Artificial Intelligence Medical Compendium

OBJECTIVE: Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinic... read more 

read more 

OBJECTIVE: The increasing incidence of thyroid nodules (TN) raises concerns about overdiagnosis and overtreatment. This study evaluates the clinical and economic impact of KOIOS, an FDA-approved artificial intelligence (AI) tool for the management of... read more 

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with complicated structural and functional impairments. For decades now, chest computed tomography (CT) has been used to quantify various abnormalities related to COPD. More re... read more 

BACKGROUND: Disability progression despite disease-modifying therapy remains a major challenge in multiple sclerosis (MS). Artificial intelligence (AI) models exploiting magnetic resonance imaging (MRI) promise personalized prognostication, yet their... read more 

AIMS: To identify predictors for initiation of frontline doublet versus triplet therapy for transplant-ineligible newly diagnosed multiple myeloma. read more 

INTRODUCTION: Type 2 diabetes (T2D) continues to worsen globally alongside rise in obesity. Asymptomatic dysglycaemia, which precedes T2D, provides opportunities to identify those at risk and target prevention but prediabetes is highly variable. Not ... read more 

Accurate prediction of CNS relapse in DLBCL remains challenging despite existing models like IPI and CNS-IPI. This study aimed to develop a machine learning (ML)-based prognostic model. A retrospective cohort of 664 R-CHOP-treated DLBCL patients was ... read more 

The oxygen evolution reaction (OER) is a key bottleneck in clean energy conversion due to sluggish kinetics and high overpotentials. Transition metal single-atom catalysts offer great promise for OER optimization thanks to their high atomic efficienc... read more 

Inkjet printing of π-conjugated organic compounds enabled rapid, low-cost generation of training images for the image-based machine learning (ML) prediction of mixing ratios. ML models with mean absolute errors below 4% were achieved within hours, ev... read more