Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Abstract

ObjectiveStriking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real‐world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence‐/information technology‐based clinical decision support tools in China.MethodsPubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision‐making.ResultsA total of 37 peer‐reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single‐center electronic medical records were the mainstream data sources leveraged to inform clinical decision‐making, with internal validation being predominately used for model evaluation.ConclusionsTo effectively promote the extensive use of real‐world data and drive a paradigm shift in clinical decision‐making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

“…This can help provide faster and more reliable diagnosis and reduce human errors. 28 , 29 , 34 , 36 - 38 , 43 , 45 , 52 Besides that, AI has shown the capability to streamline the drug prescription process. 31 Moreover, one paper reported that AI can aid in patient triage by offering guidance and evaluating the severity and priority of cases.…”