e.g. mhealth
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Skip search results from other journals and go to results- 48 Journal of Medical Internet Research
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By using advanced AI tools such as LLMs and chatbots, ERs and PA formulation is becoming increasingly scientific and personalized [21]. This not only has the potential to optimize exercise outcomes but may also enhance safety, ushering in a new era in chronic disease prevention and health management.
To our knowledge, this paper represents one of the first scoping reviews of the applications of LLMs in the fields of ERs and PA, with 2 primary objectives.
JMIR Med Inform 2025;13:e59309
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chatbotsChatbots and Conversational Agents
J Med Internet Res 2025;27:e67365
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chatbotsChatbots and Conversational Agents
JMIR Pediatr Parent 2025;8:e58752
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It is increasingly imperative that we as HCI researchers analyze the diverse ways in which such chatbots, which claim to aid mental health, are promoted, described, and used, particularly given their use by at-risk users who may not fully recognize their intended purpose. This work then investigated the relationship between design and care as manifested in today’s mental health–related chatbots.
J Med Internet Res 2025;27:e67114
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The extracted data were grouped into categories based on the type of digital technology used (ie, social media, mobile phone apps, health information systems, mobile SMS or SMS text messaging, chatbots, interactive voice response (IVR), and hotline.
J Med Internet Res 2025;27:e68352
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Our pilot study compares AI chatbot responses to potential patient questions, with the primary goal of comparing the utility of three chatbots by assessing their strengths and weaknesses. As suggested by Parker, recommending the usage of AI in place of existing patient education materials would require a larger, more robust investigation that compares AI to existing resources.
JMIR Dermatol 2025;8:e72540
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chatbotsChatbots and Conversational Agents
JMIR Dermatol 2025;8:e71768
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Finally, Yang et al [10] pointed to the high potential that medical chatbots have in clinical settings, while Gilbert et al [11] warned of the need to extensively test health care chatbots.
However, current methods of creating and evaluating early-stage health care bots face steep development costs due to the high level of human involvement in each phase of the development process. In this study, we present a novel, bot-driven method of developing, testing, and evaluating automated health care chatbots.
JMIR Nursing 2025;8:e63058
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One key limitation is the need for further evidence to confirm the long-term effectiveness of mental health chatbots through trials replicated with longer durations and exploration of the chatbots’ efficacy in comparison with other active controls [13-15].
JMIR Ment Health 2025;12:e64396
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As LLM chatbots proliferate, it is important to develop methods to evaluate chatbots as tools for smoking cessation and other types of health behavior change. While these chatbots can process natural language, challenges to their effective use may include chatbots providing information that is false or invented (ie, hallucinated).
J Med Internet Res 2025;27:e66896
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