Journal of Medical Internet Research

View query in Help articles search

Field

e.g. mhealth

Operator

Field

Criteria

e.g. mhealth

Search Results (1 to 10 of 122 Results)

Download search results: CSV END BibTex RIS

Sort by:

Generative Artificial Intelligence Tools in Medical Research (GAMER): Protocol for a Scoping Review and Development of Reporting Guidelines

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.

Xufei Luo, Yih Chung Tham, Mohammad Daher, Zhaoxiang Bian, Yaolong Chen, Janne Estill, GAMER Working Group

JMIR Res Protoc 2025;14:e64640

Download Citation: END BibTex RIS

Download PDF Download XML

An Examination of Generative AI Response to Suicide Inquires: Content Analysis

Positive motivators for youth to utilize chatbots include perceptions of (1) increased privacy and anonymity [23,24], (2) decreased prejudice, (3) enhanced trustworthiness of information, and (4) empathetic responses. Specifically, adolescents who are Black, Hispanic, or LGBTQ have reported accessing online therapy to overcome hesitancy towards receiving help [16]. While chatbots may provide knowledge, chatbots’ communication of empathy may be limited and vary by user [25].

Laurie O Campbell, Kathryn Babb, Glenn W Lambie, B Grant Hayes

JMIR Ment Health 2025;12:e73623

Download Citation: END BibTex RIS

Download PDF Download XML

Leveraging Large Language Models for Simulated Psychotherapy Client Interactions: Development and Usability Study of Client101

Contrary to the prevalence of chatbots that serve as virtual therapists, there is scant lineage of chatbots that instead simulate an individual with mental health issues. In 1972, psychiatrist Colby et al [7] developed PARRY, a chatbot that simulated a person with paranoid schizophrenia. However, PARRY was not intended to serve as a therapy training tool.

Daniel Cabrera Lozoya, Mike Conway, Edoardo Sebastiano De Duro, Simon D'Alfonso

JMIR Med Educ 2025;11:e68056

Download Citation: END BibTex RIS

Download PDF Download XML

“Digital Clinicians” Performing Obesity Medication Self-Injection Education: Feasibility Randomized Controlled Trial

Artificial Intelligence (AI) chatbots have the potential to be used for a variety of medical tasks, including note taking [15] and personalized medicine [16]. Anonymized AI chatbots have been judged to provide better, more concise, empathetic answers to general health queries than verified physicians [17]. Chat GPT-3 is known to be accurate with common chief complaints [18] and GPT-4 recently outscored 99.98% of simulated human readers when diagnosing complex clinical cases [19].

Sean Coleman, Caitríona Lynch, Hemendra Worlikar, Emily Kelly, Kate Loveys, Andrew J Simpkin, Jane C Walsh, Elizabeth Broadbent, Francis M Finucane, Derek O' Keeffe

JMIR Diabetes 2025;10:e63503

Download Citation: END BibTex RIS

Download PDF Download XML

Generative AI–Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial

DMHIs refer to a range of health information technologies (eg, websites, mobile apps, and chatbots) designed to intervene in health conditions by changing behaviors, cognitions, and emotional states [8]. Among these, chatbots stand out as artificial intelligence (AI)–driven tools capable of engaging in human-like conversations.

Jazmin A Reyes-Portillo, Amy So, Kelsey McAlister, Christine Nicodemus, Ashleigh Golden, Colleen Jacobson, Jennifer Huberty

JMIR Form Res 2025;9:e71923

Download Citation: END BibTex RIS

Download PDF Download XML

Population-Based Digital Health Interventions to Deliver at-Home COVID-19 Testing: SCALE-UP II Randomized Clinical Trial

Population-level interventions using digital health tools, such as SMS text messaging and chatbots, and coupled with a human component, are a promising approach for promoting equitable access to preventive care services. Despite disparities in access to digital technologies, cellphone ownership is now ubiquitous. Even in US households earning less than US $30,000 annually, 97% have a cellphone and 76% have a smartphone [12].

Guilherme Del Fiol, Tatyana V Kuzmenko, Brian Orleans, Jonathan J Chipman, Tom Greene, Ray Meads, Kimberly A Kaphingst, Bryan Gibson, Kensaku Kawamoto, Andy J King, Tracey Siaperas, Shlisa Hughes, Alan Pruhs, Courtney Pariera Dinkins, Cho Y Lam, Joni H Pierce, Ryzen Benson, Emerson P Borsato, Ryan C Cornia, Leticia Stevens, Richard L Bradshaw, Chelsey R Schlechter, David W Wetter

J Med Internet Res 2025;27:e74145

Download Citation: END BibTex RIS

Download PDF Download XML

Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study

Traditionally, rule-based chatbots, which use scripted responses to user queries to improve mental health, have been the predominant type of chatbot used by the general public [4] and researchers [5]. Recently, generative artificial intelligence–based chatbots have emerged. These chatbots use technological advances such as large language models (LLMs) to provide more personalized and human-like responses, which has further boosted the popularity of chatbots.

Kunmi Sobowale, Daniel Kevin Humphrey

JMIR Form Res 2025;9:e65605

Download Citation: END BibTex RIS

Download PDF Download XML

Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review

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.

Xiangxun Lai, Jiacheng Chen, Yue Lai, Shengqi Huang, Yongdong Cai, Zhifeng Sun, Xueding Wang, Kaijiang Pan, Qi Gao, Caihua Huang