Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
A cross-sectional study of 8285 male high school students from the 2023 Youth Risk Behavior Surveillance System used 36 demographic, behavioral, and psychosocial predictors to model lifetime marijuana use, reported by 28.4% of participants. After benchmarking 17 algorithms, an optimized logistic regression model with SHAP and LIME explanations achieved AUC 0.9034 and accuracy 0.8582.
This peer-reviewed educational perspective from August 2026 examined how organ-at-risk contouring is taught in an Australian undergraduate radiation therapy program as AI auto-contouring enters clinical workflows. The authors reviewed curriculum scope and technologies and examined students' preferred methods, confidence across OARs, and perceived factors affecting quality.
Researchers derived and tested the triglyceride-glucose frailty index in 2230 MIMIC-IV adults with KDIGO-defined AKI, examining associations with ICU, in-hospital, 28-day, 90-day and 365-day mortality, identifying two consensus phenotypes, and evaluating 12 prediction algorithms with SHAP and LIME interpretation and external validation in 1831 eICU patients.
On 2026-08-22, a peer-reviewed study reported testing how three different allometric reference datasets affect Sentinel-2-based aboveground biomass mapping in Pinus brutia. Using 112 field plots and CART as primary model with Random Forest as robustness check, authors mapped biomass over 13,687 ha and compared totals to forest management plan data.
This 2026 RadioGraphics review examines how artificial intelligence, especially generative models, could be applied across radiology education from curriculum planning to implementation and evaluation using Harden's 10-step framework.
A systematic review and meta-analysis up to September 2025 synthesized 83 studies involving at least 136,840 patients to evaluate machine learning models predicting hematoma expansion, poor functional outcome, and mortality after spontaneous intracerebral hemorrhage. Pooled analyses found that models combining clinical and radiomics features achieved the highest discrimination, with C-indexes of 0.822, 0.850, and 0.860 respectively, largely from internal validation sets.
Investigators developed and externally validated a machine learning model to stratify risk of postoperative hydrocephalus after posterior fossa tumor resection using data from 1,073 patients treated at five tertiary centers from 2013 to 2024. After screening 30 variables, they built a three-variable preoperative model using Evans index, tumor-fourth ventricle relationship, and preoperative CSF diversion status, with SVM showing AUC 0.877 in the external cohort.
By July 2026, a narrative review of 127 peer-reviewed studies from 2015-2026 examined how AI and ML are being used in pharmaceutical research and healthcare. The review found the technologies enable large-scale biomedical data analysis and data-driven decision-making while simultaneously introducing interconnected ethical challenges.
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