{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:30:10Z","timestamp":1776083410181,"version":"3.50.1"},"reference-count":98,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The popularity of ChatGPT has raised questions surrounding AI\u2019s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing the potential uses of ChatGPT, there is a lack of a systematic approach to identify and analyze the use cases of ChatGPT in health. This study establishes a framework for gathering and identifying tweets (i.e., posts on social media site \u201cX\u201d, formerly known as Twitter) that discuss health use cases of ChatGPT, integrating topic modeling with constructivist grounded theory (CGT) to organize these topics into common categories. Using this framework, nine topics were identified, which were further grouped into four categories: (1) Clinical Workflow, (2) Wellness, (3), Diseases, and (4) Gender Identity. The Clinical Workflow category was the most popular category, and included four topics: (1) Seeking Advice, (2) Clinical Documentation, (3) Medical Diagnosis, and (4) Medical Treatment. Among the identified topics, \u201cDiet and Workout Plans\u201d was the most popular topic. This research highlights the potential of social media to identify the health use cases and potential health applications of an AI-based chatbot such as ChatGPT. The identified topics and categories can be beneficial for researchers, professionals, companies, and policymakers working on health use cases of AI chatbots.<\/jats:p>","DOI":"10.3390\/bdcc8100130","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T12:02:10Z","timestamp":1728388930000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Health Use Cases of AI Chatbots: Identification and Analysis of ChatGPT Prompts in Social Media Discourses"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-7497","authenticated-orcid":false,"given":"Amir","family":"Karami","sequence":"first","affiliation":[{"name":"Department of Management, Information Systems & Quantitative Methods, Birmingham, Collat School of Business, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}]},{"given":"Zhilei","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Management, Information Systems & Quantitative Methods, Birmingham, Collat School of Business, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}]},{"given":"Xiaoni","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Management, Information Systems & Quantitative Methods, Birmingham, Collat School of Business, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}]},{"given":"Hadi","family":"Kharrazi","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA"}]},{"given":"Parisa","family":"Bozorgi","sequence":"additional","affiliation":[{"name":"South Carolina Department of Public Health, Columbia, SC 29021, USA"}]},{"given":"Ali","family":"Bozorgi","sequence":"additional","affiliation":[{"name":"Department Cardiology School of Medicine, Tehran University of Medical Sciences, Tehran 1417613151, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"ref_1","unstructured":"Duffy, V.G. 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