CoChat
Sign Up
Cited by 5

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Beyond traditional search: New characteristics of online health information seeking about chronic disease with Gen AI

Tang, Zhenyi, Su, Yang, Hansen, Preben, Zhang, Pengyi·2026·Information Processing & Management
DOI 10.1016/j.ipm.2026.104668
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ipm.2026.104668

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 55

  1. Albtoush, 2024, Comparative analysis of Google AI and Chatbot GPT: A study on technological approaches and applications, Kurdish Studies, 12, 4302
  2. Ali, 2023, A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities, Journal of Innovation & Knowledge, 8, 10.1016/j.jik.2023.100333 · DOI · Paper page
  3. Ayoub, 2024, Head-to-head comparison of ChatGPT versus Google search for medical knowledge acquisition, Otolaryngology–Head and Neck Surgery,, 170, 1484, 10.1002/ohn.465 · DOI · Paper page
  4. Bhuyan, 2025, Generative artificial intelligence use in healthcare: Opportunities for clinical excellence and administrative efficiency, Journal of Medical Systems, 49, 10, 10.1007/s10916-024-02136-1 · DOI · Paper page
  5. Biswas, 2023, Role of chat gpt in public health, Annals of Biomedical Engineering, 51, 868, 10.1007/s10439-023-03172-7 · DOI · Paper page
  6. Boldi, 2011, Query reformulation mining: Models, patterns, and applications, Information Retrieval, 14, 257, 10.1007/s10791-010-9155-3 · DOI
  7. Busch, 2025, Current applications and challenges in large language models for patient care: A systematic review, Communications Medicine, 5, 26, 10.1038/s43856-024-00717-2 · DOI · Paper page
  8. Chaiken, 1980, Heuristic versus systematic information processing and the use of source versus message cues in persuasion, Journal of Personality and Social Psychology, 39, 752, 10.1037/0022-3514.39.5.752 · DOI · Paper page
  9. Chen, 2025, Online health information seeking behavior among breast cancer patients and survivors: A scoping review, BMC Women's Health, 25, 1, 10.1186/s12905-024-03509-x · DOI
  10. Cho, 2023, User acceptance on content optimization algorithms: Predicting filter bubbles in conversational AI services, Universal Access in the Information Society, 22, 1325, 10.1007/s10209-022-00913-8 · DOI
  11. Chou, 2018, Addressing health-related misinformation on social media, Jama, 320, 2417, 10.1001/jama.2018.16865 · DOI
  12. Dziri, 2022, Evaluating attribution in dialogue systems: The BEGIN benchmark, Transactions of the Association for Computational Linguistics, 10, 1066, 10.1162/tacl_a_00506 · DOI
  13. Ellis, 1989, A behavioural approach to information retrieval system design, Journal of Documentation, 45, 171, 10.1108/eb026843 · DOI · Paper page
  14. Eysenbach, 2002, How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews, BMJ, 324, 573, 10.1136/bmj.324.7337.573 · DOI
  15. Gatt, 2018, Survey of the state of the art in natural language generation: Core tasks, applications and evaluation, Journal of Artificial Intelligence Research, 61, 65, 10.1613/jair.5477 · DOI · Paper page
  16. Gigerenzer, 2011, Heuristic decision making, Annual Review of Psychology, 62, 451, 10.1146/annurev-psych-120709-145346 · DOI · Paper page
  17. Groner, 2014, Approaches to heuristics: A historical review, 1
  18. Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., & Bi, X. (2025). Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948. https://doi.org/10.48550/arXiv.2501.12948. · DOI · Paper page
  19. Jansen, 2007, Defining a session on web search engines, Journal of the American Society for Information Science and Technology, 58, 862, 10.1002/asi.20564 · DOI
  20. Jansen, 2005, A temporal comparison of AltaVista web searching, Journal of the American Society for Information Science and Technology, 56, 559, 10.1002/asi.20145 · DOI
  21. Jia, 2021, Online health information seeking behavior: A systematic review, Healthcare, 1740, 10.3390/healthcare9121740 · DOI · Paper page
  22. Johnson, 1993, A comprehensive model of cancer-related information seeking applied to magazines, Human Communication research, 19, 343, 10.1111/j.1468-2958.1993.tb00305.x · DOI
  23. Kim, 2025, Generative AI characteristics, user motivations, and usage intention, Journal of Computer Information Systems, 1
  24. Kim, 2007, Modeling task-based information seeking on the web: Application of information seeking strategy schema, Proceedings of the American Society for Information Science and Technology, 44, 1
  25. Kinley, 2014, Modeling users' web search behavior and their cognitive styles, Journal of the Association for Information Science and Technology, 65, 1107, 10.1002/asi.23053 · DOI
  26. Kor, 2021, Are people with chronic diseases satisfied with the online health information related to COVID-19 during the pandemic?, Journal of Nursing Scholarship, 53, 75, 10.1111/jnu.12616 · DOI · Paper page
  27. Lebel, 2020, Health anxiety and illness-related fears across diverse chronic illnesses: A systematic review on conceptualization, measurement, prevalence, course, and correlates, PLoS One, 15, 10.1371/journal.pone.0234124 · DOI
