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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 11 of 20

301
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
Reader signal

How should this claim be treated?

302
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
Reader signal

How should this claim be treated?

303
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
Reader signal

How should this claim be treated?

304
Evidence-backed gainPeer-reviewedMedia & Arts

In a Swiss survey experiment, readers judged AI-assisted and fully AI-generated news excerpts as comparable to human-written excerpts on credibility, readability and expertise, and reported higher immediate willingness to keep reading after AI involvement was disclosed.

Source article: Quality perceptions and intended engagement in response to AI-generated and AI-assisted news

Scientific Reports
Reader signal

How should this claim be treated?

305
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
Reader signal

How should this claim be treated?

307
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
Reader signal

How should this claim be treated?

308
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
Reader signal

How should this claim be treated?

309
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 & Engineering Express
Reader signal

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310
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
Reader signal

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311
Evidence-backed gainPeer-reviewedHealth

Pooled testing of deep learning models for subdural hematoma detection on non-contrast CT achieved high diagnostic performance, with U-Net models showing significantly higher sensitivity and precision than other architectures.

Source article: The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis

Neurosurgical Review
Reader signal

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312
Evidence-backed gainPeer-reviewedHealth

A microorganism-based random forest model built from plasma metagenomic profiles predicted subsequent infection in newly diagnosed hematological patients with AUC 0.942, identifying 99.1% of those who later developed infections, and improved to AUC 0.953 when combined with clinical metrics, supporting targeted prophyl-

Source article: Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions

Infectious Diseases and Therapy
Reader signal

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313
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
Reader signal

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314
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
Reader signal

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315
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
Reader signal

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316
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
Reader signal

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317
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
Reader signal

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318
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
Reader signal

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319
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
Reader signal

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320
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
Reader signal

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321
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
Reader signal

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324
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
Reader signal

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325
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
Reader signal

How should this claim be treated?

326
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
Reader signal

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327
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
Reader signal

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328
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
Reader signal

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329
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
Reader signal

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330
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
Reader signal

How should this claim be treated?