Artificial Intelligence Medical Compendium

Vestibular disorders are common, heterogeneous, and diagnostically challenging. Nystagmus is an important sign in vestibular assessment, but its interpretation can depend on clinician expertise and recording quality. This structured narrative review ... read more 

OBJECTIVE: This commentary examines gesture-based rehabilitation (GBR) as an assistive technology within an ecosystem for survivors of infectious diseases with neurological disabilities. It describes the roles of computer vision, markerless motion ca... read more 

Hepatitis screening is clinically challenging. Conventional serological and biochemical assays rely on cumbersome procedures, require long detection times, and lack sufficient sensitivity to identify pathological changes. Surface-enhanced Raman scatt... read more 

BackgroundAs artificial intelligence (AI) becomes increasingly integrated into the workplace, an important question concerns how employees respond when technological change is perceived as threatening rather than beneficial. Prior studies have examin... read more 

BackgroundThis study investigates the complex relationship between perceptions of artificial intelligence (AI) opportunities and well-being, exploring the underlying psychological processes. Using conservation of resources theory, we examine how empl... read more 

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep-learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficultie... read more 

BACKGROUND: The Balance Error Scoring System (BESS) is widely used to assess static postural control in sports medicine and rehabilitation. However, conventional real-time scoring is observer dependent and may be influenced by rater experience, viewi... read more 

BACKGROUND: Surgical site infection (SSI) is an important complication after spine surgery, with a reported incidence of approximately 1%-4% and a substantial associated healthcare burden. Early identification of high-risk patients remains difficult.... read more 

AIMS: Traditional screening based on body mass index (BMI) fails to identify the metabolically obese normal-weight (MONW) phenotype. We aimed to develop and temporally validate an interpretable machine learning framework utilizing non-invasive surrog... read more 

BACKGROUND: Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or programmed death-ligand 1 (PD-L1) have transformed the treatment of non-small cell lung cancer (NSCLC). However, diabetes-induced immune dysfunction m... read more