± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 2026
Subscribe
G Space

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 4 of 12.

345 results
Show filters and sorting
92
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

Researchers built a DPMD-based machine learning force field with active learning for FLiBeU fuel salt and used it to systematically compute microstructure, thermophysical and transport properties across 773-1173 K and 3-50 mol% UF4.

Source article: Machine learning force field development and basic physical property studies for molten salt reactor fuel salt LiF-BeF<sub>2</sub>-UF<sub>4</sub>

Physical Chemistry Chemical Physics
93
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Random survival forests models incorporating oxidative stress index score improved prediction of disease-free and overall survival for locally advanced rectal cancer patients receiving neoadjuvant therapy, outperforming conventional ypStage and enabling risk stratification.

Source article: Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns

Diseases of the Colon &amp; Rectum
94
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

The CODEX Action Incubator at UCSF convened 30 stakeholders and reached consensus on two priority metrics of AI scribe usage by primary care physicians to assess diagnostic excellence, including timely follow-up of abnormal breast and colorectal cancer screening results.

Source article: The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Diagnosis
95
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

Generative AI applied to supply chain and operations management can enhance decision-making and optimize processes across areas like demand forecasting and inventory management to improve efficiency, accuracy, and resilience.

Source article: Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

International Journal of Production Research
96
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Solvent extraction of urine analyzed by GC-MS and XGBoost distinguished bladder cancer patients from controls with AUROC 0.869 and 85% balanced sensitivity and specificity using an 8-metabolite panel.

Source article: Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer

BJC Reports
97
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

YOLOv8x-pose and YOLOv8x-seg models automated landmark detection and apical segmentation for the Cameriere European method in children aged 5-13, achieving high detection accuracy and low measurement error in a first-stage validation.

Source article: First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method

International Journal of Legal Medicine
98
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

An ensemble combining Random Forest and Multi-Layer Perceptron improved genomic prediction of residual feed intake in 220 UK Holstein cows to R2=0.39 and RMSE=0.086, outperforming conventional gBLUP.

Source article: Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle

Journal of Animal Science and Biotechnology
99
Reader signal

How should this claim be treated?

100
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

LightGBM-based model predicted obesity versus normal weight in children aged 3-12 with prior RTIs with high accuracy and AUC, enabling early screening for targeted intervention.

Source article: Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models

JAMIA Open
102
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

Generative AI tools, especially ChatGPT chatbots, were associated with enhanced institutional performance and work productivity across sectors including academia, research, technology, communications, agriculture, government, and business as of early 2024.

Source article: Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Sustainability
103
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

AI-based machine learning applied to cholangioscopy images achieved high pooled diagnostic performance for indeterminate and malignant biliary strictures, with 95% sensitivity and 88% specificity.

Source article: The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis

Journal of Clinical Gastroenterology
104
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A Bi-LSTM and CNN fusion model combining text and visual data achieved 91.3% precision and 90.1% F-score on emotion recognition and up to 85.32% accuracy on depression classification, outperforming single-modal baselines.

Source article: Design and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment

Biomedical Physics &amp; Engineering Express
105
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedClimate

Machine learning models combined with fuzzy logic integration improved prediction of flood, avalanche, rockfall and landslide susceptibility in a mountainous region of northern Iran, with RF and AND operator achieving highest reliability for spatial planning.

Source article: Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping

Scientific Reports
107
Reader signal

How should this claim be treated?

108
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Among 358 medical students surveyed, higher digital literacy was associated with more positive attitudes towards artificial intelligence and was the strongest independent factor in adjusted analysis.

Source article: Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions

Postgraduate Medical Journal
109
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

An ensemble of five machine learning algorithms applied to spinal anesthesia cases identified actionable risk factors for postoperative nausea and vomiting, with postoperative fentanyl as the strongest contributor, and was described as accurate enough to support PONV prediction.

Source article: Machine learning-based prediction of postoperative nausea and vomiting after spinal anesthesia: A retrospective observational study

PLOS One
110
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A Bayesian joint modeling system that integrates longitudinal CD4+ counts and CD4/CD8 ratios predicted 5- to 7-year risk of incomplete immune reconstitution in people living with HIV with high discrimination, enabling real-time identification of high-risk patients for timely intervention.

Source article: Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling

Science Advances
111
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A multimodal deep learning model using first-24-hour ICU data predicted subsequent in-hospital mortality with high discrimination, and adding clinical notes and chest X-ray images further improved performance.

Source article: Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data

Anesthesiology
112
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Integrating pFBA with DoubleML identified 17 hypertension-associated metabolites and a 19-species FERM guild whose metabolic flux contribution, not abundance, tracks blood pressure, pointing to microbiome intervention targets.

Source article: The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations

mSystems
113
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

Automated vision system SolenopsisDetector can localize fire ants and classify Solenopsis species from images, with whole-body detection reaching high mAP and thorax/abdomen crops improving classification accuracy.

Source article: SolenopsisDetector: development of an automatic detection system for fire ants using computer vision and deep learning

Journal of Economic Entomology
114
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A random forest model using only routine preinjury clinical data predicted posttraumatic epilepsy onset up to 10 years after TBI in veterans, achieving AUC around 0.73-0.75 and identifying 17.5% of 5-year cases at 2.3% false positive rate.

Source article: Long-term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data

Epilepsia
115
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A StepCox plus Random Survival Forest framework built a ferroptosis- and lipid metabolism-related RiskScore that independently predicted overall survival in TCGA-COAD and GSE39582 colorectal cancer cohorts, and identified CRY2 as a regulator whose knockdown inhibited proliferation and suppressed xenograft growth.

Source article: Prognostic risk modeling based on integrated multi-omics analysis identifies CRY2 as a key regulator in tumor immunity and patient survival in colorectal cancer

Annals of Medicine
116
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Patients recovering from orthopedic surgery reported 80.3% willingness to use patient-facing AI systems for transitional care, with priorities shifting to functional safety and rehabilitation guidance after discharge.

Source article: Mapping the Evolving AI Preferences and Care Needs in Orthopedic Transitional Care From Hospitals to Home: Cross-Sectional Study

JMIR AI
119
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Logistic regression achieved comparable discrimination to 14 machine learning algorithms for 28-day mortality prediction in young adults with acute poisoning and enabled a clinically usable nomogram for early risk stratification.

Source article: Comparison of Machine Learning and Logistic Regression in Predicting Mortality from Acute Poisoning in Young Adults: A Multicenter Study Identifying Herbicide Exposure as the Predominant Risk Determinant

Toxicology Mechanisms and Methods
120
Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

An ensemble Stacking model using baseline clinical, neurological, and MRI features predicted one-year AIS grade and motor/independence scores in TCSCI patients with high discrimination and low error on external testing.

Source article: Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study

Acta Neurologica Belgica