Artificial Intelligence Medical Compendium

OBJECTIVE: To investigate whether and how dental educators use Artificial Intelligence (AI) to develop dental student assessments and to investigate their concerns and perceptions of AI in dental student learning. METHODS: A mixed methods study using... read more 

Traditional knowledge from medicinal plants receives substantial analysis through the field of ethnopharmacology in its role for drug discovery. AI technology now enhances ethnopharmacology practices through its sophisticated applications in data min... read more 

BACKGROUND AND OBJECTIVE: Accurate preoperative prediction of microvascular invasion (MVI) and survival risk is essential for personalized treatment in hepatocellular carcinoma (HCC). This study aimed to develop a multi-label deep learning framework ... read more 

BACKGROUND AND OBJECTIVE: CT-based attenuation correction (CT-AC) is commonly used in cardiac PET but introduces additional radiation exposure, which is particularly problematic for pediatrics or repeat scans. This study aims to generate attenuation-... read more 

The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which dis... read more 

The intensive use of nitrogen (N) fertilizers in agriculture has led to surpluses and widespread nitrate pollution in surface water and groundwater. Co-optimizing multiple management practices offers a pathway toward climate-smart and sustainable agr... read more 

INTRODUCTION: Psychoeducation is a key intervention in mood disorders. With the rise of artificial intelligence (AI) conversational agents, tools like ChatGPT are increasingly consulted by patients. Yet, empirical data on how AI-generated psychoeduca... read more 

Spread through air spaces (STAS) is a pathological feature associated with poor prognosis in non-small cell lung cancer (NSCLC). However, its diagnosis currently depends exclusively on postoperative histopathological examination, limiting its utility... read more 

Graph contrastive learning (GCL) has demonstrated its effectiveness in handling graph-structured data through self-supervised learning. However, its robustness against adversarial attacks remains questionable. While this topic is still under debate, ... read more 

Vast power grid infrastructure generates enormous volumes of inspection data from smart meters, unmanned aerial vehicle (UAV) patrols, and high-definition video monitoring. Meeting the demand for real-time analysis places stringent requirements on la... read more