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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 9 of 20

241
Evidence-backed gainPeer-reviewedClimate

Domain-specific EfficientNet-B0 classifier and Claude Vision API with human-in-the-loop guidance automated morphological characterization of microplastics from optical microscope images, achieving high F1-scores for shape/type, color and texture.

Source article: Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Scientific Reports
Reader signal

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243
Evidence-backed gainPeer-reviewedHealth

Adding individual-level social determinants of health and clinical features to machine learning models improved suicide risk prediction over demographic-only baselines in a Maryland sample of 1214 suicide deaths and 815,544 living patients.

Source article: Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models

Archives of Suicide Research
Reader signal

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244
Evidence-backed gainPeer-reviewedHealth

An unsupervised AI framework discovered a 13-marker cellular morphometric signature from colorectal whole-slide images that transferred to gastric and esophageal cancers and enabled risk stratification of precancerous lesions and early-stage cancers to guide surveillance and intervention.

Source article: Tissue-Agnostic Cellular Morphometric Biomarkers for Risk-Adapted Management Across Gastrointestinal Precancerous Lesions and Cancers

Advanced Science
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245
Evidence-backed gainPeer-reviewedCrime

Supervised logistic regression distinguished dominant versus non-dominant handwriting with 92.98% test accuracy using execution-quality features, providing quantitative benchmarks to support forensic evaluation of suspected off-hand disguise.

Source article: Forensic analysis of dominant versus non-dominant handwriting: Statistical and machine learning insights

Journal of Forensic Sciences
Reader signal

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246
Evidence-backed gainPeer-reviewedHealth

Machine learning and deep learning models trained on MALDI-TOF spectra improved rapid classification of bacteria versus viruses, Gram-positive versus Gram-negative, and individual species, with Extra Trees showing best generalization to an external highly pathogenic bacteria dataset.

Source article: Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence

Scientific Reports
Reader signal

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247
Evidence-backed gainPeer-reviewedHealth

An ITO/CuBi2O4/LaNiO3 heterojunction sensor with machine learning analysis identified 9 biocide types and 5 ethanol concentration gradients with 98.7% overall accuracy.

Source article: Dynamic liquid with shape-shifting induced photocurrent variation in a CuBi<sub>2</sub>O<sub>4</sub> film for antimicrobial biocide recognition

Nanoscale
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248
Evidence-backed gainPeer-reviewedHealth

An interpretable machine learning model integrating genetic risk scores, corneal biomechanical parameters, and tear molecular biomarkers was developed and validated to predict progression risk in patients with confirmed normal-tension glaucoma, providing a quantitative reference for risk stratification and personalised

Source article: A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics

Seminars in Ophthalmology
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249
Evidence-backed gainPeer-reviewedBusiness

Integration of AI and blockchain promises enhanced resource efficiency, optimized supply chains, and improved product lifecycle management to advance the circular economy.

Source article: Revolutionizing the circular economy through new technologies: A new era of sustainable progress

Environmental Technology &amp; Innovation
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250
Evidence-backed gainPeer-reviewedEducation

AI/ML integration in adaptive e-learning systems personalizes learning experiences and optimizes learning paths, leading to higher student engagement, retention, and academic performance including increased test scores.

Source article: Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Education Sciences
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251
Evidence-backed gainPeer-reviewedHealth

Integrated single-cell analysis and three machine learning algorithms identified GLS and LPIN2 as upregulated key genes in kidney stones that promote fibroblast-to-myofibroblast transition, ROS production, calcium deposition and osteogenic-like differentiation, supporting a risk prediction nomogram and drug prediction.

Source article: Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

Urolithiasis
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252
Evidence-backed gainPeer-reviewedHealth

A 16-gene macrophage-associated signature developed with ridge regression and XGBoost stratified HCC patients and achieved high diagnostic discrimination, with LGALS1 emerging as a top feature linked to survival and treatment response.

Source article: Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Translational Oncology
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253
Evidence-backed gainPeer-reviewedHealth

An interpretable logistic regression model trained on YRBS data predicted lifetime marijuana use among male high school students with high accuracy, enabling early identification of at-risk boys for targeted school and community prevention.

Source article: Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health

Public Health
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254
Evidence-backed gainPeer-reviewedHealth

A model combining clinical and sociodemographic variables predicted overall survival in cervical squamous cell carcinoma with acceptable discrimination.

Source article: Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data

American Journal of Clinical Oncology
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255
Evidence-backed gainPeer-reviewedSports

Using the fixed threshold (score > 5 = survival), clinicians achieved higher overall accuracy than AI (75.6% periodontists, 74.9% GDs, 69.2% AI; p < 0.05), with sensitivity low and comparable across groups (14.7%-22.7%).

Source article: Can Artificial Intelligence Match Human Expertise in Long-Term Periodontal Prognosis? A Comparative Accuracy Study

Journal of Clinical Periodontology
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256
Evidence-backed gainPeer-reviewedHealth

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy: Results IMIL-Net achieved the highest diagnostic performance, with a mean area under the curve (AUC) of 0.975 (95% CI 0.959-0.991), accuracy of 0.915 (95% CI 0.883-0.947), sensitivity of 0.876 (95% CI 0.803-0.949), and specificity of 0.945 (95% CI 0.910-0.980).

