AIMC Topic: Data Accuracy
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Data quality in crowdsourcing and spamming behavior detection.
Behavior research methods
Aug 8, 2025
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data to improve analysis performance and reduce biases in subsequent machin...
Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.
JMIR medical informatics
Jun 30, 2025
BACKGROUND: Evidence-based medicine combines scientific research, clinical expertise, and patient preferences to enhance the patient outcomes and improve health care quality. Clinical data are crucial in aligning medical decisions with evidence-based...
Smartphone eye-tracking with deep learning: Data quality and field testing.
Behavior research methods
Jun 18, 2025
Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking ha...
Enhancing data quality in medical concept normalization through large language models.
Journal of biomedical informatics
Apr 1, 2025
OBJECTIVE: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies on MCN primarily focus on mode...
Artificial intelligence in nursing: A journey from data to wisdom.
Nursing
Mar 24, 2025
Artificial intelligence (AI) can enhance nursing practice by assisting in clinical decisions, patient outcomes, and operational efficiencies. This article explores the role of AI in decision-making, data management, and task automation within the Dat...
Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.
Journal of biomedical informatics
Mar 21, 2025
OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.
Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.
Environment international
Feb 17, 2025
Concerns about the negative impacts of microplastics on human health are increasing in society, while exposure and risk assessments require high-quality, reliable data. Although quality assurance and -control (QA/QC) frameworks exist to evaluate the ...
Effects of Individual Research Practices on fNIRS Signal Quality and Latent Characteristics.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 20, 2024
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of ...
Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.
Journal of magnetic resonance imaging : JMRI
Jul 29, 2024
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature o...
Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.
Scientific reports
Jul 10, 2024
Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radi...
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