Utilization of artificial intelligence in clinical practice: A systematic review of China's experiences

Artificial intelligence (AI) is transforming clinical applications, including diagnostics, treatment planning, drug discovery, and administrative tasks. Despite significant progress, AI remains a double-edged sword, and its implementation requires careful, evidence-based evaluation. To date, few AI applications have been fully integrated into clinical workflows, especially in significant populations.

This study aims to synthesize evidence on AI utilization in clinical practice, identify key facilitators and barriers, and provide recommendations for implementation within relevant sociocultural and demographic contexts.

Following PRISMA guidelines, this review conducted a comprehensive search in Web of Science, Scopus, and PubMed. Bias was assessed using the JBI and NOS tools. Data on study design, population, AI technologies, applications, clinical issues, and outcomes were extracted. Emerging themes were organized using the NASSS framework.

Of 1002 records screened, 28 studies were included, most of which were cross-sectional (57%). Machine learning (ML) (43%) was the most frequently used AI technology. AI application outcomes primarily focused on application performance (61%), clinical outcomes (43%), and patient outcomes (32%). Clinical contexts included infectious diseases, chronic conditions, imaging, and physician-patient interactions. Key facilitators included perceptions of operational efficiency, availability of AI tools, confidence in improved accuracy, alignment with goals, perceived cost-saving potential, and enabling environments. Reported barriers involved ethical and privacy concerns, limited user acceptance, inconsistent accuracy, technical complexity, unclear accountability, trust-related issues, and inadequate infrastructure.

AI in clinical practice holds tremendous potential in diagnostic accuracy, workflow efficiency, patient engagement, and cost-effectiveness. AI-assisted approaches perform at least as well as conventional methods, even better. Key characteristics within specific contextual settings were synthesized, and contextually informed recommendations were proposed to facilitate AI integration and address the identified barriers. Future research should focus on evaluating AI's long-term impact and addressing emerging issues as AI becomes more embedded in clinical workflows.