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

The board · page 6 of 20

151

Evidence-backed gainPeer-reviewedPolicy

Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks: However, robust evidence demonstrating sustained improvements in regulatory performance and long-term operational impact remains limited.

Source article: Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks

The AAPS Journal

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152

Evidence-backed gainPeer-reviewedHealth

Compared to unassisted diagnosis, AI significantly improved pooled sensitivity (87%, 95% confidence interval [CI]: 84-89%, versus 73%, 95% CI: 69-78%) and maintained high specificity (95%, 95% CI: 92-97%, versus 94%, 95% CI: 89-96%).

Source article: Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis

European Radiology Experimental

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153

Evidence-backed gainPeer-reviewedHealth

Three ML approaches were evaluated: logistic regression (LR), Convolutional Neural Network (CNN) Attention-Multiple Instance Learning (MIL) (EfficientNet-V2-S), and a CNN-Extreme Gradient Boosting Hybrid (XGBoost).Patients/ParticipantsForty pediatric participants aged 2 to 17, including individuals with VPD, conditions associated with VPD, and healthy participants.Main Outcome Measure(s)Model performance was tested in binary hypernasality classification compared to SLP consensus at two CAPS-A-AM thresholds: absent (0) versus any hypernasality (1-4) and absent/borderline (0-1) versus mild-to-severe hypernasality (2-4).ResultsMultiple independent modeling approaches were able to detect clinically rated hypernasality.

Source article: Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction

The Cleft Palate Craniofacial Journal

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154

Evidence-backed gainPeer-reviewedHealth

Self-supervised patient-specific neural network with physics-based simulation aligns intraoperative X-ray fluoroscopy to preoperative 3D volumes in seconds with order-of-magnitude accuracy improvement across anatomies and hospitals.

Source article: Rapid patient-specific neural networks for X-ray to volume registration

Nature

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155

Evidence-backed gainPeer-reviewedHealth

AI-based digital pathology models have been qualified by regulators for endpoint determination in MASH trials, with additional AI risk models and large language models applied to patient enrollment, recruitment and retention.

Source article: How to apply artificial intelligence (AI) to facilitate and enhance MASH trials

Hepatology International

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156

Evidence-backed gainPeer-reviewedScience

Genolator integrates DNA, amino acid and protein structure embeddings with natural language queries and answers questions about protein localization and function with high accuracy, outperforming general LLMs.

Source article: Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction

Genome Biology

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157

Evidence-backed gainPeer-reviewedHealth

A random survival forest clinicogenomic model integrating a 21-gene recurrence score, 14-gene immunoglobulin signature, and clinicopathologic features improved prognostic stratification in ER-positive/HER2-negative early breast cancer, identifying larger low-risk groups and improving discrimination in external datasets

Source article: Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer

Breast Cancer Research

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158

Evidence-backed gainPeer-reviewedHealth

Deep learning-augmented plasmonic system PANDA profiled alpha-synuclein aggregate size distributions from human serum and classified Parkinson's disease stages with correlation to clinical scores and dopaminergic PET imaging.

Source article: Deep Learning-Powered Plasmonic Platform for Decoding Dynamic Protein Aggregation Landscapes in Parkinson's Disease Progression

Advanced Materials

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159

Evidence-backed gainPeer-reviewedScience

An interpretable SISSO symbolic-regression AI approach identified materials genes, including d-band center and particle diameter, that statistically describe selectivity of supported palladium-based alloy nanoparticles during hydrogenation of concentrated acetylene streams under elevated pressure and temperature.

Source article: Rethinking catalysis: interpretable AI and description of real-world conditions <i>via</i> materials genes

Faraday Discussions

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160

Evidence-backed gainPeer-reviewedHealth

An explainable XGBoost model using six routine preoperative variables enabled individualized reflux esophagitis risk prediction for metabolic bariatric surgery candidates with AUC 0.881 training and 0.825 internal test.

Source article: Development of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery

Obesity Surgery

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161

Evidence-backed gainPeer-reviewedScience

HyLnc combining transformer embeddings with handcrafted biological features improved lncRNA prediction to 91.3% accuracy on independent validation, outperforming existing tools.

Source article: HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction

RNA Biology

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162

Evidence-backed gainPeer-reviewedClimate

Conformal Sampling-derived MLIPs predicted reaction energies and barriers for CO2 hydrogenation on Cu(100) and Ni(100) with maximum errors of 0.05 eV and 0.03 eV, enabling rapid transfer from copper to nickel.

Source article: Carbon dioxide hydrogenation on copper and nickel catalysts <i>via</i> a conformal sampling approach

Faraday Discussions

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163

Evidence-backed gainPeer-reviewedHealth

A reliability-oriented hybrid framework combining a periocular ResNet18 and a facial ensemble of ResNet50, EfficientNet-B0 and DenseNet121 increased early ASD risk indication to 90% sensitivity in the periocular pathway, 87.1% sensitivity with 0.948 AUC in the facial pathway, and an analytically estimated 98.71% system

Source article: A hybrid deep learning framework for early autism screening

Primary Health Care Research & Development

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164

Evidence-backed gainPeer-reviewedScience

Deep-learning model automatically segmented 52 anatomical regions on brain FDG PET alone and achieved MRI-comparable pons-based SUVR quantification across cognitively normal and neurodegenerative patients.

Source article: Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum

Brain

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165

Evidence-backed gainPeer-reviewedBusiness

Machine learning classification improves risk differentiation for electric-vehicle traffic insurance by predicting claim amount levels from policy and behavioral features.