  28. Lee, 2023, Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine, New England Journal of Medicine, 388, 1233, 10.1056/NEJMsr2214184 · DOI · Paper page
  29. Liu, 2023, Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, ACM Computing Surveys, 55, 1
  30. Liu, 2024, A systematic review of research on emotional artificial intelligence in English language education, System, 126, 10.1016/j.system.2024.103478 · DOI
  31. Lu, 2024, A multimodal generative AI copilot for human pathology, Nature, 634, 466, 10.1038/s41586-024-07618-3 · DOI · Paper page
  32. Mitsutake, 2023, Chronic diseases and sociodemographic characteristics associated with online health information seeking and using social networking sites: Nationally representative cross-sectional survey in Japan, Journal of Medical Internet Research, 25, 10.2196/44741 · DOI
  33. Moulaei, 2024, Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications, International Journal Of Medical Informatics, 10.1016/j.ijmedinf.2024.105474 · DOI · Paper page
  34. Othman, 2018, Heuristic evaluation: Comparing generic and specific usability heuristics for identification of usability problems in a living museum mobile guide app, Advances in Human-Computer Interaction, 2018
  35. Park, 2013, Understanding science and technology information users through transaction log analysis, Library Hi Tech, 31, 123, 10.1108/07378831311303976 · DOI
  36. Pataranutaporn, 2023, Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness, Nature Machine Intelligence, 5, 1076, 10.1038/s42256-023-00720-7 · DOI · Paper page
  37. Piao, 2023, Human–AI adaptive dynamics drives the emergence of information cocoons, Nature Machine Intelligence, 5, 1214, 10.1038/s42256-023-00731-4 · DOI
  38. Pitz, 1984, Judgment and decision: Theory and application, Annual Review of Psychology, 35, 139, 10.1146/annurev.ps.35.020184.001035 · DOI
  39. Rashkin, H., Smith, E.M., Li, M., & Boureau, Y.-L. (2018). Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207. https://doi.org/10.48550/arXiv.1811.00207. · DOI
  40. Rieh, 2006, Analysis of multiple query reformulations on the web: The interactive information retrieval context, Information Processing & Management, 42, 751, 10.1016/j.ipm.2005.05.005 · DOI
  41. Rosenstock, 1988, Social learning theory and the health belief model, Health education quarterly, 15, 175, 10.1177/109019818801500203 · DOI · Paper page
  42. Rossettini, 2024, Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: A cross-sectional study, BMC Medical Education, 24, 694, 10.1186/s12909-024-05630-9 · DOI · Paper page
  43. Sahoo, P., Singh, A.K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024). A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint arXiv:2402.07927. · arXiv
  44. Sengar, 2024, Generative artificial intelligence: A systematic review and applications, Multimedia Tools and Applications, 1
  45. Shaham, 1992, Stability/instability of cognitive strategies across tasks determine whether stress will affect judgmental processes, Journal of Applied Social Psychology, 22, 691, 10.1111/j.1559-1816.1992.tb00998.x · DOI
  46. Sillence, 2007, How do patients evaluate and make use of online health information?, Social science & medicine, 64, 1853, 10.1016/j.socscimed.2007.01.012 · DOI
  47. Thorngate, 1980, Efficient decision heuristics, Behavioral Science, 25, 219, 10.1002/bs.3830250306 · DOI
  48. Tsoukas, 2009, A dialogical approach to the creation of new knowledge in organizations, Organization science, 20, 941, 10.1287/orsc.1090.0435 · DOI
  49. Vakkari, 2000, Cognition and changes of search terms and tactics during task performance: A longitudinal case study, Content-Based Multimedia Information, 1, 894
  50. Wang, 1996, Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems, 12, 5, 10.1080/07421222.1996.11518099 · DOI · Paper page
  51. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382. · arXiv
  52. Wilson, 1999, Models in information behaviour research, Journal of Documentation, 55, 249, 10.1108/EUM0000000007145 · DOI
  53. Wolfram, 2008, Search characteristics in different types of web-based IR environments: Are they the same?, Information Processing & Management, 44, 1279, 10.1016/j.ipm.2007.07.010 · DOI
  54. Xu, R., Le, N., Park, R., Murray, L., Das, V., Kumar, D., & Goldberg, B. (2024). New contexts, old heuristics: How young people in India and the US trust online content in the age of generative AI. arXiv preprint arXiv:2405.02522. https://doi.org/10.48550/arXiv.2405.02522. · DOI
  55. Yin, 2024, A survey on multimodal large language models, National Science Review, 11, nwae403, 10.1093/nsr/nwae403 · DOI · Paper page

Cited by

Sorted by citations

See all 5

Metadata

Authors
Tang, Z., Su, Y., Hansen, P., & Zhang, P.
ORCID
Venue
Information Processing & Management
Publisher
Elsevier BV
Published
2026-07
Volume
63
Issue
5
Pages
104668
ISSN
0306-4573
DOI
10.1016/j.ipm.2026.104668
Cited by
5
References
18
Language
en
Licence
Funders
National Natural Science Foundation of China (72174014, 72574003)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review