Using a mixed-method approach grounded in social cognitive theory and computational grounded theory, this study systematically analyzes the Chinese public's willingness to use AI-driven medical technology and the factors shaping public perceptions from a health communication perspective. A diverse dataset was gathered through surveys and semi-structured interviews, employing a mixed-methods approach that integrated qualitative and quantitative analyses to develop a theoretical framework, thematic clustering analysis, and an emotional mapping of the public's willingness to use AI-driven medical technology and its determinants. Additionally, a mixed-method evaluation framework was employed to validate responses and assess five core categories, eight subcategories, and twenty-three concepts within the theoretical model. The high evaluation score across three analytical dimensions, with a score of 92.3, validated the robustness of the research methodology. Findings indicate that cognitive perceptions, practical engagement, evaluative judgments, feedback mechanisms, and social context influence the public's willingness to use AI-driven medical technologies. These factors manifest in preconceived notions of AI in healthcare, diverse emotional associations, direct experiential interactions, and evolving expectations. These findings offer valuable insights for policy formulation and technological advancement in AI-driven medical services, with potential implications for healthcare practices in China and globally.

Jingsheng Wang, Jun Han. Is AI healthcare a blessing or a curse? An Exploratory Study on the Chinese Public's Willingness to Use AI in Medical Treatment and Its Determinants Based on Computational Grounded Theory. Academic Journal of Medicine & Health Sciences (2025), Vol. 6, Issue 7: 110-123. https://doi.org/10.25236/AJMHS.2025.060714.

[1] Bandura, A., Social Foundations of Thought and Action: A Social Cognitive Theory, Englewood Cliffs, N.J.: Prentice-Hall, 1986.

[2] Yu, G., Online Self-presentation and Perceived Social Capital: An Analysis of Social Media Use from the Social Cognitive Perspective. Journalism Research, 2016(04): 67-74+151.

[3] Johnson, R.B., Onwuegbuzie, A.J., & Turner, L.A., Toward a Definition of Mixed Methods Research. Journal of Mixed Methods Research, 2007, 1(2), 112-133.

[4] Boyd, D., & Crawford, K., Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society, 2012, 15(5), 662-679.

[5] Qiaolei J., & Yadi, Z., Grounded Theory in Computing: Methodological Exploration and Theoretical Construction in the Era of Numerical Intelligence. Journal of Education and Media Studies, 2024(03): 35-41.

[6] Peixin, C., Junyan, S., & Fan, L., The Origins, Cross-Cultural Flow, and System Construction of Health Communication Between China and America. Academic Journal of Zhongzhou, 2024, (06): 166-175.

[7] Rogers, E.M., The Field of Health Communication Today. American Behavioral Scientist, 1994(2): 208-214.

[8] Jing, Y., & Xigen, L., Theoretical Innovation and Model Construction in Health Communication Research. Chinese Journal of Journalism & Communication, 2015, 37(11): 6-20.

[9] Hepeng, J., & Weishan, M., Department of Communication at Cornell University, Institute of Journalism and Communication, Chinese Academy of Social Sciences. Chinese Journal of Journalism & Communication, 2017, 39(02): 66-89.

[10] Siqi, J., & Chunguang, W., A Study on the Basis and Logic of the Structure of the Rural Multi-Care System. Sociological Studies, 2022, 37(01): 46-67+227.

[11] Dawei, L., Rui, S., & Ming, H., Consultation Box: Spatial Communication and the Formation of Rural Doctor-Patient Communities. Media Observer, 2024, (05): 112-120.

[12] Shaojing, S., & Yuetikuer, A., Disciplinary Turn and Framework Construction of Health Communication Research. Global Journal of Media Studies, 2023, 10(01): 94-106.

[13] Hilger-Kolb, J., Diehl, K., Herr, R., & Loerbroks, A., Effort-reward imbalance among students at German universities: associations with self-rated health and mental health, International Archives of Occupational and Environmental Health, 2018, 91(8): 1011-1020.

[14] Qiaolei, J., Zonghai, C., Zizhong, Z., et al., Healthy Aging and Media Influence: An Investigation into the Impact of Media Exposure on the Mental Health of Older Adults. News and Writing, 2024, (08): 5-18.

[15] Weiwei, J., Chao, L., Yabin, W., et al., HEN Management Model of the Patients with Malignant Tumor Based on AI Health Care. Chinese Hospital Management, 2020, 40(02): 44-46.

[16] Xing, L., Ruixue, H., Bo, L., et al., An Introduction to Mixed Methods Appraisal Tool. Chinese General Practice, 2021, 24(31): 4015-4020.

[17] Yue, Z., The Visual Transformation in Health Communication: Historical Evolution, Contemporary Logic, and Future Directions. Jiangxi Social Sciences, 2024, 44(06): 197-205.