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http://dx.doi.org/10.1136/bmjgh-2025-019663 | DOI Listing |
JMIR Res Protoc
September 2025
Department of Health Services Research & Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, United States.
Background: With the availability of more advanced and effective treatments, life expectancy has improved among patients with metastatic breast cancer (MBC), but this makes communication with their medical oncologist more complex. Some patients struggle to learn about their therapeutic options and to understand and articulate their preferences. Mobile health (mHealth) apps can enhance patient-provider communication, playing a crucial role in the diagnosis, treatment, quality of life, and outcomes for patients living with MBC.
View Article and Find Full Text PDFIntegr Comp Biol
September 2025
Department of Natural Sciences, Metropolitan State University, St Paul, MN 55105.
The pandemic-driven shift to online learning necessitated a re-evaluation of traditional exams, revealing their limitations in fostering essential scientific skills and potentially disadvantaging some students. This paper presents sketchnoting, a visual note-taking method, as an authentic alternative assessment. By integrating scientific concepts, peer review, and graphical literacy, this approach aimed to cultivate skills like critical thinking and communication while assessing content.
View Article and Find Full Text PDFNutr Health
September 2025
Independent researcher, Rome, Italy.
Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDF