This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (83) Artificial Intelligence (1216) Machine Learning (1401) Systemic Racism and Racial Bias in Health Care and Society (72) Validity, Reliability, and Bias (89)

Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36388, first published .
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Authors of this article:

Jonathan Huang1 Author Orcid Image ;   Galal Galal1 Author Orcid Image ;   Mozziyar Etemadi1, 2 Author Orcid Image ;   Mahesh Vaidyanathan1, 3 Author Orcid Image
Article Authors Cited by (50) Tweetations (10) Metrics

Journals

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  3. Barton M, Hamza M, Guevel B. Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation. Cureus 2023 View
  4. Kim E, Jenness J, Miller A, Halabi R, de Zambotti M, Bagot K, Baker F, Pratap A. Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children. JAMA Network Open 2023;6(3):e235681 View
  5. Sahiner B, Chen W, Samala R, Petrick N. Data drift in medical machine learning: implications and potential remedies. The British Journal of Radiology 2023;96(1150) View
  6. Le J, Shashikumar S, Malhotra A, Nemati S, Wardi G. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape. Critical Care Clinics 2023;39(4):751 View
  7. Bays H, Fitch A, Cuda S, Gonsahn-Bollie S, Rickey E, Hablutzel J, Coy R, Censani M. Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obesity Pillars 2023;6:100065 View
  8. Khor S, Haupt E, Hahn E, Lyons L, Shankaran V, Bansal A. Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors. JAMA Network Open 2023;6(6):e2318495 View
  9. Rana S, Azizul Z, Awan A. A step toward building a unified framework for managing AI bias. PeerJ Computer Science 2023;9:e1630 View
  10. Schuch H, Furtado M, Silva G, Kawachi I, Chiavegatto Filho A, Elani H. Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults. JAMA Network Open 2023;6(11):e2341625 View
  11. Hooper S, Hecker K, Artemiou E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Veterinary Sciences 2023;10(9):537 View
  12. Allareddy V, Oubaidin M, Rampa S, Venugopalan S, Elnagar M, Yadav S, Lee M. Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthodontics & Craniofacial Research 2023;26(S1):124 View
  13. Balucan F, French B, Shi Y, Kripalani S, Vasilevskis E. Screening for the high-need population using single institution versus state-wide admissions discharge transfer feed. BMC Health Services Research 2023;23(1) View
  14. Gonzalez R, Saha A, Campbell C, Nejat P, Lokker C, Norgan A. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. Journal of Pathology Informatics 2024;15:100347 View
  15. Kumar R, Sood P, Nirala R, Ade R, Sonaji A. Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose. Journal for Research in Applied Sciences and Biotechnology 2023;2(5):51 View
  16. Banda J, Shah N, Periyakoil V. Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer’s and Parkinson’s diseases. JAMIA Open 2023;6(2) View
  17. Cary M, Zink A, Wei S, Olson A, Yan M, Senior R, Bessias S, Gadhoumi K, Jean-Pierre G, Wang D, Ledbetter L, Economou-Zavlanos N, Obermeyer Z, Pencina M. Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review. Health Affairs 2023;42(10):1359 View
  18. Risser L, Picard A, Hervier L, Loubes J. Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. Algorithms 2023;16(11):510 View
  19. Smith C, Weathers A, Lewis S. An overview of clinical machine learning applications in neurology. Journal of the Neurological Sciences 2023;455:122799 View
  20. El-Azab S, Nong P. Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice. Big Data & Society 2023;10(2) View
  21. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. SSRN Electronic Journal 2023 View
  22. Tudorie G. Reluctant Republic: A Positive Right for Older People to Refuse AI-Based Technology. Societies 2023;13(12):248 View
  23. Kerr W, McFarlane K. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Current Neurology and Neuroscience Reports 2023;23(12):869 View
  24. Koo C, Yang A, Welch C, Jadav V, Posch L, Thoreson N, Morris D, Chouhdry F, Szabo J, Mendelson D, Margolies L. Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation. Journal of Medical Imaging 2023;10(06) View
  25. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci 2023;6(1):3 View
  26. Dell’Aquila K, Vadlamani A, Maldjian T, Fineberg S, Eligulashvili A, Chung J, Adam R, Hodges L, Hou W, Makower D, Duong T. Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population. Breast Cancer Research 2024;26(1) View
  27. Ożegalska-Łukasik N, Łukasik S. Culturally Responsive Artificial Intelligence – Problems, Challenges and Solutions. Intercultural Relations 2023;7(2(14)):106 View
  28. Patel S, Baum A, Basu S. Prediction of non emergent acute care utilization and cost among patients receiving Medicaid. Scientific Reports 2024;14(1) View
  29. Davenport M, Sirrianni J, Chisolm D. Machine learning data sources in pediatric sleep research: assessing racial/ethnic differences in electronic health record–based clinical notes prior to model training. Frontiers in Sleep 2024;3 View
  30. Yao S, Dai F, Sun P, Zhang W, Qian B, Lu H. Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population. Nature Communications 2024;15(1) View
  31. Yang P, Gregory I, Robichaux C, Holder A, Martin G, Esper A, Kamaleswaran R, Gichoya J, Bhavani S. Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19. Critical Care Explorations 2024;6(3):e1059 View
  32. Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. Journal of the American Medical Informatics Association 2024;31(5):1172 View
  33. Liu Y, Joly R, Reading Turchioe M, Benda N, Hermann A, Beecy A, Pathak J, Zhang Y. Preparing for the bedside—optimizing a postpartum depression risk prediction model for clinical implementation in a health system. Journal of the American Medical Informatics Association 2024;31(6):1258 View
  34. Saldana C, Burkhardt E, Pennisi A, Oliver K, Olmstead J, Holland D, Gettings J, Mauck D, Austin D, Wortley P, Saldana Ochoa K. Development of a Machine Learning Modeling Tool for Predicting Human Immunodeficiency Virus Incidence Using Public Health Data From a County in the Southern United States. Clinical Infectious Diseases 2024 View
  35. Didier A, Nigro A, Noori Z, Omballi M, Pappada S, Hamouda D. Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis. Frontiers in Artificial Intelligence 2024;7 View
  36. Norris M, Obeid N, El‐Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. International Journal of Eating Disorders 2024;57(6):1357 View
  37. Tejani A, Ng Y, Xi Y, Rayan J. Understanding and Mitigating Bias in Imaging Artificial Intelligence. RadioGraphics 2024;44(5) View
  38. Kim J, Hasan A, Kellogg K, Ratliff W, Murray S, Suresh H, Valladares A, Shaw K, Tobey D, Vidal D, Lifson M, Patel M, Raji I, Gao M, Knechtle W, Tang L, Balu S, Sendak M, Guillot G. Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. PLOS Digital Health 2024;3(5):e0000390 View
  39. Rusinovich Y, Rusinovich V. Confounders in Predictive Medical Models: Roles of Nationality and Immigrant Status. Web3 Journal: ML in Health Science 2024;1(1) View
  40. Wang Y, Wang L, Zhou Z, Laurentiev J, Lakin J, Zhou L, Hong P. Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases. Journal of Biomedical Informatics 2024;156:104677 View
  41. Noam K, Schmutte T, Bory C, Plant R. Mitigating Racial Bias in Health Care Algorithms: Improving Fairness in Access to Supportive Housing. Psychiatric Services 2024 View
  42. Baker C, Pease M, Sexton D, Abumoussa A, Chambless L. Artificial intelligence innovations in neurosurgical oncology: a narrative review. Journal of Neuro-Oncology 2024 View
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  45. Jiang Z, Seyedi S, Griner E, Abbasi A, Rad A, Kwon H, Cotes R, Clifford G, McGinnis R. Evaluating and mitigating unfairness in multimodal remote mental health assessments. PLOS Digital Health 2024;3(7):e0000413 View

Books/Policy Documents

  1. Montasari R. Cyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution. View
  2. Swathi N, Chakrabarti M, Muzzamil M, Hamdar H, Jaber A, Chamoun A, Al Amin F, Rathod P. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry. View