Surveys

Risk-prediction models for postpartum depression: Assessment of risk of bias using PROBAST+AI.

OBJECTIVE: The postpartum depression (PPD) risk prediction model is an effective risk stratification tool and is expected to play a significant role in the early detection and intervention of PPD. This study aims to summarize the existing evidence on PPD risk prediction models and provide references for their development, validation, and clinical application. METHODS: We searched PubMed, EMBASE, a...

Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms.

Approaching problems with data-driven models often requires reliable uncertainty estimates. Bayesian neural networks can offer these for deep learning models. Without the knowledge to set informative prior distributions, sampling methods such as Hamiltonian Monte Carlo are a robust choice. However, these come with prohibitive computational costs. We study two ways to incorporate computationally li...

Blockchain-driven trust management and AI computing for sensor networks optimization.

The Internet of Things (IoT) and emerging technologies have converged to drive the remarkable development of intelligent systems. The interconnection ...

Limited validity of an AI-powered app for dietary assessment in females with obesity.

Artificial intelligence (AI) is transforming dietary assessment, yet few tools have been clinically validated against physiological reference methods....

Can AI chatbots guide patients and physicians about neck pain? A reliability and readability comparison of ChatGPT-4 and Gemini.

BackgroundArtificial intelligence (AI)-based chatbots are increasingly used as sources of medical information. Given the high prevalence of neck pain ...

Artificial Intelligence in Anatomic Education: Educational Utility, Safety Boundaries, and Implementation Considerations.

Anatomic education is central to medical training and underpins safe clinical and surgical practice. Despite its importance, traditional methods of an...

Uncertainty Exploration of Deep Learning Enabled Fast Multidimensional NMR Spectroscopy of Proteins.

Deep learning (DL) has shown great promise in accelerating multidimensional NMR spectroscopy with high-accuracy reconstruction. Nevertheless, its prac...

A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.

Cutaneous leishmaniasis (CL) is a neglected tropical and zoonotic disease affecting both human and animal health, for which microscopic examination of...

Joint effects of physical activity and diet quality on depressive symptoms and all-cause mortality in adults with diabetes: NHANES 2007-2018.

Depressive symptoms are common among adults with diabetes and are associated with adverse clinical outcomes, including mortality. Evidence from genera...

Explainable machine learning for sustainable education: Predicting college students' reliance on generative artificial intelligence.

As generative artificial intelligence (GenAI) becomes increasingly embedded in education, students are using it to support knowledge acquisition, expl...

CT acquisition protocols in lung cancer screening: implications for guideline development from a worldwide survey.

OBJECTIVES: To assess the currently applied CT image acquisition protocols in lung cancer screening (LCS) and thereby fill a knowledge gap to support ...

From Channel Bias to Feature Redundancy: Uncovering the "Less Is More" Principle in Few-Shot Learning.

Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. ...

Patient Perceptions of Artificial Intelligence in Diabetes Self-Management: Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in chronic disease management, including diabetes, where it has the potential to supp...

High-Dimensional Effect Heterogeneity of Tooth Loss on Mortality.

Tooth loss has been associated with increased mortality risk in older adults. This study examined heterogeneity in the association between tooth loss ...

Utilizing stimulation-evoked hemodynamic activity to predict antidepressant response to intermittent theta-burst stimulation in adults with major depression.

BACKGROUND: The instantaneous neural response to prefrontal theta burst stimulation (TBS) may serve as predictive marker for antidepressant treatment ...

Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.

The present study proposes a methodology to emulate an interventional trial by employing machine-learning (ML) models. A maqui-citrus beverage is used...

Machine learning and the role of the vaginal and fecal microbiome in miscarriage: a matched case-control study.

Miscarriage occurs in approximately 15% of all pregnancies, and recent studies have suggested a potential role of the microbiome. A nested case-contro...

A selective machine learning algorithm for severe periodontitis labeling from questionnaire data.

Epidemiological cohorts often collect self-reported oral health (SROH) questionnaires but lack clinical periodontal measurements. We developed a selec...

Integrating Generative AI into Nomograms for Breast Cancer Nodal Risk Predictions.

BACKGROUND: Nomograms predicting the likelihood of sentinel lymph node (SLN) metastasis in early-stage breast cancer can aid surgical decision-making ...

Performance of large language models conducting systematic review tasks in prosthodontics.

STATEMENT OF PROBLEM: Systematic reviews (SRs) are time-consuming and resource-intensive processes. Whether large language models (LLMs) can improve t...