e.g. mhealth
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Skip search results from other journals and go to results- 96 Journal of Medical Internet Research
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The findings from this phase informed the initial design of the chatbot.
Phase 2: prototype testing (June 2023 to July 2023)—this phase corresponded to the “alpha testing” step in the framework. We tested the chatbot prototype with both eligible women and health care professionals, gathering feedback on usability, content design, content comprehension, and acceptability.
JMIR Cancer 2025;11:e70251
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Oncologists were uncertain about who would bear responsibility if patients experienced adverse outcomes after following chatbot recommendations. This led to hesitation in endorsing or recommending them in clinical practice.
For certain, liability is the biggest concern. If a patient follows advice given by AI and something goes wrong, who will be held responsible?
J Med Internet Res 2025;27:e71418
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With safety in mind, the chatbot was preloaded with clinically approved, pedestrianized responses to questions and queries, which were necessary to ensure the user’s safety while using the chatbot. The scope of the chatbot was limited to the resources on the website only [16], to ensure the clinical accuracy and safety of the resources that the chatbot would surface for the user.
JMIR Diabetes 2025;10:e74032
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Few studies have explored on-demand, chatbot-based interventions that incorporate prospection-based techniques, particularly for student populations who may benefit significantly from improved future-oriented thinking.
JMIR Form Res 2025;9:e74411
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Participants who reside outside Malaysia were excluded because this study aimed to create an AI chatbot that is culturally tailored for MSM living in Malaysia. We recruited 334 participants, who generated 393 interactions with the AI chatbot. An interaction was defined as a contact initiated by a user with the chatbot.
JMIR Hum Factors 2025;12:e70034
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Comparisons among the 3 chatbot models (Chat GPT, Assistants API, and Qwen) were conducted using one-way ANOVA. For pairwise comparisons between 2 chatbot models (Chat GPT and Assistants API), a paired t test was applied. A 2-sided P
The interview transcripts were analyzed using an inductive thematic approach to identify common themes and subthemes related to the participants’ experiences with the Neuro Bot [18].
J Med Internet Res 2025;27:e74299
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A chatbot will be developed and maintained on the Whats App platform due to its wide popularity among local older adults and a user-friendly interface. The architecture of the chatbot system is shown in Figure 2. Users send information to the Whats App instant messaging server, which then delivers the message to the chatbot. The chatbot system processes and sends a message back to Whats App, where users can view the information.
JMIR Res Protoc 2025;14:e68796
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User and Provider Experiences With Health Education Chatbots: Qualitative Systematic Review
Boggiss et al [19] further supported this, noting that nearly all users wanted to customize chatbot interactions.
Trust issues, particularly those related to privacy, hinder adoption. Barnett et al [16] expressed concerns about confidentiality, noting uncertainty regarding who has access to chatbot interactions and thus preferring traditional doctor–patient confidentiality.
Technical issues such as app freezes and connectivity problems were barriers.
JMIR Hum Factors 2025;12:e60205
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Chat GPT is a chatbot designed to understand input presented by users and provide a humanlike text response.
We limited our analysis to flagship organizations for medical specialties that are considered core rotations in medical school (pediatrics, surgery, psychiatry, obstetrics and gynecology, internal medicine, and family medicine). This cross-sectional study was performed in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement.
JMIR Form Res 2025;9:e66204
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Indeed, creating a hybrid chatbot with both rule-based and LLM components can offer a versatile and streamlined user experience by ensuring that key information is covered in the rule-based components of the chatbot and allowing for the LLM component to support complex, open-ended queries that are not covered in the scripted content.
JMIR Cancer 2025;11:e65848
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