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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 4 of 10

91
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
Reader signal

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92
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
Reader signal

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93
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
Reader signal

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94
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
Reader signal

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95
Evidence-backed gainPeer-reviewedHealth

FedMediFormer-XAI combined federated learning, multimodal transformers, diffusion augmentation and GNN drug recommendation to achieve 94.2% accuracy for diabetes prediction and NDCG 0.91 for personalized drug recommendation while enabling privacy-preserving training without sharing raw patient data.

Source article: FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Journal of Visualized Experiments
Reader signal

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96
Evidence-backed gainPeer-reviewedHealth

An XGBoost model using 43 routine preoperative variables estimated 1-year mortality after total knee and hip arthroplasty with AUROC 0.761 and stable calibration, stratifying patients so the top 5% had 6.2-fold higher mortality than baseline.

Source article: Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Arthroplasty
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97
Evidence-backed gainPeer-reviewedHealth

AI and complementary digital health technologies improved travel healthcare by enhancing pre-travel risk prediction, enabling earlier outbreak detection during travel, and increasing diagnostic accuracy after travel.

Source article: Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review

Journal of Travel Medicine
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98
Evidence-backed gainPeer-reviewedHealth

XGBoost model predicted PMOS status from detailed body-composition measures with AUC 0.701 in testing, with SHAP highlighting regional fat masses as top predictors.

Source article: Causal adiposity and clinical validation of regional fat distribution in PMOS: a multi-modal analysis integrating GBD 2021, Mendelian randomization, and machine learning

International Journal of Obesity
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99
Evidence-backed gainPeer-reviewedHealth

Protocol proposes AI-based DyVe-X software to continuously quantify exertional dyspnoea against work rate and ventilation and to identify excessive and constrained breathing patterns during incremental CPET, with anticipated superior performance over peak breathing reserve criterion.

Source article: A protocol for validation of novel artificial intelligence-based framework for dyspnoea investigation with cardiopulmonary exercise testing

ERJ Open Research
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100
Evidence-backed gainPeer-reviewedHealth

An ensemble deep learning model combining AP and lateral skull X-rays detected skull fractures in neonates and infants with 91.6% accuracy and 0.938 AUC on external validation, improving diagnostic accuracy while reducing need for CT.

Source article: Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model

Journal of Korean Medical Science
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101
Evidence-backed gainPeer-reviewedHealth

Integrating surface-enhanced Raman spectroscopy with a support vector machine model enabled rapid species-level identification of seven clinically common Nocardia spp. at 99.47% accuracy to guide clinical treatment.

Source article: Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints

World Journal of Microbiology and Biotechnology
Reader signal

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102
Evidence-backed gainPeer-reviewedHealth

A logistic regression model combining grayscale ultrasound, contrast-enhanced ultrasound, and serum TPO-Ab improved differentiation of benign versus malignant thyroid nodules in Hashimoto's thyroiditis, reaching cross-validated AUC 0.849 with 77.3% sensitivity and 77.8% specificity.

Source article: A Multiparametric Approach Integrating Multimodal Ultrasound and Serum TPO-Ab for Differentiating Thyroid Carcinoma in Hashimoto's Thyroiditis: A Comparative Diagnostic Study with Internal Validation

Journal of Ultrasound in Medicine
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103
Evidence-backed gainPeer-reviewedHealth

Dynamic machine learning models using vital signs, lab trends and unstructured data can detect sepsis-related deterioration hours before clinical decompensation in critically ill cancer patients, supporting a shift from reactive to predictive onco-critical care.

Source article: Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care

World Journal of Critical Care Medicine
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104
Evidence-backed gainPeer-reviewedHealth

Automation and AI applied to ventilator management support waveform analysis, asynchrony detection, and weaning-readiness prediction to enable more individualized lung-protective care within predefined safety limits.

Source article: Physiology-guided mechanical ventilation: Monitoring, proportional assist, and bounded automation

World Journal of Critical Care Medicine
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105
Evidence-backed gainPeer-reviewedHealth

NDIB-Sim achieved high-fidelity long-term brain activity prediction from short-term observations with functional connectivity similarity above 0.97 and enabled subject-specific parameters that distinguish Alzheimer's patients from cognitively normal individuals.

Source article: NDIB-Sim: A Multimodal Bidirectional PINN Model for Simulating Brain Dynamics

IEEE Transactions on Neural Networks and Learning Systems
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106
Evidence-backed gainPeer-reviewedHealth

GIN-CRC-Pareto improved identification of miRNA-mRNA interactions in colorectal cancer, achieving 0.909 accuracy and 0.969 AUC on binding pair prediction and outperforming existing tools.

