Artificial Intelligence Medical Compendium

Acute myeloid leukaemia (AML) is an aggressive haematological malignancy with an incidence that increases with age and varies widely across regions owing to differences in risk factors, diagnostic capabilities, recording in cancer registries and acce... read more 

BACKGROUND: Differentiating between potato common scab, powdery scab, and the physiological disorder of enlarged corky lenticels is challenging due to their similar visual symptoms. To address this, we propose YOLOv8-ST, an enhanced deep learning mod... read more 

With the prevalent use of artificial intelligence (AI) in human resource (HR) activities, the ability to forecast employee behavior is an asset to firms and researchers. This study develops and tests a predictive model of Organizational Citizenship B... read more 

The human microbiome, encompassing microbial communities in the gut and breast tissue, has emerged as a critical modulator of breast cancer (BC) initiation, progression, and treatment response. This review synthesizes current evidence on the microbio... read more 

BACKGROUND: Binary classification of sex fails to capture the sex-related continuum of atrial fibrillation (AF) risk. OBJECTIVE: This study aimed to develop an artificial intelligence (AI)-enabled electrocardiography (ECG) model for sex prediction an... read more 

OBJECTIVES: Electromyography (EMG) is increasingly applied in oncology to monitor neuromuscular impairment, treatment toxicities, and rehabilitation outcomes. However, reliance on maximal voluntary contraction (MVC) normalization limits scalability, ... read more 

OBJECTIVES: Pain-fatigue-sleep disturbance symptom (PFS) cluster is the most common symptom cluster in patients with lung cancer following chemotherapy, which significantly impacts their quality of life. This study aims to develop and validate a mach... read more 

BACKGROUND: Integrating neuroimaging genetics data may enhance the diagnosis and understanding of adults with attention-deficit/hyperactivity disorder (ADHD). Here, we employed a population-based graph convolutional network (GCN) model based on multi... read more 

Reliable prediction of active pharmaceutical ingredient (API) solubility in complex solvent systems remains challenging. Existing models often sacrifice generalizability for accuracy or have limited applicability due to implementation complexity. The... read more 

Up to 70% of patients with autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus, report moderate to severe pain despite controlled inflammation, driving interest in self-managemen... read more