On the public citation graph

Investigation on the Abilities of Artificial Intelligence Methods to Accommodate Bias in Medical Diagnostic Data

https://doi.org/10.2139/ssrn.4676406
0 signed · 73 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ejhf.2675openalex-extracted
Biomarkers in heart failure clinical trials. A review from the Biomarkers Working Group of the Heart Failure Association of the European Society of Cardiology
inferredcites →https://doi.org/10.1007/978-1-4842-3790-8_4openalex-extracted
Training Neural Networks
inferredcites →https://doi.org/10.1007/978-981-19-1223-8_4openalex-extracted
Machine Learning in Medical Imaging – Clinical Applications and Challenges in Computer Vision
inferredcites →https://doi.org/10.1007/978-981-19-1223-8_9openalex-extracted
Limitations
inferredcites →https://doi.org/10.1007/s12020-022-03269-4openalex-extracted
Clinical evaluation of malignancy diagnosis of rare thyroid carcinomas by an artificial intelligent automatic diagnosis system
inferredcites →https://doi.org/10.1007/s12035-019-01856-7openalex-extracted
Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study
inferredcites →https://doi.org/10.1007/s12652-021-03091-2openalex-extracted
MicroRNA expression classification for pediatric multiple sclerosis identification
inferredcites →https://doi.org/10.1007/s13246-020-00865-4openalex-extracted
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
inferredcites →https://doi.org/10.1016/j.acra.2019.10.001openalex-extracted
Artificial Intelligence in Medicine: Where Are We Now?
inferredcites →https://doi.org/10.1016/j.compbiomed.2019.103495openalex-extracted
Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI
inferredcites →https://doi.org/10.1016/j.compbiomed.2020.103795openalex-extracted
Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compbiomed.2020.104037openalex-extracted
Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation
inferredcites →https://doi.org/10.1016/j.compbiomed.2021.104416openalex-extracted
Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography
inferredcites →https://doi.org/10.1016/j.ejrad.2020.109041openalex-extracted
Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study
inferredcites →https://doi.org/10.1016/j.eswa.2021.115658openalex-extracted
Machine learning model for diagnostic method prediction in parasitic disease using clinical information
inferredcites →https://doi.org/10.1016/j.eswa.2022.117592openalex-extracted
SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning
inferredcites →https://doi.org/10.1016/j.eswa.2023.120312openalex-extracted
DED: Diagnostic Evidence Distillation for acne severity grading on face images
inferredcites →https://doi.org/10.1016/j.eswa.2023.120450openalex-extracted
A novel image-to-knowledge inference approach for automatically diagnosing tumors
inferredcites →https://doi.org/10.1016/j.imu.2020.100360openalex-extracted
A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2
inferredcites →https://doi.org/10.1016/j.ins.2018.01.051openalex-extracted
Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
inferredcites →https://doi.org/10.1016/j.jbi.2024.104692openalex-extracted
Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals
inferredcites →https://doi.org/10.1016/j.jbi.2024.104693openalex-extracted
Recommendations to promote fairness and inclusion in biomedical AI research and clinical use
inferredcites →https://doi.org/10.1016/j.msard.2022.103673openalex-extracted
Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review
inferredcites →https://doi.org/10.1016/j.nicl.2019.102003openalex-extracted
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
inferredcites →https://doi.org/10.1016/j.ophtha.2018.04.020openalex-extracted
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs
inferredcites →https://doi.org/10.1016/j.zemedi.2018.10.004openalex-extracted
Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics
inferredcites →https://doi.org/10.1038/s41598-018-33013-wopenalex-extracted
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
inferredcites →https://doi.org/10.1038/s41598-018-33077-8openalex-extracted
Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy
inferredcites →https://doi.org/10.1038/s41598-023-33303-yopenalex-extracted
Collaborative training of medical artificial intelligence models with non-uniform labels
inferredcites →https://doi.org/10.1038/s41746-021-00438-zopenalex-extracted
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
inferredcites →https://doi.org/10.1055/a-1556-5984openalex-extracted
Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective nonrandomized comparative study
inferredcites →https://doi.org/10.1073/pnas.1919012117openalex-extracted
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
inferredcites →https://doi.org/10.1080/01972243.2017.1354593openalex-extracted
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
inferredcites →https://doi.org/10.1088/1752-7163/abd080openalex-extracted
Detecting multiple sclerosis via breath analysis using an eNose, a pilot study
inferredcites →https://doi.org/10.1093/bmb/ldaa012openalex-extracted
The potential application of artificial intelligence for diagnosis and management of glaucoma in adults
inferredcites →https://doi.org/10.1093/jamia/ocab238openalex-extracted
