Artificial Intelligence Medical Compendium

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 

The challenging transformation of medicine in the twenty-first century, involving robotics, artificial intelligence (AI) and the digitalization of daily work routines, is hampered by bureaucratic hurdles, a shortage of skilled workers and socioeconom... read more 

Parkinson disease is an ongoing neurodegenerative disorder, and both motor and non-motor symptoms are involved with the existing therapies having minimal effects on the disease but only symptom alleviation. The goal of this systematic literature revi... read more 

Epilepsy is one of the most heterogeneous and unpredictable neurological disorders, characterized by complex brain dynamics, significant interindividual variability, and a therapeutic response that is often difficult to predict. Despite major advance... read more 

OBJECTIVES: To develop and validate a phase-adaptive pipeline for automated whole-liver, parenchyma-based quantification of signal intensity-derived liver indices from gadoxetic acid-enhanced MRI without inter-phase co-registration. MATERIALS AND MET... read more 

This systematic review evaluates evidence on artificial intelligence (AI) and machine learning (ML) models for prediction and diagnosis of fetal growth restriction (FGR). The authors searched PubMed, Scopus, Embase, Web of Science, and Google Scholar... read more