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Artificial intelligence-based chatbots improve the efficiency of course orientation among medical students: a cross-sectional study.

BMC medical education
BACKGROUND: Large language models (LLMs) like ChatGPT offer new ways to improve academic and administrative workflows in medical education, particularly for students studying in a language that is not their native tongue. We set out to examine whethe...

Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.

BMC medical research methodology
BACKGROUND: Mild cognitive impairment (MCI) represents a transitional stage to Alzheimer's disease (AD), making progression prediction crucial for timely intervention. Predictive models integrating clinical, laboratory, and survival data can enhance ...

Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues.

Communications biology
Advanced 3D imaging techniques and image segmentation and classification methods can transform biomedical research by offering insights into the human brain cytoarchitecture under pathological conditions. We propose a comprehensive pipeline for 3D im...

Longitudinal development of sex differences in the limbic system is associated with age, puberty and mental health.

Communications biology
Sex differences in mental health become more evident across adolescence, with a two-fold increase of prevalence of mood disorders in females compared to males. The brain underpinnings remain understudied. Here, we investigated the role of age, pubert...

Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI.

Scientific reports
Accurate detection and classification of clinically significant prostate cancer remain critical challenges in medical imaging. Despite numerous studies focusing on feature extraction and classification, none have systematically assessed the impact of...

Development of a machine learning-based interface for insulin dependency prediction using clinical data.

Scientific reports
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...

Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.

Scientific reports
We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the ...

Introducing FREM: a decision-support approach for automated identification of individuals at high imminent fracture risk.

Archives of osteoporosis
UNLABELLED: This study used explainable AI to improve the Danish FREM model for predicting one-year risk of major osteoporotic fractures in over 2.4 million individuals aged ≥ 45. A DART boosting algorithm improved performance (AUC 0.77), with explai...

Learning to Program "Recycles" Preexisting Frontoparietal Population Codes of Logical Algorithms.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Computer programming is a cornerstone of modern society, yet little is known about how the human brain enables this recently invented cultural skill. According to the neural recycling hypothesis, cultural skills (e.g., reading, math) repurpose preexi...

Distinct Portions of Superior Temporal Sulcus Combine Auditory Representations with Different Visual Streams.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In humans, the superior temporal sulcus (STS) combines auditory and visual information. However, the extent to which it relies on visual information from the ventral or dorsal stream remains uncertain. To address this, we analyzed open-source functio...