Source article: An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

European Archives of Oto-Rhino-Laryngology
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257
Evidence-backed gainPeer-reviewedLabor

Technology-Enabled Interventions for Preventing and Responding to Elder Abuse: A Review of the Current Evidence: Although positive results for intermediate outcomes (e.g., service provision) were observed, evidence of alleviation of AOP occurrence and improvement of victim health remains limited.

Source article: Technology-Enabled Interventions for Preventing and Responding to Elder Abuse: A Review of the Current Evidence

Trauma, Violence, &amp; Abuse
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258
Evidence-backed gainPeer-reviewedHealth

Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. Results Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (P 1.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs Conclusion Deep learning-based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.

Source article: Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Journal of Cardiovascular Imaging
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259
Evidence-backed gainPeer-reviewedHealth

Statistically significant improvements were observed in 10 of 12 clinical domains, including core consultation skills such as comprehensive history taking, identifying key symptoms, adapting questioning and formulating a management plan, and differential diagnoses (all p Conclusion AI-simulated patients are feasible to implement and are associated with meaningful improvements in consultation confidence among CMTs.

Source article: AI patients, real practice: exploring the use of AI-simulated patients to support primary healthcare training for combat medical technicians

Education for Primary Care
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260
Evidence-backed gainPeer-reviewedHealth

Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning: The best single-segment performance was achieved in the left mandibular corpus with Logistic Regression (Accuracy=0.833, F1=0.832, AUC=0.958).

Source article: Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning

Dentomaxillofacial Radiology
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261
Evidence-backed gainPeer-reviewedScience

Optimal Initialization Scale for Neural Networks With Locally Quadratic Loss Landscapes: An SGD Dynamics Perspective: The results show that, for the simple network model, if the variance of the initialization distribution satisfies our theoretical optimal condition, then the corresponding network achieves lower final training loss and higher test accuracy than the conventional He-normal initialization.

Source article: Optimal Initialization Scale for Neural Networks With Locally Quadratic Loss Landscapes: An SGD Dynamics Perspective

IEEE Transactions on Neural Networks and Learning Systems
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262
Evidence-backed gainPeer-reviewedLifestyle

Redefining the design innovation process: Embedding sustainability through AI-generated camping cookware: In addition, AIGC can accurately reproduce design schemes, improve design efficiency and promote creativity generation..

Source article: Redefining the design innovation process: Embedding sustainability through AI-generated camping cookware

PLOS One
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263
Evidence-backed gainPeer-reviewedHealth

Computational AI analysis of HRCT provides automated, objective quantification of interstitial lung disease in patients with inflammatory rheumatic disorders, enabling precise volumetric measurement and pattern classification.

Source article: Beyond Visual Scoring: Computational CT-analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Arthritis Care &amp; Research
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264
Evidence-backed gainPeer-reviewedHealth

Deep-learning based AIIR reconstruction improved CT image quality and diagnostic accuracy for gastric cancer, increasing tumor conspicuity and raising AUC for detecting serosal invasion compared to hybrid iterative reconstruction.

Source article: Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy

Abdominal Radiology
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265
Evidence-backed gainPeer-reviewedHealth

An integrated framework using WBAF preprocessing, MResU-Net segmentation, IPHOG feature extraction and IShuffleNet-PCNN classification achieved 0.933 accuracy and 0.991 NPV for spinal cord injury-related fracture classification from CT images.

Source article: IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images

European Spine Journal
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266
Evidence-backed gainPeer-reviewedHealth

A CNN with contrastive learning and mixed-attention fusion of multimodal MRI and demographic data achieved test set ACC 0.855 and AUC 0.919 for preoperative identification of pineal region germinoma, outperforming single-modality models.

Source article: A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma

Neuroradiology
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267
Evidence-backed gainPeer-reviewedHealth

Automated fundus-image analysis for diabetic retinopathy enables earlier screening and classification that can prevent vision loss in diabetic patients.

Source article: A comprehensive review on automated diabetic retinopathy detection and classification using fundus image

Graefe's Archive for Clinical and Experimental Ophthalmology
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268
Evidence-backed gainPeer-reviewedClimate

Integrating an NDVI-based Productivity Index with regional prediction uncertainty to select four field samples reduced mean field-scale SOC prediction error from 0.24% to 0.18% RMSE, achieving accuracy comparable to using all available field observations.

Source article: Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

Environmental Monitoring and Assessment
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269
Evidence-backed gainPeer-reviewedHealth

Unsupervised hierarchical clustering integrated BMI, activity, comorbidities, HAQ and TNF pathway genetics to identify three RA subgroups with differing TNFi response rates, including a better-prognosis cluster with 73.5% response.

Source article: Clustering of rheumatoid arthritis patients: an unsupervised machine learning approach for characterizing the TNFi response

Immunologic Research
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270
Evidence-backed gainPeer-reviewedHealth

Psychiatric nursing internship students who used the ChatGPT-based virtual patient with a structured prompting framework showed higher MSE competency after the intervention.

Source article: An AI-Supported Virtual Patient Application Using a Structured Prompting Framework to Develop Mental Status Examination Skills in Psychiatric Nursing Internship Students: An Interventional Mixed-Methods Study

Issues in Mental Health Nursing
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