AIMC Topic: Reproducibility of Results

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Artificial intelligence to automate assessment of ocular and periocular measurements.

European journal of ophthalmology
PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.

Consistent and effective method to define the mouse estrous cycle stage by a deep learning-based model.

The Journal of endocrinology
The mouse estrous cycle is divided into four stages: proestrus (P), estrus (E), metestrus (M), and diestrus (D). The estrous cycle affects reproductive hormone levels in a wide variety of tissues. Therefore, to obtain reliable results from female mic...

Pseudo-class part prototype networks for interpretable breast cancer classification.

Scientific reports
Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key...

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.

Nature communications
Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techn...

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

JMIR mHealth and uHealth
The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT pose...

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Environmental science and pollution research international
Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictio...

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
BACKGROUND: The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to...

Bias-reduced neural networks for parameter estimation in quantitative MRI.

Magnetic resonance in medicine
PURPOSE: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability.

Sensors (Basel, Switzerland)
Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, whi...

Is ChatGPT reliable and accurate in answering pharmacotherapy-related inquiries in both Turkish and English?

Currents in pharmacy teaching & learning
INTRODUCTION: Artificial intelligence (AI), particularly ChatGPT, is becoming more and more prevalent in the healthcare field for tasks such as disease diagnosis and medical record analysis. The objective of this study is to evaluate the proficiency ...