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Trust and perceived usefulness are key to adopting large language models (LLMs) in healthcare, more than performance. This study highlights the need for managing trust and readiness for equitable AI integration in medical settings.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Sociotechnical Systems

Background:

  • Large language models (LLMs) like ChatGPT are revolutionizing health information access but face adoption barriers in healthcare.
  • Trust, privacy, and digital readiness are critical concerns, especially in low- and middle-income countries.

Purpose of the Study:

  • To investigate how trust, information behavior, and sociotechnical readiness influence LLM adoption among healthcare professionals (HCPs) and patients/caregivers (PCs) in China.
  • To identify key factors driving or hindering the use of LLMs for medical information and decision support.

Main Methods:

  • A multicenter, cross-sectional mixed-methods study involving surveys and interviews with 240 HCPs and 480 PCs.
  • Quantitative analysis using logistic regression, random forest, and extreme gradient boosting with SHAP interpretability.
  • Qualitative thematic analysis of interviews to understand role-specific expectations and concerns.

Main Results:

  • Trust was the dominant predictor for LLM adoption in both HCPs (OR 3.78) and PCs (OR 36.34).
  • For HCPs, previous use and legal clarity facilitated adoption, while privacy concerns hindered it.
  • Perceived usefulness, education, and digital tool use positively influenced PC adoption.
  • High model performance (AUC 0.83-0.96) indicated strong predictive accuracy.

Conclusions:

  • LLM adoption in healthcare hinges on managing trust, literacy, and institutional readiness, not just algorithmic performance.
  • Trust is a multidimensional construct encompassing transparency, reliability, and contextual validation.
  • Findings provide practical guidance for designing trustworthy AI systems in healthcare, emphasizing user-centered design and clear accountability.