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Good Space a ranking of the record

TheGood surrounding AI

574 documented gains are on the board. 82% rest on at least one peer-reviewed study, and 8% clear the high-evidence bar. Health carries 49% of it. The latest was filed Oct 16, 2026.

Every gain on the board, one square each, shaded by the strength of its evidence. High 45 Moderate 529 Emerging 0
574 results
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The board · page 5 of 20

121
Evidence-backed gainPeer-reviewedHealth

Final-year nursing students receiving AI-adaptive simulation integrated with a workforce decision-support system showed greater gains in simulated leadership performance, team performance, and self-efficacy at Week 10 compared to controls.

Source article: Artificial Intelligence-Based Adaptive Simulation Integrated With a Workforce Decision-Support System to Improve Simulated Nursing Leadership Performance: A Randomized Controlled Study

Cureus
Reader signal

How should this claim be treated?

122
Evidence-backed gainPeer-reviewedHealth

Fine-tuned LLaMA models extracted symptom presence and severity from free-text notes with high F1 scores, enabling the first structured quarterly trajectories for VNS patients.

Source article: Longitudinal symptom severity tracking in vagus nerve stimulation patients: a 2-stage LLM-based pipeline with explainable AI

JAMIA Open
Reader signal

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123
Evidence-backed gainPeer-reviewedScience

Random Forest predicted atmospheric attenuation for free-space optical links more accurately than deep learning models on a 1500-sample synthetic Iraqi weather dataset, with dust concentration and visibility as top drivers and faster training and inference.

Source article: Machine Learning Outperforms Deep Learning for Atmospheric Attenuation Prediction in Free-Space Optical Communications under Iraqi Weather Conditions

Journal of Visualized Experiments
Reader signal

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124
Evidence-backed gainPeer-reviewedHealth

A machine learning pipeline applied to urine SERS spectra improved multiclass diagnosis of IC and OAB versus healthy controls, achieving up to 92% accuracy with interpretable pathologic Raman features.

Source article: Multiclass Diagnostic Improvement of Interstitial Cystitis (IC) and Overactive Bladder (OAB) via Machine Learning Pipeline on Surface-Enhanced Raman Spectroscopy (SERS)

ACS Sensors
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125
Evidence-backed gainPeer-reviewedHealth

Integrating AI into clinical practice can improve disease diagnosis, treatment selection, and lab testing by leveraging large datasets to increase accuracy, reduce costs, save time, and minimize human errors.

Source article: Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education
Reader signal

How should this claim be treated?

127
Evidence-backed gainPeer-reviewedHealth

Training 13 machine learning classifiers on the NEW10 macrophage module enabled accurate distinction of acute coronary syndrome from stable coronary artery disease and ruptured from stable plaques, with SVM linear and Naive Bayes reaching AUC 0.9333 in independent testing.

Source article: Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis

Chemical Biology & Drug Design
Reader signal

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128
Evidence-backed gainPeer-reviewedHealth

In multicenter retrospective validation, greater concordance between clinician ventilator settings and VentPilot AI recommendations was associated with more ventilator-free days within 28 days, lower 28-day mortality, and shorter ICU length of stay.

Source article: Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation

Journal of Intensive Care
Reader signal

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129
Evidence-backed gainPeer-reviewedScience

Interpretable CatBoost-based machine learning framework predicted GenX-induced biphasic dose-responses in zebrafish with high fidelity, enabling mechanistic ecological risk assessment of PFAS alternatives.

Source article: Predicting Hormesis Effects of GenX in Zebrafish via Interpretable Machine Learning: Insights From SHAP Analysis

IUBMB Life
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130
Evidence-backed gainPeer-reviewedHealth

A LightGBM-based multimodal model integrating ultrasound radiomics, hemodynamic signatures and clinical data achieved high diagnostic accuracy on BI-RADS 4a nodules, supporting safer reduction of unnecessary biopsies.

Source article: Enhancing diagnostic precision for BI-RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles

Journal of Applied Clinical Medical Physics
Reader signal

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131
Evidence-backed gainPeer-reviewedHealth

VoxelMorph achieved comparable deformable registration accuracy to clinical algorithms on thoracic 4D-CT, reaching 0.98 median lung Dice and 36.83 HU median MAE with shorter processing times in the evaluated implementation.

Source article: Comparative evaluation of VoxelMorpand conventional deformable image registration algorithms for thoracic 4D-CT in radiotherapy

Journal of Applied Clinical Medical Physics
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132
Evidence-backed gainPeer-reviewedHealth

A Super Learner ensemble and derived 17-feature point-based scorecard predicted 1-year pneumonia hospitalization among community-dwelling Japanese adults aged 75+ with good discrimination and calibration on internal test data.

Source article: A Machine Learning-Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old-Old Adults

Geriatrics & Gerontology International
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133
Evidence-backed gainPeer-reviewedHealth

Researchers used machine learning and LASSO regression on single-cell data to identify seven PANoptosis key genes and build a validated IDD risk diagnosis model, with experimental knockdown of ACSBG1 reducing degeneration in a rat model.

Source article: Identification and Experimental Validation of PANoptosis Key Genes for Constructing a PANoptosis Risk Diagnostic Model in Intervertebral Disc Degeneration

The Journal of Gene Medicine
Reader signal

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134
Evidence-backed gainPeer-reviewedHealth

Researchers built and externally validated a machine learning model that identifies DCIS at high risk of progressing to invasive disease, enabling more precise risk stratification and clinical management.

Source article: Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC

Advanced Science
Reader signal

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135
Evidence-backed gainPeer-reviewedHealth

A Random Forest model combining two tryptophan metabolites (kynurenine and neopterin) with five routine clinical tests discriminated MASLD from healthy controls with high diagnostic performance.

