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Good Space a ranking of the record

TheGood surrounding AI

278 gains match this view. 97% rest on at least one peer-reviewed study, and 3% clear the high-evidence bar. The latest was filed Oct 16, 2026.

Every gain in this view, one square each, shaded by the strength of its evidence. High 9 Moderate 269 Emerging 0
278 results
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Sector: health

Matching records · page 7 of 10

181
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
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182
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
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183
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
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184
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
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185
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
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186
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
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187
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
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188
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
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189
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
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190
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
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191
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
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192
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
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193
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
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194
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
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195
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
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196
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
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197
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
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198
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
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199
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
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200
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
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201
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
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202
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
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203
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
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204
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
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205
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
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206
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
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207
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
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209
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
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210
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
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