AI Medical Compendium Journal:
Artificial intelligence in medicine

Showing 61 to 70 of 596 articles

On the role of artificial intelligence in analysing oocytes during in vitro fertilisation procedures.

Artificial intelligence in medicine
Nowadays, the most adopted technique to address infertility problems is in vitro fertilisation (IVF). However, its success rate is limited, and the associated procedures, known as assisted reproduction technology (ART), suffer from a lack of objectiv...

Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.

Artificial intelligence in medicine
BACKGROUND: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they suffer...

Explanatory argument extraction of correct answers in resident medical exams.

Artificial intelligence in medicine
Developing technology to assist medical experts in their everyday decision-making is currently a hot topic in the field of Artificial Intelligence (AI). This is specially true within the framework of Evidence-Based Medicine (EBM), where the aim is to...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Artificial intelligence in medicine
Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that fusing...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

Artificial intelligence in medicine
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease ...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

Artificial intelligence in medicine
BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted...

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug-target binding affinity prediction.

Artificial intelligence in medicine
Accurate prediction of drug-target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds...

RECOMED: A comprehensive pharmaceutical recommendation system.

Artificial intelligence in medicine
OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals.

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

Artificial intelligence in medicine
The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic examinat...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Artificial intelligence in medicine
Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review and stewardship of antimicrobials, and Post-Prescription Review (PPR) is currently the main tool used...