Application of artificial intelligence technology in evidence-based off-label drug use
Off-label drug use is widely practiced in clinical settings. In 2022, the Law on Doctors of the People's Republic of China mandated that off-label medication must be supported by evidence-based medical evidence before implementation. However, evidence-based decision-making involves searching, screening, and evaluating vast amounts of relevant evidence. The exponential growth of medical evidence has become a bottleneck and challenge for evidence-based decision-making due to the massive amount of data involved. Fortunately, the application of artificial intelligence (AI) technology presents new opportunities to address this issue. AI technology enables automatic evidence search, analysis, summarization, and tracking, assisting clinical pharmacists in obtaining key information quickly, accurately, and comprehensively while minimizing the risk of human error. This paper focuses on addressing the challenges faced during evidence-based decision-making for off-label medication. By utilizing AI technologies such as BioBERT, T5, UnifiedQA, and GPT-2, we designed functionalities such as semantic search, text classification, information extraction, and generation of decision-making content aligned with the evidence-based process. As a result, the EviMed system has been developed to facilitate real-time search, automatic analysis, and AI-assisted decision-making based on a vast array of global medical evidence. The system is fast, comprehensive, accurate, and capable of automatic updates. Widely adopted in numerous tertiary hospitals nationwide, the system has significantly enhanced the efficiency and standardization of evidence-based decision-making for off-label medication.
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