Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
By August 13, 2026, a systematic review and meta-analysis of 18 studies found AI models predicted laparoscopic cholecystectomy difficulty with pooled AUCs of 0.848 in training and 0.818 in validation, with ensemble models reaching 0.889 and 0.861. The review searched four databases to March 2, 2026 and used PROBAST and GRADE to assess bias and certainty.
On 2026-08-13, a review in Personalized Medicine examined integration of causal artificial intelligence and data-driven decision intelligence within healthcare informatics to advance personalized medicine. Using a narrative review of literature from PubMed, Scopus, Web of Science, IEEE Xplore and ScienceDirect, the authors synthesized evidence on causal inference methods and clinical applications.
In a multicentre cross-sectional study of 318 ICU nurses in China, researchers developed a machine learning risk-profiling model for moral distress, which was present in 28.6% of participants. A gradient boosting machine achieved the best balanced performance and, after SHAP-guided reduction, retained full accuracy with six predictors: monthly night shifts, financial responsibility role, psychological resilience, sleep quality, nurse-to-patient ratio, and weekly working hours.
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.
Published September 23, 2025, this peer-reviewed study systematically tested frontier Large Reasoning Models that generate detailed thinking processes before answering. Using controllable puzzle environments to vary compositional complexity, the authors analyzed final accuracy and internal reasoning traces and compared LRMs to standard LLMs under equivalent inference compute.
A September 2025 peer-reviewed review in Clinics and Practice synthesized 150 studies of AI in clinical medicine after screening 2047 PubMed records. It found strong diagnostic imaging performance with expert-level cancer detection, promise for CDSS in predicting sepsis and atrial fibrillation, and advances in surgical guidance, pathology diagnosis, and drug discovery via protein structure prediction.
In a peer-reviewed survey published October 15, 2025, researchers analyzed 10 recent Zero-Trust Architecture surveys and 136 primary studies from 2022-2024 and found most controls lacked real-world validation. They argue generative AI attacks exploit those gaps and propose a seven-stage Cyber Fraud Kill Chain that maps synthetic identities, context manipulation, and adversarial telemetry to NIST SP 800-207 components.
In a peer-reviewed study published October 2025, researchers conducted 39 interviews with ChatGPT, Gemini and Replika, prompting each to impersonate people in six occupational groups ranging from highly skilled professionals and humanities professors to blue-collar workers, construction workers, computer scientists and hairdressers. The qualitative analysis identified regularities in how the chatbots described everyday tastes and lifestyles that aligned with class distinctions.
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