Source article: GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer

Journal of Biomedical Informatics
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107
Evidence-backed gainPeer-reviewedHealth

H2O AutoML trained on 4713 MESA participants using 21 selected predictors achieved higher discrimination than logistic regression and traditional ML models, reaching AUC 0.882 and accuracy 0.864, intended as a practical tool for clinician risk stratification.

Source article: Improving 10-year cardiovascular disease risk prediction using automated machine learning

International Journal of Cardiology
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108
Evidence-backed gainPeer-reviewedHealth

An ultrasound-based habitat subregional radiomics model using SVM achieved strong external validation for preoperative prediction of invasive breast cancer with concomitant DCIS, supporting preoperative risk stratification.

Source article: Ultrasound Habitat Radiomics for Preoperative Prediction of Invasive Breast Cancer With a DCIS Component: A Dual-center Retrospective Study

Ultrasound in Medicine & Biology
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109
Evidence-backed gainPeer-reviewedHealth

Elastic Net model distinguished three depressive symptom trajectories in older adults with chronic conditions and was deployed as an interactive web-based risk calculator for individualized risk profiling.

Source article: Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study

International Psychogeriatrics
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110
Evidence-backed gainPeer-reviewedHealth

Interpretable machine learning combining admission D-dimer and total bleeding volume improved prediction of 12-month functional outcome after aneurysmal subarachnoid hemorrhage, with XGBoost achieving AUC 0.904 and combined D-dimer+TBV+Hunt-Hess outperforming single markers, to inform early risk stratification.

Source article: Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

Neurocritical Care
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111
Evidence-backed gainPeer-reviewedHealth

A CECT-based 2PI imaging scoring system combined with clinical parameters enabled a Random Survival Forest model to stratify solitary HCC patients into high- and low-risk recurrence groups and predict postoperative recurrence.

Source article: A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study

Cancer Science
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112
Evidence-backed gainPeer-reviewedHealth

Cases without consensus were excluded from the AI accuracy analysis. AI accuracy was calculated as the proportion of correct classifications among consensus cases, and the two models were compared using McNemar's test with continuity correction.

Source article: Accuracy of General-Use Multimodal AI Platforms for Pell and Gregory Classification of Impacted Mandibular Third Molars

Cureus
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113
Evidence-backed gainPeer-reviewedHealth

A 17-gene diagnostic signature showed high accuracy for MDS detection (AUC = 0.963). Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed.

Source article: Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes

Hematology
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114
Evidence-backed gainPeer-reviewedHealth

Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients: Based on the above evaluation indicators, the BPNN model had the best performance, with an F1 score of 0.95, an accuracy rate of 94.87%, and an AUC of 0.9738.

Source article: Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients

Journal of Clinical Laboratory Analysis
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115
Evidence-backed gainPeer-reviewedHealth

Our method outperforms current algorithms in ultrasound PCa data, achieving mean Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of 83.6 ± 3.1%, 71.8 ± 2.5%, and 83.5 ± 3.1%, respectively.

Source article: A polyline searching-driven evolutionary AI for disease detection of medical imaging data

Physics in Medicine & Biology
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116
Evidence-backed gainPeer-reviewedHealth

Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis: Work in this area would benefit from closer attention to generalizability, clinical relevance and the existing reporting guidelines.

Source article: Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis

Medeniyet Medical Journal
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117
Evidence-backed gainPeer-reviewedHealth

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study: In internal validation (Stage I), it achieved an accuracy of 0.980 (95% CI 0.977-0.983) and an ROC-AUC of 0.995 (95% CI 0.994-0.997).

Source article: Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study

The Journal of Pathology: Clinical Research
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118
Evidence-backed gainPeer-reviewedHealth

Artificial intelligence-based algorithms improve quantification of breast arterial calcifications detected on screening mammography and strengthen their prognostic value for cardiovascular risk in women.

Source article: [Breast arterial calcifications and cardiovascular risk in women]

Giornale Italiano di Cardiologia
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119
Evidence-backed gainPeer-reviewedHealth

Plasma proteomics XGBoost models with SHAP-selected markers improved discrimination of prevalent osteoporosis and prediction of incident osteoporosis from baseline samples in UK Biobank.

Source article: Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank

The FASEB Journal
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120
Evidence-backed gainPeer-reviewedHealth

Machine learning and related SMART technologies improved early diagnosis and monitoring of obesity by predicting metabolic risk and automating adipose tissue measurement, and were linked to meaningful weight loss after robotic bariatric procedures.

Source article: Technology and Obesity: A Year in Review

Advances in Therapy
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