Trust in AI: why we should be designing for APPROPRIATE reliance
inferredcites →https://doi.org/10.1093/jamia/ocac070openalex-extracted
An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes
inferredcites →https://doi.org/10.1093/jamia/ocad088openalex-extracted
Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework
inferredcites →https://doi.org/10.1093/jamia/ocae165openalex-extracted
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
inferredcites →https://doi.org/10.1093/jamia/ocae209openalex-extracted
Toward a responsible future: recommendations for AI-enabled clinical decision support
inferredcites →https://doi.org/10.1097/ijg.0000000000000988openalex-extracted
Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity
inferredcites →https://doi.org/10.1101/2022.01.24.22269730openalex-extracted
Diagnostic accuracy of chest X-ray interpretation for tuberculosis by three artificial intelligence-based software in a screening use-case: an individual patient meta-analysis of global data
inferredcites →https://doi.org/10.1109/access.2021.3069191openalex-extracted
Multimodal AI System for the Rapid Diagnosis and Surgical Prediction of Necrotizing Enterocolitis
inferredcites →https://doi.org/10.1109/icdmw.2009.83openalex-extracted
Building Classifiers with Independency Constraints
inferredcites →https://doi.org/10.1111/cup.13954openalex-extracted
Artificial intelligence in dermatopathology: Diagnosis, education, and research
inferredcites →https://doi.org/10.1111/joim.12822openalex-extracted
eD octor: machine learning and the future of medicine
inferredcites →https://doi.org/10.1136/bmjqs-2018-008370openalex-extracted
Artificial intelligence, bias and clinical safety
inferredcites →https://doi.org/10.1136/svn-2017-000101openalex-extracted
Artificial intelligence in healthcare: past, present and future
inferredcites →https://doi.org/10.1145/3357777.3357793openalex-extracted
Fusing of Medical Images and Reports in Diagnostics of Brain Diseases
inferredcites →https://doi.org/10.1148/radiol.2020192224openalex-extracted
Preparing Medical Imaging Data for Machine Learning
inferredcites →https://doi.org/10.1155/2019/4061313openalex-extracted
Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images
inferredcites →https://doi.org/10.1161/circresaha.121.319916openalex-extracted
Sexual Dimorphism in Cardiovascular Biomarkers: Clinical and Research Implications
inferredcites →https://doi.org/10.1167/iovs.17-23387openalex-extracted
Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression
inferredcites →https://doi.org/10.1177/0840470419873123openalex-extracted
Artificial intelligence in healthcare: An essential guide for health leaders
inferredcites →https://doi.org/10.1177/2048872616661693openalex-extracted
Editor’s Choice - Impact of initial hospital diagnosis on mortality for acute myocardial infarction: A national cohort study
inferredcites →https://doi.org/10.1186/s12947-021-00261-2openalex-extracted
Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis
inferredcites →https://doi.org/10.1186/s13073-021-00968-xopenalex-extracted
Deep learning in cancer diagnosis, prognosis and treatment selection
inferredcites →https://doi.org/10.1253/circj.cj-20-1121openalex-extracted
Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases
inferredcites →https://doi.org/10.1371/journal.pone.0216410openalex-extracted
Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques
inferredcites →https://doi.org/10.1373/clinchem.2013.202531openalex-extracted
Biomarkers in ACS and Heart Failure: Should Men and Women Be Interpreted Differently?
inferredcites →https://doi.org/10.1515/cclm-2022-0291openalex-extracted
Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis
inferredcites →https://doi.org/10.23736/s2784-8671.20.06753-xopenalex-extracted
Artificial intelligence for melanoma diagnosis
inferredcites →https://doi.org/10.31083/j.rcm2204121openalex-extracted
Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future
inferredcites →https://doi.org/10.32614/cran.package.treeopenalex-extracted
tree: Classification and Regression Trees
inferredcites →https://doi.org/10.3389/fneur.2019.00781openalex-extracted
Computational Intelligence Technique for Prediction of Multiple Sclerosis Based on Serum Cytokines
inferredcites →https://doi.org/10.3390/cancers14122897openalex-extracted
Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets
inferredcites →https://doi.org/10.3390/diagnostics12020309openalex-extracted
Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis
inferredcites →https://doi.org/10.3390/e22050517openalex-extracted
Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features
inferredcites →https://doi.org/10.3390/sym12040651openalex-extracted
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
inferredcites →https://doi.org/10.3748/wjg.v28.i20.2152openalex-extracted
Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease
inferredcites →https://doi.org/10.5124/jkma.2020.63.11.696openalex-extracted
Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence
inferredcites →https://doi.org/10.5858/arpa.2022-0066-oaopenalex-extracted
Clinical Validation of Artificial Intelligence–Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection
inferredcites →https://doi.org/10.7150/ijbs.58855openalex-extracted
Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.