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Journal articles on the topic 'Medical bias'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

Alderton, Gemma. "Bias in medical devices." Science 372, no. 6537 (2021): 43.10–45. http://dx.doi.org/10.1126/science.372.6537.43-j.

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2

KILIC, Selim. "Bias in medical research." Journal of Mood Disorders 4, no. 2 (2014): 92. http://dx.doi.org/10.5455/jmood.20140606042223.

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3

Barabino, Gilda, and Harriet Nembhard. "Suffocating from Medical Bias." American Scientist 110, no. 4 (2022): 204. http://dx.doi.org/10.1511/2022.110.4.204.

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4

Criado-Perez, Caroline. "End medical gender bias." New Scientist 242, no. 3234 (2019): 23. http://dx.doi.org/10.1016/s0262-4079(19)31077-2.

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5

Pinzur, Michael S. "Bias in Medical Education." Foot & Ankle International 41, no. 1 (2019): 121. http://dx.doi.org/10.1177/1071100719851795.

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6

AlHasan, AJMS. "Bias in medical artificial intelligence." Bulletin of the Royal College of Surgeons of England 103, no. 6 (2021): 302–5. http://dx.doi.org/10.1308/rcsbull.2021.111.

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7

Pittler, Max H. "Bias in (complementary) medical research." Focus on Alternative and Complementary Therapies 15, no. 3 (2010): 210–13. http://dx.doi.org/10.1111/j.2042-7166.2010.01041.x.

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8

Friedman, C. P., and J. C. Wyatt. "Publication Bias in Medical Informatics." Journal of the American Medical Informatics Association 8, no. 2 (2001): 189–91. http://dx.doi.org/10.1136/jamia.2001.0080189.

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9

Elston, Dirk M. "Cognitive bias and medical errors." Journal of the American Academy of Dermatology 81, no. 6 (2019): 1249. http://dx.doi.org/10.1016/j.jaad.2019.06.1284.

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10

Eichler, Margrit, Anna Lisa Reisman, and Elaine Manace Borins. "Gender Bias in Medical Research." Women & Therapy 12, no. 4 (1992): 61–70. http://dx.doi.org/10.1300/j015v12n04_06.

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11

Bolton, Jonathan. "Medical practice and anthropological bias." Social Science & Medicine 40, no. 12 (1995): 1655–61. http://dx.doi.org/10.1016/0277-9536(94)00282-x.

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12

Ojasoo, T., and J. C. Doré. "Citation bias in medical journals." Scientometrics 45, no. 1 (1999): 81–94. http://dx.doi.org/10.1007/bf02458469.

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13

Griffin, Barbara N., and Ian G. Wilson. "Interviewer bias in medical student selection." Medical Journal of Australia 193, no. 6 (2010): 343–46. http://dx.doi.org/10.5694/j.1326-5377.2010.tb03946.x.

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Griffin, Barbara N., and Ian G. Wilson. "Interviewer bias in medical student selection." Medical Journal of Australia 193, no. 8 (2010): 486. http://dx.doi.org/10.5694/j.1326-5377.2010.tb04015.x.

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15

Calfee, Ryan, Eric Fynn-Thompson, and Peter Stern. "Surgeon Bias in the Medical Record." Orthopedics 32, no. 10 (2009): 732–36. http://dx.doi.org/10.3928/01477447-20090818-07.

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16

Eichbaum, Quentin. "Medical Error, Cognitive Bias, and Debiasing." Academic Medicine 94, no. 8 (2019): 1065–66. http://dx.doi.org/10.1097/acm.0000000000002791.

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17

Brice, A., and I. Chalmers. "Medical journal editors and publication bias." BMJ 347, oct22 6 (2013): f6170. http://dx.doi.org/10.1136/bmj.f6170.

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18

Boyle, Gregory J., and George Hill. "Circumcision-generated emotions bias medical literature." BJU International 109, no. 4 (2012): E11. http://dx.doi.org/10.1111/j.1464-410x.2012.10917.x.

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19

Brahams, Diana. "Truth, bias, and the medical “expert”." Lancet 353, no. 9162 (1999): 1380. http://dx.doi.org/10.1016/s0140-6736(99)00143-9.

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20

Westfall, John M. "Change in Medical Student Implicit Bias." Journal of General Internal Medicine 31, no. 7 (2016): 714. http://dx.doi.org/10.1007/s11606-016-3684-4.

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21

Elston, Dirk M. "Confirmation bias in medical decision-making." Journal of the American Academy of Dermatology 82, no. 3 (2020): 572. http://dx.doi.org/10.1016/j.jaad.2019.06.1286.

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22

de Melker, H. E., F. R. Rosendaal, and J. P. Vandenbroucke. "Is publication bias a medical problem?" Lancet 342, no. 8871 (1993): 621. http://dx.doi.org/10.1016/0140-6736(93)91449-v.

