AIMC Topic: False Positive Reactions

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Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows.

Analytical chemistry
Metabolite identification is a pivotal step in drug discovery and development, enabling the comprehensive analysis of drug-derived compounds within biological systems. However, the complexity of liquid chromatography-mass spectrometry data often resu...

A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
BACKGROUND: Antenatal screening for Trisomy 21 (T21) in the UK is performed primarily in the first trimester. Nuchal Translucency (NT), gestational age, Free β-HCG and PAPP-A are used in combination, creating the 'combined' test. Multivariate Gaussia...

False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions.

Scientific reports
As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. One among such risks is the lack of a mitigation strategy agai...

Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial.

Journal of dentistry
OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic rad...

Lung nodule detection using a multi-scale convolutional neural network and global channel spatial attention mechanisms.

Scientific reports
Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges,...

Using aggregated AI detector outcomes to eliminate false positives in STEM-student writing.

Advances in physiology education
Generative artificial intelligence (AI) large language models have become sufficiently accessible and user-friendly to assist students with course work, studying tactics, and written communication. AI-generated writing is almost indistinguishable fro...

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

La Radiologia medica
PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF...

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

PloS one
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, tr...

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

Prehospital emergency care
OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention (PPCI). Many emergency medical services (EMS) activate catheterization labs to reduce time to PPCI,...

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.

Topics in magnetic resonance imaging : TMRI
OBJECTIVES: The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for t...