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已发表论文
在监控与支持之间：中国结核病患者对人工智能辅助远程医疗服务的期望与担忧的定性研究
Authors Wang X, Xu L, Zhang H, Fu Q
Received 13 June 2025
Accepted for publication 19 August 2025
Published 21 November 2025 Volume 2025:19 Pages 3717—3729
DOI https://doi.org/10.2147/PPA.S546926
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Johnny Chen
Xiaojun Wang,1,* Luo Xu,2,* Han Zhang,2 Qian Fu2
1Wuhan Pulmonary Hospital, Medical Department, Jianghan University, Wuhan, Hubei, People’s Republic of China; 2School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qian Fu, Email fuqian@hust.edu.cn
Objective: This study explores how tuberculosis (TB) patients in China perceive AI-assisted remote health services, focusing on the psychological and sociocultural dynamics involved in balancing perceived support and perceived surveillance.
Methods: A qualitative descriptive approach was adopted. 25 TB patients were recruited from urban and rural health facilities in Hubei Province, including both those currently in treatment and those who had recently completed it. In-depth, semi-structured interviews were conducted to examine patients’ treatment experiences, digital literacy, and attitudes toward AI-assisted care. The AI system described to participants was a hypothetical prototype based on emerging technologies rather than an implemented service. Thematic analysis was guided by the Health Belief Model and Affordance Theory to identify key patterns and interpret their meanings.
Results: Five key themes emerged. Patients reported treatment fatigue and fluctuating motivation, reflecting complex psychological demands. Trust in AI systems was conditional, shaped by concerns about usability, digital unfamiliarity, and system reliability. Participants experienced a tension between viewing AI tools as supportive and feeling uncomfortable with constant monitoring, especially given the stigmatized and regulated nature of TB. A strong desire to preserve autonomy and dignity shaped patients’ preferences for systems that minimize disruption and allow self-regulation. Acceptability was influenced by interface simplicity, preferred modalities such as voice-based prompts, and the assurance that AI would supplement rather than replace human care. These findings were synthesized into a conceptual framework, illustrating how treatment burden, psychological interpretations of AI, and perceived empowerment converge into a process of contextualized acceptance.
Conclusion: This study offers new insight into digital health engagement among an underserved population. It shows that TB patients do not passively receive AI interventions but interpret and evaluate them in light of their experiences and expectations. Designing acceptable AI-assisted systems requires sensitivity to patients’ social contexts, emotional needs, and desire for agency in care.
Keywords: tuberculosis, artificial intelligence, health management, expectation, concern, qualitative study
1Wuhan Pulmonary Hospital, Medical Department, Jianghan University, Wuhan, Hubei, People’s Republic of China; 2School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qian Fu, Email fuqian@hust.edu.cn
Objective: This study explores how tuberculosis (TB) patients in China perceive AI-assisted remote health services, focusing on the psychological and sociocultural dynamics involved in balancing perceived support and perceived surveillance.
Methods: A qualitative descriptive approach was adopted. 25 TB patients were recruited from urban and rural health facilities in Hubei Province, including both those currently in treatment and those who had recently completed it. In-depth, semi-structured interviews were conducted to examine patients’ treatment experiences, digital literacy, and attitudes toward AI-assisted care. The AI system described to participants was a hypothetical prototype based on emerging technologies rather than an implemented service. Thematic analysis was guided by the Health Belief Model and Affordance Theory to identify key patterns and interpret their meanings.
Results: Five key themes emerged. Patients reported treatment fatigue and fluctuating motivation, reflecting complex psychological demands. Trust in AI systems was conditional, shaped by concerns about usability, digital unfamiliarity, and system reliability. Participants experienced a tension between viewing AI tools as supportive and feeling uncomfortable with constant monitoring, especially given the stigmatized and regulated nature of TB. A strong desire to preserve autonomy and dignity shaped patients’ preferences for systems that minimize disruption and allow self-regulation. Acceptability was influenced by interface simplicity, preferred modalities such as voice-based prompts, and the assurance that AI would supplement rather than replace human care. These findings were synthesized into a conceptual framework, illustrating how treatment burden, psychological interpretations of AI, and perceived empowerment converge into a process of contextualized acceptance.
Conclusion: This study offers new insight into digital health engagement among an underserved population. It shows that TB patients do not passively receive AI interventions but interpret and evaluate them in light of their experiences and expectations. Designing acceptable AI-assisted systems requires sensitivity to patients’ social contexts, emotional needs, and desire for agency in care.
Keywords: tuberculosis, artificial intelligence, health management, expectation, concern, qualitative study