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

Reviews in Medical Informatics (93) Artificial Intelligence (1371) Machine Learning (1465) Systemic Racism and Racial Bias in Health Care and Society (85) Validity, Reliability, and Bias (93)

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 (63) Tweetations (11) Metrics

Journals

  1. Landau A, Blanchard A, Atkins N, Salazar S, Cato K, Patton D, Topaz M. Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning–Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers. JMIR Formative Research 2023;7:e40194 View
  2. Al-Ani M, Bai C, Hashky A, Parker A, Vilaro J, Aranda Jr. J, Shickel B, Rashidi P, Bihorac A, Ahmed M, Mardini M. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Frontiers in Cardiovascular Medicine 2023;10 View
  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, Ochoa K. Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States. Clinical Infectious Diseases 2024;79(3):717 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):d070224 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;75(11):1167 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;169(3):489 View
  43. Ganta T, Kia A, Parchure P, Wang M, Besculides M, Mazumdar M, Smith C. Fairness in Predicting Cancer Mortality Across Racial Subgroups. JAMA Network Open 2024;7(7):e2421290 View
  44. Mathis W, Zhao S, Pratt N, Weleff J, De Paoli S. Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods?. Computer Methods and Programs in Biomedicine 2024;255:108356 View
  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
  46. Krauss D, Engel L, Ott T, Bräunig J, Richer R, Gambietz M, Albrecht N, Hille E, Ullmann I, Braun M, Dabrock P, Kölpin A, Koelewijn A, Eskofier B, Vossiek M. A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring. IEEE Open Journal of Engineering in Medicine and Biology 2024;5:680 View
  47. Patino G, Roberts L. The Need for Greater Transparency in Journal Submissions That Report Novel Machine Learning Models in Health Professions Education. Academic Medicine 2024;99(9):935 View
  48. Cordella C, Marte M, Liu H, Kiran S. An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications. Perspectives of the ASHA Special Interest Groups 2024:1 View
  49. Khalil A, Bellesia G, Norton M, Jacobsson B, Haeri S, Egbert M, Malone F, Wapner R, Roman A, Faro R, Madankumar R, Strong N, Silver R, Vohra N, Hyett J, MacPherson C, Prigmore B, Ahmed E, Demko Z, Ortiz J, Souter V, Dar P. The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model. American Journal of Obstetrics and Gynecology 2024;231(5):554.e1 View
  50. Huang Y, Guo J, Donahoo W, Lee Y, Fan Z, Lu Y, Chen W, Tang H, Bilello L, Saguil A, Rosenberg E, Shenkman E, Bian J. A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes. Nature Communications 2024;15(1) View
  51. McCoy L, Ci Ng F, Sauer C, Yap Legaspi K, Jain B, Gallifant J, McClurkin M, Hammond A, Goode D, Gichoya J, Celi L. Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Medical Education 2024;24(1) View
  52. Carbunaru S, Neshatvar Y, Do H, Murray K, Ranganath R, Nayan M. Survival after Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model (Preprint). JMIR Medical Informatics 2024 View
  53. Park K, Saleem M, Al-Garadi M, Ahmed A. Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review. BMC Medical Informatics and Decision Making 2024;24(1) View
  54. Lucas M, Schootman M, Laryea J, Orcutt S, Li C, Ying J, Rumpel J, Yang C. Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection. JCO Clinical Cancer Informatics 2024;(8) View
  55. Ladin K, Cuddeback J, Duru O, Goel S, Harvey W, Park J, Paulus J, Sackey J, Sharp R, Steyerberg E, Ustun B, van Klaveren D, Weingart S, Kent D. Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor. npj Digital Medicine 2024;7(1) View
  56. Sarullo K, Swamidass S. Understanding and mitigating the impact of race with adversarial autoencoders. Communications Medicine 2024;4(1) View
  57. Grant J, Javaid A, Carrick R, Koester M, Kassamali A, Kim C, Isakadze N, Wu K, Blaha M, Whelton S, Arbab-Zadeh A, Orringer C, Blumenthal R, Martin S, Marvel F. Digital health innovation and artificial intelligence in cardiovascular care: a case-based review. npj Cardiovascular Health 2024;1(1) View
  58. Dudek N, Chakhvadze M, Kobakhidze S, Kantidze O, Yuriy G. Supervised machine learning for microbiomics: bridging the gap between current and best practices. Machine Learning with Applications 2024:100607 View

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