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Expert and Interdisciplinary Analysis of AI-Driven Chatbots for Mental Health Support: Mixed Methods Study

Expert and Interdisciplinary Analysis of AI-Driven Chatbots for Mental Health Support: Mixed Methods Study

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.

Kayley Moylan, Kevin Doherty

J Med Internet Res 2025;27:e67114

Technology-Assisted Interventions in the Delivery of HIV Prevention, Care, and Treatment Services in Sub-Saharan Africa: Scoping Review

Technology-Assisted Interventions in the Delivery of HIV Prevention, Care, and Treatment Services in Sub-Saharan Africa: Scoping Review

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.

Louis Henry Kamulegeya, Ivan Kagolo, Brenda Kabakaari, Joan Atuhaire, Racheal Nasamula, J M Bwanika

J Med Internet Res 2025;27:e68352

Authors’ Reply: The Importance of Comparing New Technologies (AI) to Existing Tools for Patient Education on Common Dermatologic Conditions: A Commentary

Authors’ Reply: The Importance of Comparing New Technologies (AI) to Existing Tools for Patient Education on Common Dermatologic Conditions: A Commentary

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.

Courtney Chau, Hao Feng, Gabriela Cobos, Joyce Park

JMIR Dermatol 2025;8:e72540

Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

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.

Seungheon Choo, Suyoung Yoo, Kumiko Endo, Bao Truong, Meong Hi Son

JMIR Nursing 2025;8:e63058

The Efficacy of Conversational AI in Rectifying the Theory-of-Mind and Autonomy Biases: Comparative Analysis

The Efficacy of Conversational AI in Rectifying the Theory-of-Mind and Autonomy Biases: Comparative Analysis

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].

Marcin Rządeczka, Anna Sterna, Julia Stolińska, Paulina Kaczyńska, Marcin Moskalewicz

JMIR Ment Health 2025;12:e64396

Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis

Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis

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).

Lorien C Abroms, Artin Yousefi, Christina N Wysota, Tien-Chin Wu, David A Broniatowski

J Med Internet Res 2025;27:e66896

Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis

Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis

The proportion of medical health–related knowledge obtained through the internet is large, and chatbots are also used to answer various medical questions [9,10]. Researchers have performed many evaluations and studies on chatbots to answer medical questions, including ophthalmology, pediatric, urology, dentistry, and other professional directions, involving myopia, cirrhosis, hypertension, obesity, and other diseases and medical examination questions [11,12].

Yong Zhang, Xiao Lu, Yan Luo, Ying Zhu, Wenwu Ling

JMIR Med Inform 2025;13:e63924

Scalable Technology for Adolescents and Youth to Reduce Stress in the Treatment of Common Mental Disorders in Jordan: Protocol for a Randomized Controlled Trial

Scalable Technology for Adolescents and Youth to Reduce Stress in the Treatment of Common Mental Disorders in Jordan: Protocol for a Randomized Controlled Trial

One such delivery method is using conversational agents, more commonly known as chatbots. Chatbots may provide a potential alternative and highly engaging method to deliver such interventions. Chatbots are text-based automated conversational agents that give the impression of speaking to a human, although the user is aware beforehand, that they are speaking with a computer program. They may use machine learning and respond to user inputs or use a decision tree format with users following a defined path.

Aemal Akhtar, Anne Marijn de Graaff, Rand Habashneh, Dharani Keyan, Adnan Abualhaija, Sarah Fanatseh, Muhannad Faroun, Ibrahim Said Aqel, Latefa Dardas, Chiara Servili, Mark van Ommeren, Richard Bryant, Kenneth Carswell

JMIR Res Protoc 2024;13:e54585

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study

A total of 5 board-certified emergency faculty (EH, MIL, JSR, JS, and WW) validated the completeness and accuracy of each response to these 10 questions from the 4 chatbots. We assessed the reliability of chatbots for emergency medical advice with a standardized grading rubric to mitigate evaluator bias and enhance objectivity.

Jonathan Yi-Shin Yau, Soheil Saadat, Edmund Hsu, Linda Suk-Ling Murphy, Jennifer S Roh, Jeffrey Suchard, Antonio Tapia, Warren Wiechmann, Mark I Langdorf

J Med Internet Res 2024;26:e60291