Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
A structured narrative review of literature from January 2015 to December 2025 examined AI for hypertension screening, diagnosis, risk stratification, treatment optimization and remote monitoring. It found ML models often outperformed conventional risk scores with AUCs of 0.75 to 0.90 and showed promise for personalized therapy and continuous monitoring.
This review examines generative AI-enabled synthetic relationships, defined as ongoing associations with AI companions designed to simulate human-like bonds, as a potential intervention for loneliness where traditional approaches face availability and scalability limits.
On 2026-07-01, Nature Communications published a peer-reviewed study introducing EAGLE, a deep learning framework for digital pathology. The system was tested across 43 tasks from nine cancer types and was reported to outperform patch aggregation methods by up to 23% while processing one slide in 2.27 seconds.
Researchers conducted a cross-national analysis of generative AI guidelines issued by leading universities in the United States, Japan, and China, coding policies across five Technology Acceptance Model domains to identify 20 themes and to build the University Policy Development Framework for Generative AI (UPDF-GAI).
Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary identifiers are unavailable for direct individual comparison. In forensic scenarios involving advanced decay, specific taphonomic alterations, or midfacial blunt force impacts, the mandibular ramus serves as a valuable anatomical marker due to its distinct sexual dimorphism and thick cortical structure, making it more resilient to fragmentation than other, more fragile facial bones.
Background The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy is reduced in such patients due to low albumin levels.
Researchers retrospectively tested ChatGPT on 300 histopathologically confirmed oral lichen planus cases with at least 24 months of follow-up, using serial clinical records, intraoral photographs, and histopathology reports. Compared with blinded expert panel consensus, the model achieved 94.7% accuracy for trajectory classification and 78.8% sensitivity with 99.6% specificity for high-risk detection as of the August 2026 publication.
Researchers retrospectively analyzed 306 infants aged 1 to 90 days hospitalized between 2014 and 2022 in Khorasan Razavi, Iran, using CSF culture via lumbar puncture as the gold standard, to train nine machine learning classifiers on routine non-invasive paraclinical markers with nested cross-validation and SHAP interpretation.
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