Artificial intelligence bias in medical system designs: a systematic review

[1]  Sardar M. N. Islam,et al.  6G-Enabled IoT and AI for Smart Healthcare , 2023 .

[2]  Rachna Jain,et al.  Explaining sentiment analysis results on social media texts through visualization , 2023, Multimedia Tools and Applications.

[3]  J. Suri,et al.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine , 2022, Cancers.

[4]  K. Camphausen,et al.  Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets , 2022, Cancers.

[5]  Jeffrey G. Klann,et al.  An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes , 2022, J. Am. Medical Informatics Assoc..

[6]  David W. Sobel,et al.  Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0 , 2022, Computers in Biology and Medicine.

[7]  Alvaro Fernandez-Quilez Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability , 2022, AI and Ethics.

[8]  G. Pontone,et al.  The emerging role of atrial strain assessed by cardiac MRI in different cardiovascular settings: an up-to-date review , 2022, European Radiology.

[9]  Jonathan I. Tamir,et al.  Implicit data crimes: Machine learning bias arising from misuse of public data , 2022, Proceedings of the National Academy of Sciences of the United States of America.

[10]  L. Celi,et al.  Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review , 2022, PLOS digital health.

[11]  J. Suri,et al.  A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review , 2022, Diagnostics.

[12]  Pankaj K. Jain,et al.  Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models , 2022, Diagnostics.

[13]  M. Figl,et al.  Technical note: A PET/MR coil with an integrated, orbiting 511 keV transmission source for PET/MR imaging validated in an animal study , 2022, Medical physics.

[14]  L. Belenguer AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry , 2022, AI and Ethics.

[15]  Gaurav Kumar Nayak,et al.  An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review , 2022, Comput. Biol. Medicine.

[16]  J. Suri,et al.  Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review , 2022, Diagnostics.

[17]  J. Suri,et al.  Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review , 2022, Comput. Biol. Medicine.

[18]  Sean L. Hill,et al.  Conceptualising fairness: three pillars for medical algorithms and health equity , 2022, BMJ Health & Care Informatics.

[19]  Jasjit S. Suri,et al.  A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study , 2021, Comput. Biol. Medicine.

[20]  Matthew B. A. McDermott,et al.  Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations , 2021, Nature Medicine.

[21]  Md. Raihan Mia,et al.  A privacy-preserving National Clinical Data Warehouse: Architecture and analysis , 2021, Smart Health.

[22]  Jinseok Lee,et al.  Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19 , 2021, Frontiers in Physiology.

[23]  F. Cabitza,et al.  Machine Learning for Health: Algorithm Auditing & Quality Control , 2021, Journal of Medical Systems.

[24]  L. Celi,et al.  Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias. , 2021, British journal of anaesthesia.

[25]  M. Ortega,et al.  Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? , 2021, BMC Medical Research Methodology.

[26]  J. Suri,et al.  Atrial Strain by Feature-Tracking Cardiac Magnetic Resonance Imaging in Takotsubo Cardiomyopathy. Features, Feasibility, and Reproducibility , 2021, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[27]  Athina Tzovara,et al.  Addressing bias in big data and AI for health care: A call for open science , 2021, Patterns.

[28]  Charles E. Kahn,et al.  A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI , 2021, Nature Medicine.

[29]  Iam Palatnik de Sousa,et al.  Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers , 2021, Sensors.

[30]  Filippo Cademartiri,et al.  Emerging role of artificial intelligence in stroke imaging , 2021, Expert review of neurotherapeutics.

[31]  Matthew D. Byrne Reducing Bias in Healthcare Artificial Intelligence. , 2021, Journal of perianesthesia nursing : official journal of the American Society of PeriAnesthesia Nurses.

[32]  W. Holmes,et al.  Disentangling prevalence induced biases in medical image decision-making , 2021, Cognition.

[33]  Florin C. Ghesu,et al.  Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training , 2021, European Radiology.

[34]  Christopher M. Danforth,et al.  Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance , 2021, ACM Trans. Comput. Heal..

[35]  Shira Mitchell,et al.  Algorithmic Fairness: Choices, Assumptions, and Definitions , 2021, Annual Review of Statistics and Its Application.

[36]  Beatriz Garcia Santa Cruz,et al.  Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem , 2021, Medical Image Analysis.

[37]  Chuizheng Meng,et al.  MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset , 2021, ArXiv.

[38]  Jeffrey G. Klann,et al.  Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data , 2021, J. Am. Medical Informatics Assoc..

[39]  Juan Lavista Ferres,et al.  Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary , 2021, GoodIT.

[40]  R. Socher,et al.  Deep learning-enabled medical computer vision , 2021, npj Digital Medicine.

[41]  Gurjit Singh Walia,et al.  Recent trends in multicue based visual tracking: A review , 2020, Expert Syst. Appl..

[42]  Isabel Straw,et al.  The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future , 2020, Artif. Intell. Medicine.

[43]  Mainak Biswas,et al.  COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review , 2020, Computers in Biology and Medicine.

[44]  Nigam H. Shah,et al.  An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction , 2020, J. Biomed. Informatics.

[45]  Jasjit S. Suri,et al.  Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm , 2020, Comput. Biol. Medicine.

[46]  E. Guney,et al.  Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare , 2020, npj Digital Medicine.

[47]  Diego H. Milone,et al.  Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis , 2020, Proceedings of the National Academy of Sciences.

[48]  M. Pantic,et al.  Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study , 2020, Image Vis. Comput..

[49]  Gary S Collins,et al.  Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness , 2020, BMJ.

