Medical Education

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specificall...

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation r...

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergr...

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo ...

For the busy clinical-imaging professional in an AI world: Gaining intuition about deep learning without math.

Medical diagnostics comprise recognizing patterns in images, tissue slides, and symptoms. Deep learning algorithms (DLs) are well suited to such tasks...

GSSCL: A framework for Graph Self-Supervised Curriculum Learning based on clustering label smoothing.

Graph self-supervised learning is an effective technique for learning common knowledge from unlabeled graph data through pretext tasks. To capture the...

Introducing high correlation and high quality instances for few-shot entity linking.

Entity linking, the process of connecting textual mentions in documents to canonical entities within a knowledge base, plays an integral role in a myr...

Deciphering Cathepsin K inhibitors: a combined QSAR, docking and MD simulation based machine learning approaches for drug design.

Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous syste...

A Survey of Perspectives and Educational Needs of Canadian Oncology Residents on Artificial Intelligence.

This study evaluated the perspectives and educational needs of Canadian oncology residents with regard to artificial intelligence (AI) in medicine, ex...

Explanatory argument extraction of correct answers in resident medical exams.

Developing technology to assist medical experts in their everyday decision-making is currently a hot topic in the field of Artificial Intelligence (AI...

A novel approach for heart disease prediction using hybridized AITHO algorithm and SANFIS classifier.

In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease...

Performance of a Novel Muscle Synergy Approach for Continuous Motion Estimation on Untrained Motion.

When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motio...

Does using artificial intelligence take the person out of personal statements? We can't tell.

BACKGROUND: Use of artificial intelligence to generate personal statements for residency is currently not permitted but is difficult to monitor. This ...

Large Language Model Use in Radiology Residency Applications: Unwelcomed but Inevitable.

OBJECTIVE: This study explores radiology program directors' perspectives on the impact of large language model (LLM) use among residency applicants to...

AI-based green technology implementation simulation for achieving carbon neutrality: exploring the role of subsidies and knowledge management.

This study investigates the role of green technology implementation (GTI) based on artificial intelligence (AI) at the household level to achieve carb...

Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study.

PURPOSE: Metabolic bariatric surgery (MBS) became integral to managing severe obesity. Understanding surgical risks associated with MBS is crucial. Di...

Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education.

BACKGROUND: Cardiovascular diseases present a significant challenge in clinical practice due to their sudden onset and rapid progression. The manageme...

Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.

The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a...

CORR® Curriculum-Orthopaedic Education: Artificial Intelligence and Surgical Assessment.

Model based deep learning method for focused ultrasound pathway scanning.

The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing ...