Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published August 14, 2026, this peer-reviewed synthesis argues that lymph-node metastasis prediction in colorectal cancer should move beyond static histology to clonal ecology, integrating computational pathology with evolutionary oncology and AI-enabled tracking of dominant and dormant subclones.
This narrative review from August 2026 summarizes AI applications across occlusion-oriented digital reconstruction of maxillofacial fractures, where treatment must address stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation as interdependent targets. It evaluates tasks from CT/CBCT screening and segmentation to model repair, shape completion, planning assistance, and postoperative deviation analysis.
Researchers developed and validated a machine learning model to predict erectile dysfunction risk from routine blood test data, using 4116 NHANES participants for training and internal validation and 489 NPTR-confirmed patients for independent external validation. The random forest model achieved the best results in external validation.
This review from August 2026 summarizes how artificial intelligence applied to standard electrocardiograms has been tested in pediatric and congenital heart disease. It reports that deep learning models have been shown to identify arrhythmias, ventricular dysfunction, and CHD, and are being extended to predict future risk and to analyze wearable and telemetry data.
Published October 17 2025, this peer-reviewed review summarizes how machine learning techniques are being used to predict mechanical behavior of composite materials from experimental and simulation data.
Researchers built an 18-feature model across basic information, training, fitness and lifestyle dimensions for 800 Chinese university football players from a Kaggle dataset, comparing 10 algorithms and finding SVM best at 95.6% accuracy with SHAP highlighting stress, sleep and balance as key predictors.
The peer-reviewed article examines how AI has become an intimate presence in mental health through mood-tracking apps, emotion wearables, and therapeutic chatbots like Woebot and Wysa. It argues these systems enable cognitive offloading by aggregating biometric and self-report data and delivering CBT-based prompts, while simultaneously risking cognitive overload.
By November 2025, this peer-reviewed review synthesized how artificial intelligence bridges the cancer multi-omics data deluge to clinical decisions, integrating genomics, transcriptomics, proteomics, metabolomics and radiomics using deep learning, graph neural networks, transformers, and explainable AI.
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