Artificial Intelligence Medical Compendium

Ultrasound shear wave elastography offers a promising non-invasive method to measure cardiac stiffness, providing valuable insights into an individual's heart condition. The technique uses high-frame-rate imaging to capture shear wave propagation alo... read more 

Daily anatomical changes during radiotherapy demand accurate imaging and segmentation to support adaptive treatment. We propose Simultaneous Evidential Network for Synthesis and SEgmentation (SENSSE), a deep learning framework that jointly performs s... read more 

IMPORTANCE: Despite growing interest in the use of artificial intelligence (AI) in medicine, empirical evidence on how clinicians use these tools is limited. OBJECTIVE: To characterize clinician use of an AI-enabled clinical information platform, the... read more 

Artificial intelligence (AI) is rapidly transforming healthcare. Yet people with disabilities (PWD), representing 16% of the global population, remain disproportionately excluded from these advances. This exclusion is driven by algorithmic bias, non-... read more 

INTRODUCTION: Detection of prevalent mild cognitive impairment (MCI) is difficult. We tested whether adding gait features to clinical variables improved prevalent MCI classification among older adults. METHODS: This cross-sectional analysis included ... read more 

This study aims to systematically explore the potential molecular mechanism of Roburic Acid (RBA) in the treatment of Anaplastic Thyroid Carcinoma (ATC) through the approaches of network pharmacology, machine learning and in vitro experiments. Public... read more 

BACKGROUND: Accurate prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) is crucial for staging and treatment planning. This study aimed to establish a standardized machine learning workflow based on routinely available cl... read more 

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