Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On 2026-08-12, a peer-reviewed study described CURL-AID, a fully automated framework that segments the posterior mitral annulus and left ventricular wall on parasternal long-axis echocardiograms, tracks tissue motion, and classifies posterior systolic curling using nine kinematic features in 100 retrospectively analyzed patients.
On August 12, 2026, a review in the International Journal of Toxicology summarized AI and machine learning use in toxicological risk assessment to predict chemical toxicity and support regulatory decisions, noting that explainable AI methods like SHAP and LIME are being explored to make model decisions interpretable.
Researchers developed a high-precision machine learning force field for the molten salt reactor fuel salt LiF-BeF2-UF4 using deep potential molecular dynamics combined with active learning, validated against density functional theory and experiments. They then used the model to systematically calculate microstructural metrics and thermophysical and transport properties across a wide temperature range of 773-1173 K and UF4 concentrations of 3-50 mol%.
By August 2026, a Histopathology study re-examined fallopian tube tissue from 19 BRCA1/2 carriers who had undergone risk-reducing salpingo-oophorectomy around age 40. Using deeper sections cut at 150 μm intervals and a deep learning model to support STIC detection, the team found occult STIC or HGSC in all patients who later developed peritoneal HGSC despite having no STIC or HGSC at initial diagnosis.
A peer-reviewed study published November 10, 2025 tested LSTM and CNN models to classify classical music as AI-generated or human-composed using statistical pitch, velocity, and duration features extracted from MIDI files via beat-based segmentation. Trained on a dataset containing both types of compositions, the models were evaluated on primary and auxiliary test sets.
Published 19 November 2025, this peer-reviewed review in Administrative Sciences consolidates recent literature on technology-driven change in human resource management. It examines AI, automation and data analytics as drivers and assesses their impact on talent acquisition, development and retention and on organizational design.
Published November 11, 2025, this peer-reviewed paper examines how AI, RPA, blockchain, and immersive technologies are redefining strategic human resource management. Drawing on a systematic literature review, institutional reports, and illustrative cases from IBM, Walmart, Unilever, and UiPath, it argues human capital has shifted from passive input to strategic enabler and proposes a conceptual model linking emerging technologies, SHRM practices, and competitiveness.
A Viewpoint published April 23, 2024 in The Lancet Digital Health examines ethical and regulatory challenges of large language models in medicine, arguing their architecture and emergent abilities set them apart from prior AI and NLP tools.
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