Artificial Intelligence Medical Compendium

Despite their strengths, Aboriginal mothers experience significant health disparities, including perinatal mental health challenges influenced by social, historical, and cultural determinants. Routine risk screening informs follow-up care, yet conven... read more 

A constraint-first framework for future 'prompt-to-genome' AI across Bacteria, Archaea, Eukaryota, and viruses is proposed. Genome-scale models can now generate long DNA sequences, and experimentally viable genome-scale designs have begun to appear, ... read more 

The rapid integration of artificial intelligence (AI) in healthcare requires nurses to possess strong digital competence, advanced digital literacy, and readiness to engage with AI technologies. Despite this need, the ways in which digital competence... read more 

Screening for gastric cancer (GC) is critical for improving patient survival, yet current clinical methods suffer from invasiveness and limited sensitivity. The aim of this study is to develop a machine learning model for the non-invasive diagnosis o... read more 

The rapid proliferation of Internet of Things (IoT) devices has intensified network security challenges. Simultaneously, growing computational demands raise critical concerns about energy consumption and environmental sustainability. This paper propo... read more 

Crop diseases threaten agricultural productivity, creating a need for recognition models that combine accuracy, robustness, and computational efficiency. Existing CNN-, Transformer-, and state-space-based approaches can weaken fine-grained lesion edg... read more 

The use of Deep learning (DL) for recognition of wild animals has become an important development in wildlife monitoring. The advances will include the use of multimodal data for improved accuracy and development of lightweight models for use in edge... read more 

To address the heavy reliance of deep learning models on manually labeled data in ECG classification, we need to tackle two major limitations. First, sample sizes are often limited in clinical settings, making data annotation costly and impractical. ... read more 

Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to highly reactive states remain elusive. This stems from a fundamental computational bottleneck... read more 

Recent research and advances in LLMs have led to widespread concern that AI agents could pose as human online survey-takers. However, it remains unclear how prevalent AI agents are on survey research platforms and how to effectively detect AI agents.... read more