Artificial Intelligence Medical Compendium

With the rapid growth of scholarly literature, efficient artificial intelligence (AI)-aided abstract screening tools are becoming increasingly important. This study evaluated 10 different machine learning (ML) algorithms used in AI-aided screening to... read more 

Artificial intelligence (AI) has emerged as a powerful resource in healthcare for diagnosis and management. However, successful AI deployment depends on well-developed software for the preprocessing and analysis of digital medical images, along with ... read more 

Tidal cycles in estuaries dynamically regulate the composition and transformation of dissolved organic matter (DOM). However, conventional methods exhibit inadequate capacity to decipher the molecular transformation pathways, thereby limiting the und... read more 

The automated identification of retinal disorders is one of the most popular real-world computer vision applications related to ophthalmology. It has several advantages and can help ophthalmologists identify diseases more accurately. Technically, it ... read more 

OBJECTIVE: Nerve growth factor (NGF) inhibitors have been shown to provide pain relief in patients with osteoarthritis but are associated with adjudicated arthropathies (AAs). Exploratory analyses were performed to identify whether peripheral biomark... read more 

OBJECTIVES: This study systematically evaluates how existing AI frameworks in healthcare address intersectional bias across the AI lifecycle and explores the mitigation strategies proposed. STUDY DESIGN: Scoping review. METHODS: A scoping review was ... read more 

BACKGROUND: Named entity recognition (NER) is critical in natural language processing (NLP), particularly in the medical field, where accurate identification of entities, such as patient information and clinical events, is essential. Traditional NER ... read more 

Ketosis, a metabolic disorder in dairy cows, poses a risk of substantial economic losses, particularly when it progresses to clinical forms. Previous prediction models relied on smart farming data and binary classification, without incorporating risk... read more 

BACKGROUND: Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual treatment responses. Digital phenotyping-the passive collection of behavioral and physiological data thr... read more 

Single-cell sequencing (SCS) is a robust high-throughput sequencing technology used to measure RNA and DNA molecules, revealing hidden insights into the multi-ome profiles of humans, plants, animals, and microorganisms. Recently, advances in SCS tech... read more