Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On 2026-09-08, a peer-reviewed study reported development of an integrative clinical-molecular model for glioma malignancy grading using 400 patients from a single-center retrospective cohort. Using LASSO and machine learning, the authors built a Random Forest classifier based on seven predictors including age, KPS, tumor diameter, inflammatory ratios, IDH status and Ki-67, achieving AUC 0.864 in training and 0.820 in validation.
A systematic review covering January 2013 to June 2025 examined non-contact and AI-assisted neonatal heart rate monitoring versus conventional ECG. It found a progressive shift toward camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems that showed strong correlation with ECG, rapid acquisition, and better robustness to motion and lighting.
This peer-reviewed review examines how artificial intelligence could change onco-critical care, focusing on early sepsis detection and dynamic mortality prediction for critically ill cancer patients whose complex physiology limits traditional scores like APACHE II and SOFA.
Published September 9, 2026, this peer-reviewed review outlines a pragmatic framework for physiology-guided mechanical ventilation that combines advanced monitoring, proportional assist modes, and bounded automation. It notes that automation and artificial intelligence are increasingly used for waveform analysis, asynchrony detection, and weaning prediction within predefined safety limits.
Published July 6 2026, this law review article argues that Section 230 and the First Amendment do not categorically immunize digital platforms for harms caused by their own design choices. It proposes a typology separating direct primary harms from design decisions from secondary harms from user content and tertiary harms, focusing on personal-data-driven algorithmic targeting and dark patterns like infinite scrolling.
A comparative study published August 7, 2026 evaluated ChatGPT-5.0 against five supervised machine learning algorithms for orthodontic extraction decisions. Using 520 cases (42.88% extraction, 57.12% non-extraction) and 23 clinical, cephalometric and photographic variables, with expert consensus as reference, ChatGPT-5.0 achieved 75.77% accuracy and 76.68% sensitivity under 5-fold cross-validation, compared to 78.08% accuracy for XGBoost.
Researchers developed and validated an interpretable machine learning model to predict 5-year all-cause mortality in non-dialysis chronic kidney disease using data from 1,858 patients in the KNOW-CKD prospective cohort, with 94 deaths observed. The CatBoost model achieved AUC 0.813 versus 0.747 for logistic regression, and a simplified version using age, eGFR, albumin, urine protein-to-creatinine ratio, and total calcium retained AUC 0.795 in an external cohort of 348 patients.
The peer-reviewed article examines responsibility gaps when AI tools in healthcare cause patient harm. It notes that traditional models struggle because decisions are spread across clinicians, developers, institutions and the AI systems themselves.
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