dr. Joy at least 3 questions at any time and place and then giving the chatbot either positive or negative feedback with emoji, using at least one feature of the chatbot, and finally, sending a facilitator all screenshots for the history of the day's use via KakaoTalk before midnight. One day after the UT completion, all participants were asked to fill out a questionnaire on the evaluation of usability, perceived benefits and risks, intention to seek and share health information on the chatbot, and strengths and weaknesses of its use, as well as demographic characteristics.
resultsDespite the relatively higher score of ease of learning (EOL), the results of the Spearman correlation indicated that EOL was not significantly associated with usefulness (ρ=0.26; P=.36), ease of use (ρ=0.19; P=.51), satisfaction (ρ=0.21; P=.46), or total usability scores (ρ=0.32; P=.24). Unlike EOL, all 3 subfactors and the total usability had significant positive associations with each other (all ρ>0.80; P<.001). Furthermore, perceived risks exhibited no significant negative associations with perceived benefits (ρ=-0.29; P=.30) or intention to seek (SEE; ρ=-0.28; P=.32) or share (SHA; ρ=-0.24; P=.40) health information on the chatbot via KakaoTalk, whereas perceived benefits exhibited significant positive associations with both SEE and SHA. Perceived benefits were more strongly associated with SEE (ρ=0.94; P<.001) than with SHA (ρ=0.70; P=.004).
conclusionsThis study provides the potential for the uptake of this newly developed Q&A knowledge database-based KakaoTalk chatbot for obstetric and mental health care. As Dr. Joy had quality contents with both utilitarian and hedonic value, its male and female users could be encouraged to use medical chatbots in a convenient, easy-to-use, and enjoyable manner. To boost their continued usage intention for Dr. Joy, its Q&A sets need to be periodically updated to satisfy user intent by monitoring both male and female user utterances.
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