Artificial Intelligence Medical Compendium

Background Accurate prediction of 90-day outcomes following ischemic stroke is crucial for optimizing clinical strategies. This study aimed to systematically review and meta-analyze studies using deep learning (DL) and hand-crafted radiomics (HCR) mo... read more 

Survival prediction frequently exhibit unstable and irreproducible performance, limiting their clinical applicability. Rather than proposing a new predictive architecture, this work presents a diagnostic investigation into the sources of this instabi... read more 

Background: Biological risk scores provide objective markers of bipolar disorder (BD) liability. Large-scale genome-wide association studies (GWASs) have led to polygenic risk scores (PRSs) that capture genetic liability to BD at the individual level... read more 

Using an analysis pipeline that combines machine learning-based feature selection with classical statistical modelling, we aimed to predict incident inflammatory bowel disease (IBD) and to identify candidate proteins associated with disease developme... read more 

Predicting implantation outcomes in vitro fertilization (IVF) is challenging because clinically relevant information becomes available at different points during treatment. In this study, we developed a stage-aware, leakage-conscious, and explainable... read more 

Background: Urine and plasma metabolites are attractive noninvasive cancer biomarkers, but most panels are developed and normalized using case-control labels and evaluated only at study prevalence. We assessed how well a four-metabolite panel separat... read more 

Venetoclax and azacitidine (ven-aza) is a standard of care therapy for newly diagnosed acute myeloid leukemia (AML) patients unfit to receive intensive therapy, and may also be appropriate for certain fit patients. Models predicting response to ven-a... read more 

As digital self-triage becomes more widely used, quality assurance requires evaluation of both emergency recognition and overall agreement with clinical judgement, including how these outcomes differ across generations of large language models. The B... read more 

The accuracy of machine learning interatomic potentials suffers from reference data that contain numerical noise. Often originating from unconverged or inconsistent electronic-structure calculations, this noise is challenging to identify. Existing mi... read more 

Peanut (Arachis hypogaea L.) contributes importantly to the global food supply, and its yield potential is closely linked to leaf photosynthetic efficiency. However, conventional measurement of leaf net-photosynthetic-rate (Leaf Pn) is time- and labo... read more