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The open-ended questions invited participants to reflect on their general attitudes toward mental health chatbots, perceived benefits and challenges in using them for anxiety support, and their views on chatbots as a complement or alternative to traditional care.
The open-ended questions were:
What do you wish an anonymous conversation with a mental health chatbot would give you?
What would make you hesitate to talk to a chatbot about mental health?
JMIR Form Res 2025;9:e66163
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Recent systematic reviews offer preliminary evidence that AI-based chatbots promote healthy lifestyle change [22-25]; smoking cessation [26,27]; and self-management of chronic disorders [6,21,28], such as hypertension [29]. In addition, HCPs [30] and patients [31-33] seem to find chatbots acceptable and would be willing to use them, particularly if they complement clinical care [29,33] and are not used for severe conditions [33,34].
JMIR Res Protoc 2025;14:e72942
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Having worked on and researched chatbots, I read this with great interest but, as per my comments, I am a little confused, as it seems this bot simply refers the user to a person after recognizing an image as an X-ray, for example. I was under the impression from the content or was half expecting the ability to input an image, be that of an X-ray or retina, and it would start giving me some diagnostic information or the like.
Response: Yes, it does.
JMIRx Med 2025;6:e83417
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Overall summary: Having worked on and researched chatbots, I read this with great interest but, as per my comments, I am a little confused, as it seems this bot simply refers the user to a person after recognizing an image as an X-ray, for example. I was under the impression from the content or was half expecting the ability to input an image, be that of an X-ray or retina, and it would start giving me some diagnostic information or the like.
JMIRx Med 2025;6:e83217
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This study aims to contribute to the discussion on how chatbots can be integrated with specialized models applied to bioinformatics. I have previously referred to this as innovating with biomathematics [14]. In my view, there is no more user-friendly interface for such integration than a chatbot powered by an LLM. I hope that this discussion will encourage bioinformaticians to integrate their models into chatbots. This approach is an alternative to the classical user interface/user experience model.
JMIRx Med 2025;6:e56090
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ChatGPT-Based Chatbot for Help Quitting Smoking via Text Messaging: An Interventional Study
One meta-analysis of chatbots for smoking cessation identified 6 trials of such chatbots and found that chatbot use by smokers was associated with a 66% increase in the odds of quitting [10]. The largest and best designed of these was a study by Perski et al [13] that examined the addition of a chatbot to a smoking cessation app and found that offering the chatbot+app increased engagement and improved smoking cessation–related outcomes over offering the app alone.
JMIR Form Res 2025;9:e79402
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Evidence shows that health chatbots tailored to user profiles through personalized content, interfaces, or communication styles improve user satisfaction and dialogue quality [35-37]. This may include aligning messages with a patient’s medical history, goals, or preferences; adapting interface elements such as avatars or notification frequency; and adjusting language to suit varying levels of health literacy or emotional tone.
JMIR Form Res 2025;9:e76978
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Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review
A previous review similarly suggested that AI-powered chatbots may contribute to better health outcomes; however, the evidence was limited due to insufficient technical documentation [12]. These findings should be interpreted with caution, as all included studies exhibited a heightened Ro B—a concern raised by earlier literature as well [12].
The field of CA interventions for chronic disease self-management remains in an early stage [11,14], with many studies being exploratory.
J Med Internet Res 2025;27:e72309
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Generative artificial intelligence (Gen AI)–based tools and chatbots have been widely applied in medicine, facilitating interactions between users and AI through virtual conversational agents and thereby presenting new opportunities for enhancing health care practice and research methodology [1,2]. The increasing use of Gen AI tools linked to chatbots in medical research brings numerous opportunities and supports innovations, but it also poses many challenges and creates issues.
JMIR Res Protoc 2025;14:e64640
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