This paper is in the following e-collection/theme issue:

Smoking Cessation (629) AI Language Models in Health Care (202) mHealth for Wellness, Behavior Change and Prevention (3315) Artificial Intelligence (2129) Chatbots and Conversational Agents (740) Generative Language Models Including ChatGPT (708)

Published on in Vol 11 (2024)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53778, first published .
Generation of Backward-Looking Complex Reflections for a Motivational Interviewing–Based Smoking Cessation Chatbot Using GPT-4: Algorithm Development and Validation

Generation of Backward-Looking Complex Reflections for a Motivational Interviewing–Based Smoking Cessation Chatbot Using GPT-4: Algorithm Development and Validation

Generation of Backward-Looking Complex Reflections for a Motivational Interviewing–Based Smoking Cessation Chatbot Using GPT-4: Algorithm Development and Validation

Authors of this article:

Ash Tanuj Kumar1 Author Orcid Image ;   Cindy Wang1 Author Orcid Image ;   Alec Dong1 Author Orcid Image ;   Jonathan Rose1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics Author Video

Journals

  1. Andrew A. Overview of the emerging role of chatbots, including large language models, in supporting tobacco smoking and vaping cessation: a narrative review. Global Health Journal 2025;9(1):6 View
  2. Wei B, Yao L, Hu X, Hu Y, Rao J, Ji Y, Dong Z, Duan Y, Wu X. Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study. Journal of Medical Internet Research 2025;27:e67883 View
  3. Karve Z, Calpey J, Machado C, Knecht M, Mejia M. New Doc on the Block: Scoping Review of AI Systems Delivering Motivational Interviewing for Health Behavior Change. Journal of Medical Internet Research 2025;27:e78417 View
  4. Du X, Zhou Z, Wang Y, Chuang Y, Li Y, Yang R, Hong P, Bates D, Zhou L. Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review. International Journal of Medical Informatics 2026;205:106091 View