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Application Strategies for Artificial Intelligence– based Clinical Decision Support System: From the Simulation to the Real-World
2
Zitationen
2
Autoren
2022
Jahr
Abstract
Clinical decision support systems (CDSS) have entered a new age thanks to algorithms based on artificial intelligence (AI) [1].AI has shown the potential to enhance the precision and suitability of CDSS and attracted widespread interest from physicians, hence expanding its potential applications to nearly every aspect of contemporary medicine, from pharmacogenetics to public health [2,3].In-hospital nursing care could be one of the most significant AI-CDSS use cases [4].AI-CDSS can assist hospital nurses in providing high-quality care in an effective and efficient manner.Patient safety-related measures such as minimizing pressure injuries and falls are a top concern [5].Recent efforts have demonstrated the potential benefits of AI-CDSS in various sectors (Figure 1).However, the application of AI-CDSS in a real-world setting is challenging.Whereas the majority of the literature on AI development has been produced in simulation settings (i.e., on researchers' well-controlled stand-alone servers and in silico studies), the application of AI models to the real world necessitates connecting to extremely complex and restricted environments involving numerous interactions, particularly with healthcare providers [6].A five-phase process of evaluating an AI-CDSS for health-
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