TruaceTracing the truth around AIFriday, August 28, 2026
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The Good surrounding AI

Documented gains, ranked by source quality, corroboration, and recency. Reader feedback is shown separately and never changes the evidence rank. 345 records · page 9 of 12.

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245
Reader signal

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Evidence-backed gainPeer-reviewedHealth

A six-protein plasma signature tested with SVM distinguished fibrotic hypersensitivity pneumonitis from idiopathic pulmonary fibrosis on an independent test set with 71.4% accuracy, offering a non-invasive diagnostic aid.

Source article: Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis

Journal of Translational Medicine
246
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

LightGBM model trained on 282 Lenke 1/2 AIS patients predicted postoperative coronal imbalance with AUC 0.885 training and 0.824 internal validation, identifying LIV-LSTV, Lumbar Modifier, and Risser grade as key predictors.

Source article: Predicting postoperative coronal imbalance in Lenke 1/2 adolescent idiopathic scoliosis: A machine learning model with clinical interpretability

International Orthopaedics
247
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Multi-Scale Feature Fusion model combining U-Net segmentation, EfficientNet and attention autoencoder features fused via CCA and YOLO classification achieved 99.95% accuracy on apple leaf disease datasets, enabling early detection for sustainable agriculture.

Source article: Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion

BMC Plant Biology
250
Reader signal

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254
Reader signal

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255
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Machine learning classifiers using demographic, clinicopathological and inflammatory markers predicted 5-year overall survival and second primary cancer occurrence in a European nasopharyngeal carcinoma cohort.

Source article: Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

European Archives of Oto-Rhino-Laryngology
256
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

XGBoost model integrating UHR and iPTH predicted protein-energy wasting in incident hemodialysis patients with AUC 0.801, enabling individualized risk assessment.

Source article: Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study

Renal Failure
258
Reader signal

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260
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

AI-optimized monoclonal antibody GB-0669 was safe and well-tolerated in healthy adults with dose-proportional pharmacokinetics and a 54-day half-life and dose-dependent serum live virus neutralization.

Source article: Randomized, Double-Blind, Placebo-Controlled First-in-Human Trial of a First-in-Class AI-Designed Monoclonal Antibody (GB-0669) Against the Conserved SARS-CoV-2 Spike S2 Stem Helix

The Journal of Infectious Diseases
262
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedEducation

Three elementary students with dyslexia improved from low baseline reading comprehension to mastery and maintained gains after GenAI-based visual instruction using ChatGPT-generated visuals aligned to their Arabic coursebook.

Source article: The effects of AI-based visual instruction on the reading comprehension of students with dyslexia in Saudi Arabia: a single-case experimental study

Frontiers in Education
266
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

A deep neural network trained on DFT data learned a transferable potential that predicts total energies for organic molecules with chemical accuracy versus DFT, generalizing to systems up to 54 atoms despite training on smaller molecules.

Source article: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Chemical Science
270
Reader signal

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