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. 413 records · page 7 of 14.

181

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

182

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedClimate

Hybrid STICS plus random forest integration improved accuracy and interpretability of apple fruit maturity date prediction to support harvest timing and climate adaptation across China's apple regions.

Source article: Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions

Journal of the Science of Food and Agriculture

183

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

LLM-based staged extraction framework localized PVC origin as left versus right from 12-lead ECG images with discrimination comparable to a CNN baseline while providing a traceable stepwise diagnostic process.

Source article: Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study

Journal of Cardiovascular Electrophysiology

184

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Researchers developed a nomogram using AI-derived CCTA measures of pericoronary adipose tissue and plaque plus HbA1c to predict progression of non-obstructive coronary lesions in T2DM patients.

Source article: Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study

Acta Diabetologica

185

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A hybrid Mask R-CNN and YOLOv11 segmentation pipeline automated postoperative Pink Esthetic Score attribute assessment from intraoral photographs with over 82% accuracy per attribute and 79.3% total-score agreement within one point of experts.

Source article: Artificial intelligence-based photographic detection of pink esthetic score attributes using a hybrid deep learning segmentation pipeline: a method development study

Scientific Reports

186

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Using Cox regression, SHAP analysis and 100-algorithm validation, researchers selected five autophagy-related core genes and built a prognostic model for lung adenocarcinoma that showed AUC > 0.9 in independent cohort GSE68465.

Source article: Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma

Naunyn-Schmiedeberg's Archives of Pharmacology

187

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Clinical-parameter XGBoost model stratified patients into low-risk and high-risk groups with 94.0% vs 65.0% 2-year local control after carbon-ion radiotherapy for early-stage peripheral NSCLC.

Source article: Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Anticancer Research

188

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

START-AI improved prediction of inpatient admission from the emergency department from AUROC 0.78 to 0.90 when ensemble and transformer features were added.

Source article: The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis

Emergency Medicine Australasia

189

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A point-of-care exhaled breath condensate device combined with physiological parameters and ensemble machine learning identified early-stage lung cancer in a real-world screening cohort with 85.7% accuracy and 100% specificity on held-out test data.

Source article: Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study

Clinical and Translational Science

190

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Integrating large-scale transcriptomic profiling with machine learning identified a three-gene blood signature that enables early sepsis diagnosis and risk stratification across severity levels.

Source article: Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis

Journal of Cellular Physiology

191

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedEducation

AI-generated subtitles for ECFS e-learning modules enabled quick and affordable creation of multilingual education packages in Ukrainian, Romanian, and Turkish that achieved high accuracy after expert editing.

Source article: Bridging the Gap: Translated Medical Education to Support Cystic Fibrosis Centers From Non-English Speaking Countries

Pediatric Pulmonology

192

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Machine learning models using structural MRI, amyloid PET, and demographic features can classify tau positivity in the Braak III/IV region in amyloid-positive cohorts, achieving AUC 0.92 and 85% accuracy on external validation.

Source article: Classification of tau status with machine learning models in amyloid-positive cohorts

Alzheimer's & Dementia

193

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedMedia & Arts

Adoption of text-to-image generative AI increased human creative productivity and the likelihood of peer favorites per view among artists in a large online art community.

Source article: Generative artificial intelligence, human creativity, and art

PNAS Nexus

194

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Survival models including machine learning approaches predicted imminent 30-day opioid overdose following a first opioid-related diagnosis with C-indices up to 0.745, identifying prior overdose and opioid misuse as strongest predictors for proactive clinical intervention.

Source article: Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program

The American Journal of Drug and Alcohol Abuse

195

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

ConvNeXt-based AI trained on 3676 smartphone images from 1016 patients achieved AUCs of 0.825-0.860 for EASI component scores >=2 and >0.900 for score 3, supporting use as an educational tool to standardize EASI scoring.

Source article: Educational Artificial Intelligence Software to Support Assessment of Atopic Dermatitis Severity

The Journal of Dermatology

196

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

AI with multi-omics and systems-level frameworks was used to support target identification, microbial network reconstruction, biomarker discovery, and therapeutic prioritization for anti-virulence strategies against Salmonella Typhi.

Source article: Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges

Archives of Microbiology

197

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Survey-weighted Explainable Boosting Machine achieved strong temporal stability for severe tooth loss prediction on US BRFSS cohorts, supporting transparent population-level risk stratification.

Source article: Interpretable Machine Learning for Population-Level Tooth Loss Prediction

Journal of Dental Research

198

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A two-stage CNN pipeline using Faster R-CNN with EfficientNet-B7 detected dental implants on panoramic radiographs and classified brand and prosthetic platform size with high accuracy, supporting faster and more standardized clinical workflows when records are missing.

Source article: Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

The Journal of Prosthetic Dentistry

199

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

LLM-driven adaptive level modification framework classified players by skill with 97.82% accuracy and generated modified levels that remained traversable at 74.1% full-level and 83.5% chunk-level rates.

Source article: Adaptive level modification via player skill classification and large language models

Scientific Reports

200

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A machine learning-derived methionine metabolism-related risk score stratified colorectal cancer patients into high- and low-risk groups with significantly different overall survival in training and external validation cohorts.

Source article: Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment

Asia-Pacific Journal of Clinical Oncology

201

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Deep learning models using orbital MRI predicted continuous clinical activity score with low error and inferred patient characteristics including age, sex, and smoking status.

Source article: Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Orbit

202

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Machine learning models trained on TriNetX data stratified patients receiving immune checkpoint inhibitors into risk tiers for cardiac immune-related adverse events within 90 days, achieving moderate discrimination.

Source article: Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors

Supportive Care in Cancer

203

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A LightGBM model trained on metabolomic data predicted incident major depressive disorder among obese participants with AUCs around 0.82-0.84 over 3 to 9 years and outperformed existing clinical models.

Source article: Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

Psychological Medicine

204

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Advances in AI have enabled opportunistic identification of osteoporosis and other pathologies on imaging performed for other indications.

Source article: Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging

Journal of Radiological Protection

205

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

In a retrospective audit of 5,000 women, the LBC-NET pipeline detected HSIL+ with higher sensitivity than senior cytopathologists and reduced review time while increasing daily capacity.

Source article: An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers

Acta Clinica Belgica

206

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedScience

Release of SynTabFall synthetic dataset with 745,380 samples allows training fall-risk prediction models without original patient data while achieving performance on par with models trained on real data.

Source article: Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment

Scientific Data

207

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

An XGBoost model trained on routinely collected prehospital vitals and demographics reduced under-triage and over-triage compared to a surrogate of the 2021 National Field Triage Guidelines in 1.2M trauma patients.

Source article: Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

The American Surgeon™

208

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Clinicians assisted by Retina4IRD achieved higher top-5 genetic diagnosis accuracy and better downstream management scores for suspected inherited retinal disease compared to specialist-only care.

Source article: AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

Nature Medicine

209

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

A machine learning model using bedside fNIRS functional connectivity during audio movie clips predicted 6-month functional outcome in ICU patients with acute brain injury with 81.3% balanced accuracy, outperforming clinical models.

Source article: Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy

Neurocritical Care

210

Reader signal

How should this claim be treated?

Evidence-backed gainPeer-reviewedHealth

Monthly allocation of Medicaid care-management outreach by predicted individualized treatment effect prevented substantially more ED visits or hospital admissions than risk-based allocation at the same 10% capacity.

Source article: Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning

Population Health Management