AI Medical Compendium Journal:
Artificial intelligence in medicine

Showing 51 to 60 of 596 articles

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Artificial intelligence in medicine
Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference metho...

Generating synthetic clinical text with local large language models to identify misdiagnosed limb fractures in radiology reports.

Artificial intelligence in medicine
Large language models (LLMs) demonstrate impressive capabilities in generating human-like content and have much potential to improve the performance and efficiency of healthcare. An important application of LLMs is to generate synthetic clinical repo...

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Artificial intelligence in medicine
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...

Acoustical features as knee health biomarkers: A critical analysis.

Artificial intelligence in medicine
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical...

Exploring the effectiveness of instruction tuning in biomedical language processing.

Artificial intelligence in medicine
Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in general language tasks, their performance in domain-specific downstream tas...

Developing healthcare language model embedding spaces.

Artificial intelligence in medicine
Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by ta...

A systematic review of networks for prognostic prediction of health outcomes and diagnostic prediction of health conditions within Electronic Health Records.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Using graph theory, Electronic Health Records (EHRs) can be represented graphically to exploit the relational dependencies of the multiple information formats to improve Machine Learning (ML) prediction models. In this syste...

From pre-training to fine-tuning: An in-depth analysis of Large Language Models in the biomedical domain.

Artificial intelligence in medicine
In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Large Language Models (LLMs) within the biomedical domain, a field that poses unique challenges due to its complexity and the specialized nature of its da...

Intelligent wearable-assisted digital healthcare industry 5.0.

Artificial intelligence in medicine
The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearable devices and digital technologies, has revolutionized healthcare delivery and improved patient treatment. Integrating smart wearables such as fitnes...

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Artificial intelligence in medicine
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural networ...