[50]  Haoran Zhang,et al.  Hurtful words: quantifying biases in clinical contextual word embeddings , 2020, CHIL.

[51]  Chris Russell,et al.  Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI , 2020, Comput. Law Secur. Rev..

[52]  Maria Noelle Noriega The application of artificial intelligence in police interrogations: An analysis addressing the proposed effect AI has on racial and gender bias, cooperation, and false confessions , 2020, Futures.

[53]  Varadraj Gurupur,et al.  Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare , 2020, Medicina.

[54]  S. Walsh,et al.  Patient gender bias on the diagnosis of idiopathic pulmonary fibrosis , 2020, Thorax.

[55]  Christian Wachinger,et al.  Detect and Correct Bias in Multi-Site Neuroimaging Datasets , 2020, Medical Image Anal..

[56]  Steffen Staab,et al.  Bias in data‐driven artificial intelligence systems—An introductory survey , 2020, WIREs Data Mining Knowl. Discov..

[57]  Marzyeh Ghassemi,et al.  CheXclusion: Fairness gaps in deep chest X-ray classifiers , 2020, PSB.

[58]  Bhramar Mukherjee,et al.  Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification , 2019, Biometrics.

[59]  Rifat Atun,et al.  Artificial intelligence and algorithmic bias: implications for health systems , 2019, Journal of global health.

[60]  Ravi B. Parikh,et al.  Addressing Bias in Artificial Intelligence in Health Care. , 2019, JAMA.

[61]  Brian W. Powers,et al.  Dissecting racial bias in an algorithm used to manage the health of populations , 2019, Science.

[62]  Lena Maier-Hein,et al.  BIAS: Transparent reporting of biomedical image analysis challenges , 2019, Medical Image Analysis.

[63]  Elmar Kotter,et al.  Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement , 2019, Insights into Imaging.

[64]  Eirini Ntoutsi,et al.  AdaFair: Cumulative Fairness Adaptive Boosting , 2019, CIKM.

[65]  Kristina Lerman,et al.  A Survey on Bias and Fairness in Machine Learning , 2019, ACM Comput. Surv..

[66]  Jessica L. Panman,et al.  Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison , 2019, Front. Neurosci..

[67]  Carlos Castillo,et al.  Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries , 2019, Front. Big Data.

[68]  Jeanna Neefe Matthews,et al.  Managing Bias in AI , 2019, WWW.

[69]  Jasjit S. Suri,et al.  The present and future of deep learning in radiology. , 2019, European journal of radiology.

[70]  F J Gilbert,et al.  Artificial intelligence in breast imaging. , 2019, Clinical radiology.

[71]  Catherine Tucker,et al.  Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads , 2019 .

[72]  Alexander Binder,et al.  Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.

[73]  S. Park,et al.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers , 2019, Korean journal of radiology.

[74]  Sendhil Mullainathan,et al.  Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People , 2019, FAT.

[75]  J. Denny,et al.  Artificial intelligence, bias and clinical safety , 2019, BMJ Quality & Safety.

[76]  S. Tamang,et al.  Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data , 2018, JAMA internal medicine.

[77]  Petia Radeva,et al.  Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review , 2018, Comput. Biol. Medicine.

[78]  Susan Leavy,et al.  Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning , 2018, 2018 IEEE/ACM 1st International Workshop on Gender Equality in Software Engineering (GE).

[79]  Tobias Berg,et al.  On the Rise of FinTechs – Credit Scoring Using Digital Footprints , 2018, The Review of Financial Studies.

[80]  Timnit Gebru,et al.  Datasheets for datasets , 2018, Commun. ACM.

[81]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[82]  Blake Lemoine,et al.  Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.

[83]  Jasjit S. Suri,et al.  Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization , 2017, Journal of Medical Systems.

[84]  Xin Zhang,et al.  TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.

[85]  Cathy O'Neil,et al.  Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification , 2017, Big Data.

[86]  Lu Zhang,et al.  Anti-discrimination learning: a causal modeling-based framework , 2017, International Journal of Data Science and Analytics.

[87]  Ayman El-Baz,et al.  Biomedical Image Segmentation : Advances and Trends , 2016 .

[88]  Krishna P. Gummadi,et al.  Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.

[89]  M. Hernán,et al.  ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions , 2016, British Medical Journal.

[90]  Paul De Hert,et al.  Expanding the European data protection scope beyond territory: Article 3 of the General Data Protection Regulation in its wider context , 2016 .

[91]  Tadashi Araki,et al.  PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology , 2016, Comput. Methods Programs Biomed..

[92]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[93]  Marco Tulio Ribeiro,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, HLT-NAACL Demos.

[94]  Jasjit S. Suri,et al.  Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm , 2015, Expert Syst. Appl..

[95]  Kristina Lerman,et al.  Leveraging Position Bias to Improve Peer Recommendation , 2014, PloS one.

[96]  U. Rajendra Acharya,et al.  Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization , 2013, Comput. Methods Programs Biomed..

[97]  Shahriar Akter,et al.  Algorithmic bias in data-driven innovation in the age of AI , 2021, Int. J. Inf. Manag..

[98]  Ann Kucera Jiri Stanley Smart Healthcare Devices and Applications, Machine Learning-based Automated Diagnostic Systems, and Real-Time Medical Data Analytics in COVID-19 Screening, Testing, and Treatment , 2021, American Journal of Medical Research.

[99]  Jasjit S Suri,et al.  State-of-the-art review on deep learning in medical imaging. , 2019, Frontiers in bioscience.