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23

Loewenstein, George. "Projection Bias in Medical Decision Making." Medical Decision Making 25, no. 1 (2005): 96–105. http://dx.doi.org/10.1177/0272989x04273799.

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24

Hsiehchen, David, and Magdalena Espinoza. "Detecting editorial bias in medical publishing." Scientometrics 106, no. 1 (2015): 453–56. http://dx.doi.org/10.1007/s11192-015-1753-9.

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25

Saha, Somnath. "Rectifying Institutional Bias in Medical Research." Archives of Pediatrics & Adolescent Medicine 163, no. 2 (2009): 181. http://dx.doi.org/10.1001/archpediatrics.2008.552.

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26

Domen, Gracia. "Educational Bias." AORN Journal 77, no. 6 (2003): 1084. http://dx.doi.org/10.1016/s0001-2092(06)60962-9.

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27

Manchikanti, Laxmaiah. "Medical Journal Peer Review: Process and Bias." Pain Physician 18;1, no. 1;1 (2015): E1—E14. http://dx.doi.org/10.36076/ppj/2015.18.e1.

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Scientific peer review is pivotal in health care research in that it facilitates the evaluation of findings for competence, significance, and originality by qualified experts. While the origins of peer review can be traced to the societies of the eighteenth century, it became an institutionalized part of the scholarly process in the latter half of the twentieth century. This was a response to the growth of research and greater subject specialization. With the current increase in the number of specialty journals, the peer review process continues to evolve to meet the needs of patients, clinici

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28

Grote, Thomas, and Geoff Keeling. "On Algorithmic Fairness in Medical Practice." Cambridge Quarterly of Healthcare Ethics 31, no. 1 (2022): 83–94. http://dx.doi.org/10.1017/s0963180121000839.

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AbstractThe application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. In this pap

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29

Graber, Mark A. "Heuristics and medical errors. Part 2: How to make better medical decisions." Russian Family Doctor 25, no. 1 (2021): 45–52. http://dx.doi.org/10.17816/rfd62009.

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This publication is a continuation of the article published in the 4th issue of the journal Russian family doctor for 2020 Heuristics, language and medical errors, which described the ways of making medical decisions that can lead to errors in patient management tactics, in particular affect of heuristics / visceral bias, attribution error, frame of reference, availability bias, one-word-one-meaning-fallacy. This article discusses additional sources of diagnostic error, including diagnosis momentum, confirmation bias, representativeness, and premature closure also the conflict that arises from

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30

Burris, James F. "Publication bias." Clinical Pharmacology and Therapeutics 53, no. 4 (1993): 495. http://dx.doi.org/10.1038/clpt.1993.56.

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31

Strüver, V. "Publication Bias." Current Therapeutic Research 78 (2016): S7. http://dx.doi.org/10.1016/j.curtheres.2016.05.016.

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32

Kopans, Daniel B. "Bias in the Medical Journals: A Commentary." American Journal of Roentgenology 185, no. 1 (2005): 176–82. http://dx.doi.org/10.2214/ajr.185.1.01850176.

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33

Capers, Quinn, Daniel Clinchot, Leon McDougle, and Anthony G. Greenwald. "Implicit Racial Bias in Medical School Admissions." Academic Medicine 92, no. 3 (2017): 365–69. http://dx.doi.org/10.1097/acm.0000000000001388.

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34

Harrison, Leila E., Bobbie Ann A. White, Kaitlyn Hawrylak, and David McIntosh. "Explicit bias among fourth-year medical students." Baylor University Medical Center Proceedings 32, no. 1 (2019): 50–53. http://dx.doi.org/10.1080/08998280.2018.1519519.

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35

Nardini, Cecilia, and Jan Sprenger. "Bias and Conditioning in Sequential Medical Trials." Philosophy of Science 80, no. 5 (2013): 1053–64. http://dx.doi.org/10.1086/673732.

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36

Rankin-Box, Denise. "Shaping medical knowledge II: Bias and balance." Complementary Therapies in Clinical Practice 12, no. 2 (2006): 77–79. http://dx.doi.org/10.1016/j.ctcp.2006.02.001.

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37

Levy, Andrea Gurmankin, and John C. Hershey. "Value-Induced Bias in Medical Decision Making." Medical Decision Making 28, no. 2 (2008): 269–76. http://dx.doi.org/10.1177/0272989x07311754.

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38

Chen, Jacky, Stephen Littlefair, and Warren Reed. "Investigating Visual Hindsight Bias in Medical Imaging." Academic Radiology 27, no. 10 (2020): 1494. http://dx.doi.org/10.1016/j.acra.2020.05.044.

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39

Russell, Stephen, Fabio Montes Suros, and Ashwin Kumar. "Exploiting Machine Learning Bias: Predicting Medical Denials." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 58–63. http://dx.doi.org/10.1609/aaaiss.v3i1.31181.

