Tuberculosis (TB) remains a significant public health challenge in many low- and middle-income countries, including China, where the disease is highly prevalent and difficult to manage. Despite advances in biomedical treatments and policy-driven control strategies, effective TB management relies heavily on patients' adherence to lengthy and demanding treatment regimens. China's implementation of the Directly Observed Treatment, Short-course (DOTS) strategy has been critical for national TB control, but it relies on a labor-intensive supervision model that may exacerbate stigma, especially for patients with limited health literacy or from disadvantaged backgrounds.
The limitations of DOTS, particularly the burden of face-to-face supervision, have sparked interest in digital health technologies as more flexible and patient-centered alternatives. However, digital health adoption in China is uneven, with urban-rural disparities in health literacy, infrastructure, and trust in technology posing challenges for equitable access and use. This study explores how AI-assisted remote health systems can address these challenges and improve TB care, focusing on the psychological and sociocultural tensions between perceived support and surveillance.
The study reveals that TB patients face a complex motivational landscape, with adherence influenced by practical and emotional factors. Attitudes toward AI systems are characterized by conditional trust, reflecting a balance of curiosity and caution shaped by digital literacy and system reliability concerns. AI technologies are simultaneously viewed as supportive and intrusive, revealing a tension between their monitoring functions and patients' desire for dignity and privacy. Autonomy emerges as a central concern, with participants favoring systems that minimize disruption and respect personal agency.
Ultimately, the acceptability of AI-assisted services depends on usability, clarity, and their perceived role as complementary to human care. This study contributes to the theoretical understanding of digital health engagement by integrating the Health Belief Model (HBM) and Affordance Theory to interpret patient evaluations of emerging technologies. The findings highlight the importance of addressing patients' preferences, limitations, and trust concerns for successful implementation, emphasizing the need for inclusive innovation frameworks that prioritize infrastructure development, affordability, and participatory system design, particularly for vulnerable or digitally underserved populations.
In conclusion, while patients acknowledge the potential benefits of AI-assisted remote health services, they also express concerns about surveillance, loss of autonomy, and usability. Designing acceptable and effective systems requires attention to patients' lived experiences, digital capabilities, and sociocultural contexts. Incorporating patient perspectives into system development is essential to ensure that AI technologies are human-centered, ethically responsive, and sensitive to both individual and social dimensions of care.