Source article: Machine learning-based discrimination of metabolic dysfunction-associated steatotic liver disease: implications of tryptophan metabolites as potential biomarkers

Clinica Chimica Acta
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136
Evidence-backed gainPeer-reviewedHealth

An interpretable machine learning nomogram using eGFR, anhepatic phase time, postoperative log D-dimer and ALT available within 1 hour after liver transplantation predicted AKI with AUC 0.861 in training and 0.761 in validation, with SVM reaching 0.846, to enable early identification for intensified renal monitoring.

Source article: Machine learning-based nomogram for acute kidney injury after liver transplantation

Renal Failure
Reader signal

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137
Evidence-backed gainPeer-reviewedHealth

Parent-report ML model combining expressive vocabulary and joint attention items achieved over 0.7 sensitivity and specificity for both autism and developmental delay at 18-month visits, approximately doubling sensitivity versus existing screens.

Source article: Exploring the Potential of Parent Report for Autism/Developmental Screening at the 18-Month Visit

Journal of Autism and Developmental Disorders
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138
Evidence-backed gainPeer-reviewedHealth

Machine learning models trained on perioperative data achieved high discrimination for 30-day postoperative mortality and identified clinically meaningful predictors, supporting more transparent surgical risk assessment and patient counseling.

Source article: Toward Clinically Interpretable Perioperative Decision Support: Explainable Machine Learning for 30-Day Postoperative Mortality Prediction

The American Surgeon™
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139
Evidence-backed gainPeer-reviewedScience

AlphaFold DB scaled from 300k to over 214 million predicted structures and was integrated into primary resources like PDB and UniProt, expanding structure coverage for research use.

Source article: AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

Nucleic Acids Research
Reader signal

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140
Evidence-backed gainPeer-reviewedHealth

Patient-specific 3D point-cloud model predicted motor improvement within 5 MDS-UPDRS-III points for 15 of 24 test cases, outperforming a voxelized CNN baseline in this fixed split.

Source article: Three-Dimensional Point-Cloud Learning for Patient-Specific Deep Brain Stimulation Motor Response Prediction in Parkinson Disease

BME Frontiers
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141
Evidence-backed gainPeer-reviewedHealth

Unsupervised hierarchical clustering of 20,352 VLBW infants identified eight diagnosis-independent clusters of intestinal injury vulnerability, with high-risk Cluster 8 further split into five etiologic subclusters, creating a framework for a more precise taxonomy beyond traditional NEC staging.

Source article: Distinct clinical patterns of preterm infants vulnerable to intestinal injury identified by unsupervised clustering

Pediatric Research
Reader signal

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142
Evidence-backed gainPeer-reviewedScience

By October 2023 testing, six generative chatbots including GPT-4 produced ideas judged equal in quality to human ideas, indicating they can function as assistants in creative work.

Source article: Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity

Journal of Creativity
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143
Evidence-backed gainPeer-reviewedLifestyle

In contrast, the combination of Whale Optimization Algorithm (WOA) and PLS achieved the highest validation estimation accuracy (R 2 = 0.7452).

Source article: Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring

Environmental Monitoring and Assessment
Reader signal

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144
Evidence-backed gainPeer-reviewedHealth

PPV for high-confidence was superior to low-confidence detections (99.1% vs 59.1%, p Conclusion The algorithm showed good diagnostic performance for extremity fracture detection on radiographs.

Source article: Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort

Emergency Radiology
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145
Evidence-backed gainPeer-reviewedScience

We unfold the successive-interference cancellation (SIC) algorithm into a deep-learning network, where repeated stages of a single multitask network implement iterative signal estimation and interference cancellation that reflect the structural prior of SIC.

Source article: Code-multiplexed multi-frequency impedance cytometry with a unified deep-unfolding network

Microsystems & Nanoengineering
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146
Evidence-backed gainPeer-reviewedHealth

We further clarify that predictive accuracy, interpretability and causal explanation are related but distinct aims, and that machine learning should complement rather than replace psychological theory, causal reasoning and clinical judgement.

Source article: Methodological transitions in mental health research driven by machine learning

BJPsych Open
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147
Evidence-backed gainPeer-reviewedHealth

Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder: These features were used to train seven machine-learning models, among which K-nearest neighbours (KNN) performed best, achieving 95.3% ± 2.7% accuracy and an AUC (Micro) of 0.990 ± 0.010 in a nested 5-fold cross-validation framework.

Source article: Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder

Glycobiology
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148
Evidence-backed gainPeer-reviewedHealth

Results The AI model achieved an overall accuracy of 72.2%, a macro F1 score of 0.64, and a weighted F1 score of 0.73 compared with psychiatrists' ratings.

Source article: Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism

Journal of Occupational Health
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149
Evidence-backed gainPeer-reviewedHealth

Smartphone AI-Enabled Lateral Flow Immunoassay Platform Using Advanced Quantum Dots for Intelligent Quantitative Diagnostics: Lateral flow immunoassays (LFIAs) are widely used in point-of-care diagnostics for their low cost and simplicity, but conventional formats rely on subjective visual readout and lack quantitative accuracy and sensitivity.

Source article: Smartphone AI-Enabled Lateral Flow Immunoassay Platform Using Advanced Quantum Dots for Intelligent Quantitative Diagnostics

ACS Applied Materials & Interfaces
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150
Evidence-backed gainPeer-reviewedHealth

Using 1-month EHR data, the LLM achieved an accuracy of 87.0%, AUC of 0.921, F1 score of 0.579, sensitivity of 89.1%, specificity of 86.8%, and detection rate for early kidney injury of 42.2%.

Source article: Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care

BMJ Health & Care Informatics
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How should this claim be treated?