Lipidomic profiling generates vast datasets, making manual annotation and trend interpretation complex and time-intensive. The structural and chemical diversity of the lipidome further complicates the analysis. While existing tools support targeted l...
This study aimed to design a desktop application that implements machine learning algorithms to predict dental treatment time durations, assess the accuracy of the model, and assess its clinical efficiency. The Python programming language was used to...
This mixed-methods study evaluated clinicians' user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying ...
BACKGROUND: Clinical care globally faces increasing strain due to escalating documentation demands. Simultaneously, technological solutions for clinical workflows, particularly inpatient handovers, are being developed to alleviate workforce stress. H...
BACKGROUND: Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for succes...
BACKGROUND: Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.
BACKGROUND: The practice of medicine has evolved significantly during the past decade, with the emergence of Machine Learning (ML) that offers the opportunity of personalized patient-tailored care. However, ML models still face some challenges when c...
The implementation of artificial intelligence (AI) solutions in hospital logistics is becoming increasingly important for optimizing clinical processes. This paper examines the use of AI in hospital logistics and other operational areas of healthcare...
Deep Learning methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is...
Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, W...
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