Source article: Insurance claim level estimation and influencing factor analysis for electric vehicles: A case study in Changsha city

Traffic Injury Prevention

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166

Evidence-backed gainPeer-reviewedBusiness

Organizations integrating AI can advance sustainability and contribute to UN Sustainable Development Goals by aligning strategy, infrastructure and continuous improvement to produce social, environmental and economic benefits.

Source article: Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals

Sustainable Development

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167

Evidence-backed gainPeer-reviewedScience

Life-science physics students engaged in prompting, refining, and validating generative AI-constructed simulations as a new method for virtual learning about physical phenomena.

Source article: Leveraging generative artificial intelligence for simulation-based physics experiments: A new approach to virtual learning about the real world

Semantic Scholar–indexed venue

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168

Evidence-backed gainPeer-reviewedHealth

Machine learning models trained on clinical, radiomics and dosiomics features predicted radionecrosis after stereotactic radiotherapy for brain metastases with ROC-AUC up to 80%, offering decision support that may improve patient-specific treatment and reduce radiotherapy-induced toxicity severity.

Source article: Radiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach

Physica Medica

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169

Evidence-backed gainPeer-reviewedHealth

Random forest with SHAP and network analysis among 6573 adolescents ranked key contributors to IGD risk and identified central nodes, which may inform early risk identification.

Source article: Identifying key risk factors of adolescent Internet Gaming Disorder using explainable machine learning and network analysis

Journal of Affective Disorders

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170

Evidence-backed gainPeer-reviewedHealth

A 3D-ResNet18 multi-task model reduced error in estimating rib fracture age from chest CT to 7.94 days MAE, outperforming manual expert evaluation at 10.65 days, while also classifying healing stage and fracture type.

Source article: A multi-task learning-based deep learning model for precise estimation of rib fracture age on chest CT

International Journal of Legal Medicine

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171

Evidence-backed gainPeer-reviewedHealth

Among Medicare fee-for-service beneficiaries aged 65+ with ADRD hospitalized in 2023, greater hospital adoption of patient-related AI/ML tools was associated with lower odds of frequent hospitalizations, 30-day readmissions, and preventable acute hospitalizations, with inpatient risk prediction tools also linked to no

Source article: Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias

Journal of the American Geriatrics Society

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172

Evidence-backed gainPeer-reviewedHealth

GPT-4-generated, accessibility-optimised discharge letters increased comprehension of diagnosis, treatment, investigations and follow-up compared with conventional letters.

Source article: Comprehension of an AI-generated discharge letter versus a traditional discharge letter: two controlled quasi-experimental parallel-group studies

International Journal for Quality in Health Care

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173

Evidence-backed gainPeer-reviewedClimate

Machine learning-based risk zoning using integrated biological and abiotic indexes improved fit and classification, with CFBRI raising R2 to 0.62 and Random Forest models reaching cross-validation accuracies of 0.888 for agricultural land and 0.905 for construction land.

Source article: Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Environmental Geochemistry and Health

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174

Evidence-backed gainPeer-reviewedHealth

A 1-D CNN with sharpness-aware minimization applied to Raman spectra distinguished unstimulated, activated, and exhausted T cells with >97% accuracy and quantified exhaustion percentage in heterogeneous populations with R2 = 1.

Source article: Nucleic Acid Turnover and Lipid Remodeling Distinguish T Cell Activation and Exhaustion States via Label-Free Raman Spectroscopy

ACS Sensors

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175

Evidence-backed gainPeer-reviewedHealth

Interpretable random survival forest model predicted all-cause mortality in adults with unrepaired PAH-CHD and stratified survival in both Eisenmenger and non-Eisenmenger subgroups where the ESC model did not.

Source article: Interpretable machine learning-enabled risk stratification for pulmonary arterial hypertension associated with unrepaired congenital heart disease: results from a national prospective registry

International Journal of Cardiology

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176

Evidence-backed gainPeer-reviewedHealth

In implant dentistry, MR-based dynamic navigation achieved sub-millimetric entry deviation and outperformed freehand technique, with up to 72% angular accuracy improvement for inexperienced operators and more conservative tissue removal in endodontic and prosthetic tasks.

Source article: Augmented and mixed reality as auxiliary tools in dental procedures and their integration with artificial intelligence: a scoping review

Journal of Dentistry

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177

Evidence-backed gainPeer-reviewedHealth

Systematic review finds AI and deep learning models achieve high accuracy on cytology and histopathology images and offer gains in consistency, speed, cost-effectiveness, and reduced pathologist workload in breast cancer screening and diagnosis.

Source article: Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era

International Journal of Breast Cancer

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178

Evidence-backed gainPeer-reviewedHealth

XGBoost models trained on 5798 participants identified depression risk among older adults with chronic diseases across cognitive impairment levels with accuracy up to 0.767 and good calibration.

Source article: Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status

Geriatric Nursing

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179

Evidence-backed gainPeer-reviewedHealth

Adding 913 conditional-GAN simulated ganglionic tiles to 461 real ganglionic and 1374 real aganglionic FCM tiles improved a CNN's ability to discriminate ganglionic bowel on an independent real-image test set of 590 aganglionic and 198 ganglionic tiles, raising accuracy from 75% to 84% and sensitivity from 78% to 91% .

Source article: GENERATIVE AI FOR HIRSCHSPRUNG DISEASE: CAN SYNTHETIC FLUORESCENCE CONFOCAL MICROSCOPY IMAGES ENHANCE INTRAOPERATIVE DETECTION OF GANGLIONIC BOWEL?

Journal of Pediatric Surgery

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180

Evidence-backed gainPeer-reviewedClimate

Physics-informed categorical chain differential model improved coupled prediction of four ash fusion temperatures, reducing deformation temperature error and eliminating physically impossible temperature inversions to support boiler safety and slagging-risk management.

Source article: Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

Bioresource Technology

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