Identifying and Reporting Dependent Adult abuse

DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology.

DECIDE-AI is a new, stage-specific reporting guideline for the early and live clinical evaluation of decision-support systems based on artificial intelligence (AI). It answers a need for more attention to the human factors influencing clinical AI performance and more transparent reporting of clinical studies investigating AI systems. Given the rapid expansion of AI systems and the concentration of...

Natural language processing in narrative breast radiology reporting in University Malaya Medical Centre.

Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstr...

[Literacy for Appropriate Use of Medical Big Data and Artificial Intelligence].

Recent developments have enabled daily accumulated medical information to be converted into medical big data, and new evidence is expected to be creat...

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models.

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as ge...

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learnin...

Deep Learning and Explainable Artificial Intelligence to Predict Patients' Choice of Hospital Levels in Urban and Rural Areas.

Maldistribution of healthcare resources among urban and rural areas is a significant challenge worldwide. People living in rural areas may have limite...

Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review.

Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We sys...

Promoting the Importance of Recall Visits Among Dental Patients in India Using a Semi-Autonomous AI System.

In many developing countries like India, there is a widespread lack of general awareness about the importance of good oral health, which causes dental...

BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions.

MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new d...

Gell-Mann-Low Criticality in Neural Networks.

Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits ...

Systematic Review and Meta-Analysis of Pediatric Robot-Assisted Laparoscopic Pyeloplasty.

To perform a systematic review (SR) and meta-analysis (MA) of outcomes of robot-assisted laparoscopic pyeloplasty (RALP) for ureteropelvic junction (...

Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility.

Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity ...

Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We ev...

A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports.

BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting s...

Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for v...

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the ...

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved ...

Artificial Intelligence Enabling Radiology Reporting.

The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies...

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garn...

Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare.

High-quality research is essential in guiding evidence-based care, and should be reported in a way that is reproducible, transparent and where appropr...