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Generative AI Applications in Nursing Practice and Clinical Decision-Making
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Author(s): P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), R. Parthiban (IFET College of Engineering, India), T. C. Manjunath (Rajarajeswari College of Engineering, Bengaluru, India), S. Saradha (Thiagarajar College of Engineering, India), Syed Muqthadar Ali (CVR College of Engineering, India)and K. K. Naresh (Padmashree Institute of Management and Sciences, India)
Copyright: 2026
Pages: 28
Source title:
Breakthroughs in Smart Nursing With Generative AI
Source Author(s)/Editor(s): Suha Khalil Assayed (National Kaohsiung University of Science and Technology, Taiwan), Maha Atout (Philadelphia University, Jordan)and Chin-Shiuh Shieh (National Kaohsiung University of Science and Technology, Taiwan)
DOI: 10.4018/979-8-3373-8247-0.ch002
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Abstract
Generative AI in Clinical Documentation represents one of the most transformative intersections of machine intelligence and healthcare delivery, reconfiguring how clinicians record, interpret, and operationalise patient information in increasingly complex care environments. As global health systems grapple with rising administrative burdens, workforce shortages, and the escalating complexity of multimorbidity, generative AI functions not merely as a digital scribe but as an adaptive, context-aware partner that enhances the cognitive and operational capacity of clinical teams. At its core, generative AI leverages large-scale language models capable of parsing unstructured inputs—such as physician-patient conversations, fragmented notes across disparate electronic health record (EHR) environments, and evolving diagnostic narratives—to produce coherent, accurate, and contextually aligned documentation in real time. This shift from manual charting to dynamic, AI-assisted documentation not only minimises clerical load but also reshapes.
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