Breakthroughs in Smart Nursing With Generative AI

Generative artificial intelligence (GenAI) transforms healthcare and nursing through new technology. Smart nursing powered by GenAI reshapes how nurses deliver care, make clinical decisions, and manage daily workflows. From AI-assisted documentation and personalized care plans to real-time patient monitoring and predictive insights, these technologies help reduce workload while improving accuracy and patient outcomes. As healthcare systems face growing demands and workforce challenges, GenAI offers innovative solutions that enhance efficiency, support decision-making, and improve the quality of patient-centered care. Breakthroughs in Smart Nursing With Generative AI explores how GenAI reshapes modern healthcare, with a special emphasis on nursing innovation and intelligent care systems. It examines the real-world applications in hospitals, clinics, and community health settings, highlighting how GenAI can enhance clinical decision-making, improve patient monitoring, simplify documentation, and personalize care delivery. This book covers topics such as clinical science, hospital management, and medical technologies, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists.

Suha Khalil Assayed holds a Ph.D. in Computer Science with a specialization in Artificial Intelligence. She is currently advancing her research as a Postdoctoral Researcher at the National Kaohsiung University of Science and Technology (NKUST), where she leads projects applying Natural Language Processing to healthcare, including disease prediction and AI-driven virtual health assistants. Dr. Assayed’s research focuses on developing AI-driven chatbots and NLP-based systems to enhance both education and healthcare. She also serves as a keynote speaker on AI in healthcare, sharing insights on the role of intelligent systems in transforming medical practice and learning. With over 30 publications in reputable journals and international conferences, Dr. Assayed has made significant contributions to advancing the integration of deep learning, speech–text interaction, and language modeling across the health and education sectors.

Maha Mohammed Wahbi Atout is an Associate Professor of Pediatric Nursing at Philadelphia University, Jordan. She currently serves as Head of the International Projects Office, where she leads international collaborations, Erasmus+ projects, and institutional partnerships. Dr. Atout is also actively involved in quality assurance and academic accreditation processes, with extensive experience in curriculum development, research supervision, and international mobility programs. Her academic and professional interests focus on pediatric nursing, health education, and strengthening global academic cooperation.

Professor Chin-Shiuh Shieh is a distinguished academic and researcher in the field of electronic engineering and computer science, currently serving as a Professor in the Department of Electronic Engineering at National Kaohsiung University of Science and Technology (NKUST), Taiwan. He received his Ph.D. in Computer Science and Engineering from the National Sun Yat-sen University, Taiwan, and has since dedicated his career to pioneering research and teaching in areas including computer networking, wireless communication, information security, computational intelligence, embedded systems, and medical informatics. With a career spanning several decades, Professor Shieh has made significant contributions to both academia and industry. In the past five years alone, he has authored or co-authored 28 journal papers, of which 24 are indexed in SCI (Science Citation Index), and 80 conference papers, encompassing 26 international and 54 domestic conferences. His editorial acumen is evident in his role as editor for two international academic conference proceedings. Professor Shieh has been actively engaged in various research initiatives, participating in 10 Ministry of Science and Technology (MOST) projects, leading five of them. He has also been involved in three Ministry of Education projects, one of which he led, and a campus research project. Furthermore, his strong industry collaboration is reflected in 10 industry-academia cooperation projects, eight of which he led. He has also contributed to one technology transfer authorization. As a recognized figure in the global research community, Professor Shieh is affiliated with four professional societies, and serves as a reviewer for 106 academic journals. He has participated as a committee member for 21 journals and 175 international conferences, underscoring his influence and expertise in his fields of specialization. Among his recent notable publications is the 2024 SCI-indexed paper titled “Open-set recognition in unknown DDoS attacks detection with reciprocal points learning” published in IEEE Access, which highlights his innovative work in cybersecurity and artificial intelligence. He has also played key editorial roles in conferences such as the International Conference on Genetic and Evolutionary Computing (ICGEC) and Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP). His work has earned multiple accolades including Best Paper Awards and Excellent Paper Awards in international conferences. He has co-developed cutting-edge methods in machine learning, optimization algorithms, and adversarial defense mechanisms, particularly in the domain of cybersecurity threats such as DDoS attacks. His academic journey, prolific publication record, leadership in research projects, and commitment to interdisciplinary collaboration have established him as a leading voice in advancing electronic engineering and computing sciences in Taiwan and beyond.