Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Purpose The purpose of this study was to evaluate the performance of two general-use artificial intelligence models, ChatGPT and Grok, in classifying impacted mandibular third molars using the Pell and Gregory system on panoramic radiographs, compared with a resident consensus reference standard. Materials and methods One hundred panoramic radiographic images of impacted mandibular third molars were independently classified by two blinded resident reviewers using the Pell and Gregory classification system.
Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases.
Accurate prediction of refrigerant deficiency in consumer air conditioning (AC) systems is critical for optimizing energy efficiency and operational stability. However, existing data-driven models often suffer from significant performance degradation due to domain shift across different AC types and a lack of explanatory transparency.
Objective We set out to examine how completely artificial intelligence (AI)-based orthopedic studies report their methods. Methods A PubMed search covering 2023-2025 was run with a predefined strategy.
By August 2026, peer-reviewed guidance for neurologists described a shift in obstructive sleep apnea diagnosis from in-laboratory polysomnography alone to home testing augmented by wearables, nearables, and FDA-cleared software-as-a-medical-device platforms that leverage artificial intelligence and multisignal integration to estimate sleep parameters. The article framed these tools as improving accessibility for patients unable or unwilling to undergo lab studies.
On 2026-08-03, a peer-reviewed study reported a random forest model trained on 16S rRNA gene sequencing from a field mesocosm to predict antifouling paint particle contamination in sediment. In lab incubation tests it identified 100% of presence samples and 83.3% of absence samples, and when applied to 14 Baltic Sea and Warnow estuary sites it correctly labeled all uncontaminated sites and three of five contaminated sites.
A meta-analysis of 110 studies up to June 2026 evaluated AI algorithms for diagnosing urological cancers on CT, MRI, and ultrasound, pooling sensitivity, specificity, and AUC and comparing to clinician performance where reported.
This PRISMA systematic review evaluated 33 peer-reviewed studies (2019-2026) comprising 50 deep learning models that predict knee osteoarthritis progression from medical imaging. It extracted AUC as primary outcome, categorized nine different progression definitions, and assessed bias with PROBAST-AI, finding median internal AUCs of 0.87 for surgery, 0.78 for structural, and 0.79 for symptomatic endpoints.
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