Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
As of the August 2026 commentary, AI was increasingly integrated into oncology for detection, risk stratification, treatment planning, and documentation. The authors reviewed evidence that these systems can reproduce or amplify disparities and examined technical sources of bias and competing statistical definitions of fairness.
Published August 17 2026 in Abdominal Radiology, this Perspective examines large language models applied to prostate MRI reporting, a task where laterality, sector, size, PI-RADS, and staging language directly affect biopsy and treatment decisions and where patients often see reports via portals before clinician discussion.
Researchers prospectively tested a deep-learning based artificial intelligence iterative reconstruction algorithm against conventional hybrid iterative reconstruction in 132 gastric cancer patients undergoing preoperative abdominal CT before surgery or staging laparoscopy. By August 2026, they reported higher Likert scores for tumor margin and enhancement, higher contrast-to-noise ratios in arterial and portal venous phases, and higher AUC for detecting serosal invasion with AIIR.
Researchers developed a four-stage deep learning framework for CT-based spinal cord injury fracture assessment, combining Weighted Balanced Anisotropic Filtering for denoising, Modified Residual U-Net for spinal cord segmentation, Improved Pyramid Histogram of Oriented Gradients for feature extraction, and a new IShuffleNet-Parallel CNN classifier with Group Normalization and Adaptive Swish-Mish activation.
This peer-reviewed review from January 2025 examined how AI technologies including robotics, machine learning, deep learning, and natural language processing are being applied in healthcare. Drawing on Web of Science literature from 2014-2024 and case studies such as Google Health and IBM Watson Health, it reported growth in publications and use in patient interaction, predictive analytics, and remote monitoring.
Published February 24, 2025, this Nature Communications review examines how artificial intelligence is used to model and understand extreme weather and climate events including floods, droughts, wildfires, and heatwaves. It reports that AI has improved weather forecasting, model emulation, parameter estimation, and prediction of extremes, while also discussing methods to identify and explain events more effectively.
This February 2025 systematic review of 103 papers examined how Cognitive Load Theory, Educational Neuroscience, and AI/ML combine in adaptive learning. It found that systems using EEG, fNIRS and other physiological signals to feed CNN, RNN and SVM models can automatically manage cognitive load and dynamically adapt learning pathways for K-12 and adult learners.
On March 12, 2025, an umbrella review in the Journal of Medical Internet Research synthesized 18 reviews from 274 screened records on AI in nursing. It found consistent reports of potential advances in patient care and clinical workflows alongside an urgent push to update nursing curricula with AI-driven tools and ethics training.
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