The record holds 934 sourced gains and 771 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.
Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
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A systematic review and meta-analysis of 48 studies published through March 2025 synthesized diagnostic accuracy of generative AI in radiology and compared it to physician performance using multilevel random-effects meta-regression.
Updated Oct 5, 2026 · TRV-2026-1284
Using CHARLS data, researchers built an interpretable machine-learning framework to identify MCI at time of assessment in older adults with chronic conditions. After LASSO selection of 27 predictors, a prespecified XGBoost model achieved AUROC 0.692 internally in 2018 and 0.702 in 2020 temporal validation, with TreeSHAP highlighting number of living children, sleep duration, education, ADL and age.
Updated Oct 5, 2026 · TRV-2026-1283
This peer-reviewed review from October 2026 surveys deep learning approaches to ultrasound beamforming, including CNNs, GANs, transformers and hybrid models, and evaluates them against traditional delay-and-sum methods. It also examines datasets, benchmarks, and hardware strategies such as FPGA acceleration and cloud-edge inference for real-time use.
Updated Oct 5, 2026 · TRV-2026-1281
A systematic review and meta-analysis of imaging AI for avascular necrosis of the femoral head pooled nine estimates and reported sensitivity of 0.888, specificity of 0.937, and SROC AUC of 0.967, with MRI-based models showing higher sensitivity than radiography-based models.
Updated Oct 5, 2026 · TRV-2026-1280
In patients with newly diagnosed diffuse large B-cell lymphoma from the PETAL trial, investigators used machine learning-supported body composition analysis of CT imaging to measure skeletal muscle mass. Those in the lowest tertile had inferior survival after adjustment for established risk factors, with cause-specific analyses pointing to nonrelapse mortality rather than lymphoma-specific death.
Updated Sep 1, 2026 · TRV-2026-0948
A prospective study of 14 edentulous patients compared actual post-denture facial photographs with AI-generated predictions from pretreatment images using Gemini and FaceApp, assessing patient preference, expert esthetic ratings, and quantitative anthropometric measurements.
Updated Aug 31, 2026 · TRV-2026-0938
Published August 29, 2026, this peer-reviewed synthesis reviews 2020-2025 literature on machine learning for mapping potentially toxic elements in soils. It finds growing use of ML with environmental covariates but persistent use of spatially naive validation, limited interpretation, and incomplete uncertainty reporting.
Updated Aug 31, 2026 · TRV-2026-0932
By August 2026, a review in Clinical Microbiology and Infection summarized evidence on using unsupervised machine learning to derive clinical subphenotypes of bloodstream infections. The strongest data were in Staphylococcus aureus bacteraemia, where latent class and cluster analyses identified distinct subgroups with different mortality across cohorts, with emerging work in ICU mixed-pathogen and transplant populations and early bedside calculators for phenotype assignment.
Updated Aug 30, 2026 · TRV-2026-0927
Recomputed live from the record · Oct 12, 2026, 11:15 AM
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