In this article, the author reflects upon recent advancements in artificial intelligence (AI) technologies have led to profound discussions about AI's role in scientific research and education. AI technologies are now capable of summarizing and analyzing large volumes of data, creating presentations, and even drafting parts of scientific papers and grants with minimal human input. With their exponential ongoing advancements, they will become even more capable in doing these tasks. As boundaries between humans and AI blurs, serious questions arise about the future roles, responsibilities, and identity of academic researchers, intellectual property, publishing and grantsmanship, and equity in the world of AI potentiated research.
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http://dx.doi.org/10.1016/j.jaac.2024.03.013 | DOI Listing |
J Eval Clin Pract
February 2025
Akşehir Kadir Yallagöz Health School, Selcuk University, Konya, Türkiye.
Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.
Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction.
Sci Rep
December 2024
College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Vector-borne diseases pose a major worldwide health concern, impacting more than 1 billion people globally. Among various blood-feeding arthropods, mosquitoes stand out as the primary carriers of diseases significant in both medical and veterinary fields. Hence, comprehending their distinct role fulfilled by different mosquito types is crucial for efficiently addressing and enhancing control measures against mosquito-transmitted diseases.
View Article and Find Full Text PDFBehav Res Methods
December 2024
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, 30, 28040, Madrid, Spain.
This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Pharmacy Services, Vocational School of Health Services, Osmaniye Korkut Ata University, Osmaniye, Turkey.
In this work, artificial neural network coupled with multi-objective genetic algorithm (ANN-NSGA-II) has been used to develop a model and optimize the conditions for the extracting of the Mentha longifolia (L.) L. plant.
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