98%
921
2 minutes
20
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1038/s41559-023-02063-3 | DOI Listing |
Nurs Rep
July 2025
Department of Community Health Nursing, College of Nursing, Jouf University, Sakakah 72388, Saudi Arabia.
Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care.
View Article and Find Full Text PDFJ Med Internet Res
August 2025
School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Background: Generative artificial intelligence (AI) for tabular synthetic data generation (SDG) has significant potential to accelerate health care research and innovation. A critical limitation of generative AI, however, is hallucinations. Although this has been commonly observed in text-generating models, it may also occur in tabular SDG.
View Article and Find Full Text PDFJ Gen Intern Med
August 2025
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Primary care is the cornerstone of every patient's health journey. Given its central role in the delivery of medical care, primary care is crucial in the integration of artificial intelligence (AI) into healthcare. Generative AI has the potential to augment primary care workflows to achieve the quintuple aim.
View Article and Find Full Text PDFComput Biol Med
September 2025
School of Computer Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si, 28644, Chungbuk, Republic of Korea. Electronic address:
Childhood obesity is a growing global health concern as it is correlated with an increased risk of adult-onset and chronic diseases. Recent advances in digital healthcare technologies have enhanced the efficiency of health data analysis and diagnosis, leading to increased interest in artificial intelligence (AI) applications in childhood obesity research; however, several challenges remain, such as data limitations, class-imbalance issues, and difficulties in model interpretability. This study addresses these challenges through a comprehensive framework that utilizes wearable devices for real-time lifestyle data collection and employs Wasserstein generative adversarial networks (WGANs) to address data imbalance concerns.
View Article and Find Full Text PDFMethodsX
December 2025
DSTI/Mintek Nanotechnology Innovation Centre, Advanced Materials Division, Mintek, 200 Malibongwe Drive, Randburg 2194, South Africa.
The rise of artificial intelligence (AI) applications in the modern industry has been shown to boost productivity in many sectors such as manufacturing, mining, finance, marketing, pharmacy, textiles, and a few more industries. AI is a neural network system that is wired to learn by interacting with humans and by repetition of tasks. A sub-category of AI common to many ordinary citizens is the Chat Generative Pre-training Transformer (ChatGPT) a program of OpenAI Global, LLC.
View Article and Find Full Text PDF