Medicine
Sanaz Yasrebinia; Mansour Rezaei
Abstract
Introduction: As the global community strives to ensure the health and well-being of mothers and newborns, AI emerges as a powerful ally in this noble endeavor. Through this systematic review, we seek to provide a comprehensive overview of the state of AI-driven mortality prediction, offering insights ...
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Introduction: As the global community strives to ensure the health and well-being of mothers and newborns, AI emerges as a powerful ally in this noble endeavor. Through this systematic review, we seek to provide a comprehensive overview of the state of AI-driven mortality prediction, offering insights that may shape the future of maternal and neonatal healthcare and bring us closer to the goal of ensuring safe pregnancies and healthy beginnings for all. Material and methods: We systematically reviewed the literature, restricting our search to publications from the past decade, and utilized the five major scientific databases as primary sources. Results: Out of the initial pool of 671 works, a total of 18 primary studies were meticulously chosen for in-depth analysis. It was evident that a predominant focus of these studies revolved around the prediction of neonatal mortality, predominantly employing machine learning models, with Random Forest being a popular choice. The top five frequently utilized features for model training encompassed birth weight, gestational age, the child's gender, Apgar score, and the mother's age. The development of predictive models for mitigating mortality during and after pregnancy holds immense potential, not only for enhancing the quality of life for mothers but also as a potent and cost-effective tool for reducing mortality rates. Conclusion: Drawing from the findings of this systematic review, it becomes evident that substantial scientific endeavors have been undertaken in this domain. However, it is equally apparent that numerous unexplored research avenues and opportunities await further exploration within the research community.
Medicine
Melika Shojaei
Abstract
A B S T R A C TIntroduction: Since its release, ChatGPT has taken the world by storm with its utilization in various fields of life. This review's main goal CHATGPT is a CHATGPT developed by Open AI. This robot is trained with the help of artificial intelligence on a large amount of data to learn language ...
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A B S T R A C TIntroduction: Since its release, ChatGPT has taken the world by storm with its utilization in various fields of life. This review's main goal CHATGPT is a CHATGPT developed by Open AI. This robot is trained with the help of artificial intelligence on a large amount of data to learn language patterns. In the medical application of CHATGPT, the main topic is the conversation between doctors and patients. Method: In this study we searched in Scopus, Google scholar, PubMed databases and by searching with keywords such as "Nursing Services", "Importance of CHATGPT” and “Medical Education” during 2018-2024 to obtain articles related to the selected keywords. This innovation has the potential to automate daily tasks such as generating patient records or writing reports. By studying more than 45 articles about CHATGPT and the role of artificial intelligence in medicine, the results of this study showed that CHATGPT, with its very high potential, can play an important role in the field of interactions between humans and artificial intelligence and intelligent systems in the future. Results: The move towards the use of artificial intelligence in medicine, which is informed by patient information, can provide more personalized and clinically accurate answers to patients. Also, according to the findings of this research, it can be said: Automating administrative functions, scheduling visits, simplifying notes, checking insurance approvals for drugs, and other repetitive daily tasks can reduce the focus on administrative tasks and more time for providing medical care in to authorize the personnel. Conclusion: In this research, the researchers noticed the mistakes of CHATGPT chat bot in creating cancer treatment programs. According to these researchers, this chat bot had provided one-third of its answers in the field of designing treatment programs with incorrect information.
