A published correspondence outlines how developing trustworthy artificial intelligence and fostering user confidence can modernize evidence synthesis across policy domains.
A multi-cohort transcriptomic study identified shared genetic biomarkers and immune mechanisms connecting atherosclerosis and lung adenocarcinoma, providing new models for ...
A pre-treatment Random Survival Forest model achieved strong internal performance in predicting local failure and its timing after Gamma Knife ...
Researchers developed an explainable machine learning model that accurately predicts the hydraulic permeability of sustainable fly ash pervious concrete while ...
Environmental scientists have established standardized evaluation criteria to rigorously assess and benchmark artificial intelligence systems used in product carbon footprinting.
A deep feature extraction framework enhances traditional classifiers to predict long-term loan delinquency transitions using tabular credit data with short ...
A machine learning and multi-criteria vulnerability study indicates that approximately ten percent of Toliara, Madagascar, faces high to very high ...
An interpretable random forest model trained on early emergency resuscitation data accurately predicted extubation failure in mechanically ventilated patients with ...
A mixed-methods investigation reveals that artificial intelligence accelerates routine engineering tasks across software development cycles while core creative decisions remain ...
A retrospective computational reanalysis of Gene Expression Omnibus microarray cohorts identified a candidate 13-gene transcriptomic model that achieved an out-of-fold ...
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