Academic Journal
Examining user-AI interaction patterns in health-Information queries.
| Τίτλος: | Examining user-AI interaction patterns in health-Information queries. |
|---|---|
| Συγγραφείς: | Wang JT; Department of Information Systems, College of Business, University of Nevada, Reno, 1664 N. Virginia Street, NV 89557, USA. Electronic address: jadewang@unr.edu., Chung HW; Department of Information Systems, College of Business, University of Nevada, Reno, 1664 N. Virginia Street, NV 89557, USA. Electronic address: hyunwooc@unr.edu., Do GN; Department of Information Systems, College of Business, University of Nevada, Reno, 1664 N. Virginia Street, NV 89557, USA. Electronic address: ngocquynhgiand@unr.edu., Yang AT; Department of Information Systems, College of Business, University of Nevada, Reno, 1664 N. Virginia Street, NV 89557, USA. Electronic address: alany@unr.edu. |
| Πηγή: | International journal of medical informatics [Int J Med Inform] 2026 Jul 15; Vol. 215, pp. 106453. Date of Electronic Publication: 2026 Apr 26. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997- |
| Ιατρικοί όροι (MeSH): | Data Mining*/methods , Artificial Intelligence* , User-Computer Interface* , Consumer Health Information*, Humans ; Generative Artificial Intelligence ; Support Vector Machine ; Digital Health |
| Περίληψη: | In this study, we examine how individuals utilize generative artificial intelligence (GAI) when seeking health-related information. Using a dataset of user-GAI chat logs available on Hugging Face, we analyzed real-world interactions in which users posed health-related questions to a generative model. We applied a combination of data and text-analytic methods to categorize these interactions, including supervised machine learning techniques such as Support Vector Machines (SVMs). SVMs were selected for their efficiency and strong performance in high-dimensional text classification tasks, and used to identify recurrent themes in user queries and interactions. We found that users frequently consult AI chatbots for symptom exploration, medical education, mental health support, and general health advice. The findings suggest that GAI tools may not only function as informational resources, but also as preliminary support tools that can shape users' health knowledge and encourage them to seek consultation with medical professionals. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Generative artificial intelligence; Online health information seeking; Support vector machine; Text-mining |
| Entry Date(s): | Date Created: 20260430 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260716 |
| DOI: | 10.1016/j.ijmedinf.2026.106453 |
| PMID: | 42061119 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-8243 |
|---|---|
| DOI: | 10.1016/j.ijmedinf.2026.106453 |