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For a large healthcare system, ignoring costs associated with managing the patient encounter denial process (staffing, contracts, etc.), total denial-related amounts can be more than $1B annually in gross charges. Being able to predict a denial before it occurs has the potential for tremendous savings. Using machine learning to predict denial has the potential to allow denial-preventing interventions. However, challenges of data imbalance make creating a single generalized model difficult. We employ two biased models in a hybrid voting scheme to achieve results that exceed the state-of-the art

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40

Hedelin, Henrik, and Emil Péclard. "The Bias of Medical Care—Keeping Medical Journals Free of Propaganda." Wilderness & Environmental Medicine 23, no. 3 (2012): 294. http://dx.doi.org/10.1016/j.wem.2012.05.001.

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41

Hziung, Lim Quan, Tan Wooi Hang, Leong Van Jet, et al. "WEIGHT BIAS AMONG MEDICAL STUDENTS IN A SOUTHEAST ASIAN MEDICAL SCHOOL." Journal of the ASEAN Federation of Endocrine Societies 38, S3 (2023): 85–86. https://doi.org/10.15605/jafes.038.afes.135.

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INTRODUCTIONWeight bias is a preconceived negative notion towards individuals who are overweight and obese. These biases can be explicit, which are negative attitudes consciously held and outwardly expressed; or implicit, which may be covert and subconscious. Both implicit and explicit weight biases have been documented to be prevalent among medical students in multiple countries and may potentially persist into their professional careers and compromise healthcare delivery to patients who are overweight and obese. METHODOLOGYIn this cross-sectional study carried out from July to August 2023, u

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42

Maddison, Jill, and Helen Dirrig. "Diagnostic bias and its impact on clinical decision making." Companion Animal 30, no. 7 (2025): 2–6. https://doi.org/10.12968/coan.2024.0025.

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Clinical decision making is a fundamental part of practicing veterinary medicine and can be a complex process. All clinicians are at risk of diagnostic bias, and diagnostic bias can lead to medical errors. Medical errors are estimated to occur in up to 15% of patients in the human healthcare system, and it seems unlikely that the level of error in veterinary practice is substantially better. This article will discuss how clinicians make clinical decisions using a combination of type I (fast) and type II (slow) thinking strategies as well as factors that can lead to medical errors. Cognitive er

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43

Buchs, Shalon, and Karen Mulitalo. "Implicit Bias." Journal of Physician Assistant Education 27, no. 4 (2016): 193–95. http://dx.doi.org/10.1097/jpa.0000000000000098.

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44

Reidenberg, Marcus M. "Decreasing publication bias." Clinical Pharmacology & Therapeutics 63, no. 1 (1998): 1–3. http://dx.doi.org/10.1016/s0009-9236(98)90115-1.

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45

Justman, Stewart. "Theory vs. Evidence: Unconscious Bias in Medical Decisions." Journal of Controversial Ideas 5, no. 1 (2025): 1. https://doi.org/10.63466/jci05010003.

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Racial disparities of medical care have been well documented for decades. That much is clear. Less clear are the cause or causes. By the time of the historic 2003 report by the Institute of Medicine (IOM), investigators had uncovered a pervasive pattern of disparities – often, however, without access to clinical or socioeconomic data which might help explain them. It was in these circumstances that the authors of the IOM report adopted the theory of unconscious bias as the ultimate explanation of observed disparities of care. The theory of a profound bias working outside the holder’s awareness

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46

Hsu, Che-Wei. "Mind Over Prejudice: An Implicit Bias Training in Medical Education Using Cognitive Bias Modification." Journal of Graduate Medical Education 15, no. 5 (2023): 541–43. http://dx.doi.org/10.4300/jgme-d-23-00146.1.

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47

Schrager, Sarina. "Moving Beyond Bias in Practice and Medical Education." Family Medicine 54, no. 6 (2022): 425–26. http://dx.doi.org/10.22454/fammed.2022.648268.

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48

Hamada, Chikuma, Yushi Nakanishi, and Nobushige Matsuoka. "Meta-analysis and Publication Bias in Medical Research." Japanese Journal of Biometrics 27, no. 2 (2006): 139–57. http://dx.doi.org/10.5691/jjb.27.139.

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49

&NA;. "The potential for commercial bias in medical journals." Inpharma Weekly &NA;, no. 1543 (2006): 2. http://dx.doi.org/10.2165/00128413-200615430-00002.

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50

Kulatunga-Moruzi, Chan, Lee R. Brooks, and Geoffrey R. Norman. "Using Comprehensive Feature Lists to Bias Medical Diagnosis." Journal of Experimental Psychology: Learning, Memory, and Cognition 30, no. 3 (2004): 563–72. http://dx.doi.org/10.1037/0278-7393.30.3.563.

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