We’re Ranking AI Models All Wrong And Why Human Capability Should Drive The Benchmark

At a moment when the AI industry is obsessed with bigger models and higher scores, Professor Ganna Pogrebna opened the ...
The Lancet

CARDBiomedBench: a benchmark for evaluating the performance of large language models in biomedical research

Although large language models (LLMs) have the potential to transform biomedical research, their ability to reason accurately across complex, data-rich domains remains unproven. To address this ...

Bay Area lab’s new model for forecasting wildfires could change how they’re fought — and save lives

Multi-ignition wildfires are a dangerous challenge for firefighters. But a new model from the Lawrence Livermore National ...
Psychiatry Advisor

Accuracy of Machine Learning in Predicting CVD Risk in Bipolar Disease

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...
Ophthalmology Advisor

ChatGPT Update May Identify Glaucoma From Clinical Images

The ChatGPT o1 Pro can accurately identify glaucoma from visual field and optical coherence tomography data, a study shows.
News Medical

New AI models detect dementia with high accuracy using EEG signals

Researchers at örebro University have developed two new AI models that can analyze the brain's electrical activity and accurately distinguish between healthy individuals and patients with dementia, ...
IEEE

A Hybrid CNN-Logistic Regression Model for Accurate Classification of Casting Defects

Abstract: The abstract is an imperfect defect detection model meant to classify various defects of castings. It presents an excellent precision, recall, and $\mathbf{F 1}$-score of six classes of ...
Frontiers

ConvAttentionNet: a high-performance model for efficient and accurate PolSAR data classification

This paper presents ConvAttentionNet, a lightweight and high performing deep learning model developed for accurate and efficient classification of Polarimetric Synthetic Aperture Radar (PolSAR) ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...
IEEE

Analysis of Ansätze Expressibility and Complexity and Their Impact on Classification Accuracy Using QNN and QLSTM

Abstract: Quantum Neural Networks (QNN) and Quantum Long Short-Term Memory (QLSTM) models are emerging as powerful tools in quantum machine learning. The effectiveness of these models is largely ...
Scientific Research Publishing

Dreiseitl, S. and Ohno-Machado, L. (2002) Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review. Journal of Biomedical Informatics, 35 ...

ABSTRACT: A degenerative neurological condition called Parkinson disease (PD) that evolves progressively, making detection difficult. A neurologist requires a clear healthcare history from the ...