Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers combined three lung adenocarcinoma GEO transcriptome datasets with a human autophagy gene set, identified 276 shared differentially expressed autophagy genes, and used protein interaction networks plus machine learning to select five core prognostic genes ENG, CDH1, KLF4, IL6 and MMP9, validating a model based on them in independent cohort GSE68465 with AUC > 0.9 by August 2026.
By October 2025, a peer-reviewed study examined AI use in electoral management in Indonesia, Thailand, Philippines and Myanmar between 2019 and 2024, finding that biometric voter identification, cyber-based registration, and real-time result monitoring streamlined administration and improved list accuracy, particularly amplifying coordination in Thailand.
The paper examines how AI and extended reality enable creation of avatars and human digital twins from personal and biometric data that persist after death as Human Digital Remains. Using cross-disciplinary analysis and doctrinal review, it finds that current EU instruments do not extend protections to the deceased and identifies urgent legal and ethical gaps.
Researchers eye-tracked 100 cytotechnologists diagnosing 30 digital cytology images and then tracked 28 students before and after a 3-month training program. They found years of experience did not predict accuracy, while shorter fixation on the low-power field main object did, and students markedly improved time to first target fixation and reduced background attention after training.
The peer-reviewed article reviews risk stratification in cardiothoracic surgery, noting that EuroSCORE II and STS scores are standard but static, while AI and machine learning studies have reported improved predictive discrimination in selected cohorts by handling complex data.
A systematic review published June 30, 2026 searched PubMed, Scopus and ScienceDirect for 2020-2025 literature on AI in cardiology and HTA. After screening 223 records, six studies were included, covering stroke outcome prediction, atrial fibrillation screening and wearable-based monitoring, supported by 17 documents, and compared against three HTA frameworks for EU HTAR alignment.
Researchers evaluated six AI chatbots using 36 simulated English-language patient questions about nipple discharge, generating 216 first-turn responses scored against clinical guidelines. By the July 2026 publication date, 87.5% of responses were rated safe, 8.8% had minor omissions, and 3.7% were potentially misleading, with an overall red-flag recognition rate of 90.6%.
By July 2026, researchers had tested whether native thin-slice images could recover coronary calcification missed on standard 5.0 mm chest CT. Using a validated deep learning algorithm to quantify CAC on paired reconstructions, they found 19.0% of internal cohort patients and 10.2% of NLST participants were reclassified from CAC =0 to CAC >0 on thinner slices.
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