Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 13,501 to 13,510 of 211,153 articles

Data Integrity in Medical AI.

Studies in health technology and informatics
The reliability and ethical use of artificial intelligence (AI) in medicine fundamentally depend on the integrity of underlying data. This peer-review-style report examines data integrity - covering data quality, noise, bias, and data collection desi... read more 

Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.

Studies in health technology and informatics
Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and therapeutic decisions. We describe the harmonization of IMALIVE -a real-world HCC cohort- with the c... read more 

An Innovative 3D Slicer Plugin for Brain Images Annotation and Lesions Study.

Studies in health technology and informatics
This article presents the design, development, and validation of a plugin that integrates deep learning models into the open-source 3D Slicer framework for the visualization and analysis of biomedical images. The system implements a convolutional neu... read more 

Test-Time Data Quality Degradation in Clinical ML: A Systematic Robustness Analysis on MIMIC-IV.

Studies in health technology and informatics
We conducted a controlled experimental study using the MIMIC-IV database to evaluate the robustness of five clinical classification models under data quality (DQ) degradation. Dimension-specific corruptions were applied exclusively at test time, whil... read more 

Integrating ARIMA and Deep Learning Models for Counterfactual Evaluation of Nirsevimab's Early Impact on RSV Infant Admissions.

Studies in health technology and informatics
This study presents a hybrid modelling framework that integrates classical statistical methods (ARIMA) with deep learning architectures to enhance longitudinal forecasting of hospital admissions. We conducted a preliminary, hospital-based evaluation ... read more 

A Data-Driven Visit Windowing Approach Applied to Cochlear Implant Follow-Up Data.

Studies in health technology and informatics
INTRODUCTION: Cochlear implant (CI) patients require regular follow-up care over multiple visits to achieve optimal hearing outcomes. In clinical routine, however, visits rarely occur on the exact scheduled dates and cluster around them, reducing rep... read more 

Prediction of Early Functional Outcome After Acute Ischemic Stroke Using Real-World Clinical Data in Vietnam and Indonesia: Retrospective Cohort Study.

Studies in health technology and informatics
Accurate prediction of early functional outcome after acute ischemic stroke is critical for clinical decision-making. This retrospective cohort study developed and externally validated a machine learning model using routine clinical data from two set... read more 

Feasibility of Causality-Aware Machine Learning for Drug Safety on OMOP-CDM.

Studies in health technology and informatics
Regulators increasingly view real-world healthcare data and potential use of Artificial Intelligence (AI) approaches as vital for pharmacovigilance (PV). Large European and global initiatives have invested in the development of scalable pharmacovigil... read more 

Explainable ML for Predicting Vision Loss in Pediatric NF-1 Patients Using OCT Data.

Studies in health technology and informatics
We applied explainable machine learning to optical coherence tomography (OCT) data from 168 pediatric NF-1 patients to predict vision abnormalities. The Balanced Random Forest model achieved an AUC-ROC of 0.82. SHapley Additive exPlanations (SHAP) an... read more 

Supervised Learning Provides Small but Consistent Improvements to Clustering when Predicting Chronic Pain Outcomes Following Treatment.

Studies in health technology and informatics
Using registry and questionnaire data from 47,235 chronic pain patients, we evaluated whether supervised learning outperforms clustering in predicting nine one-year outcomes. Supervised models showed small but consistent improvements (best RMSE 5.49 ... read more