Medical Education

Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence.

The rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. image enhancement, segmentation and three-dimensional reconstruction) and artificial intelligence (AI) into the undergraduate MI curriculum. By examini...

Artificial Intelligence-Generated Writing in the ERAS Personal Statement: An Emerging Quandary for Post-graduate Medical Education.

OBJECTIVE: This study was designed to investigate if artificial intelligence (AI) detection software can determine the use of AI in personal statements for residency applications.

Artificial Intelligence Compared to Manual Selection of Prospective Surgical Residents.

BACKGROUND: Artificial Intelligence (AI) in the selection of residency program applicants is a new tool that is gaining traction, with the aim of scre...

Artificial Intelligence (AI)-Based simulators versus simulated patients in undergraduate programs: A protocol for a randomized controlled trial.

BACKGROUND: Healthcare simulation is critical for medical education, with traditional methods using simulated patients (SPs). Recent advances in artif...

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is i...

Application of artificial intelligence in medical education: A meta-ethnographic synthesis.

With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI...

Pharmacy students' perception and knowledge of chat-based artificial intelligence tools at a Nigerian University.

BACKGROUND: Chat-based Artificial Intelligence (AI) tools, such as ChatGPT, are becoming integral to various aspects of pharmacy education. However, t...

Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling.

Predicting the probability that a given location will be burnt by a wildfire is an important part of understanding the risk that wildfires pose and ho...

Pedagogy and generative artificial intelligence: Applying the PICRAT model to Google NotebookLM.

Healthcare educators (HPE) are challenged by rapid developments in Generative Artificial Intelligence (GenAI) tools. They need a standardized model to...

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 c...

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 simu...

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...

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...