The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; 1 of the 48 sources its claims cite were checked for support.
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
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Claims the paper makes
40 Not checked yet · 24 Own interpretation · 11 No basis given · 1 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Since black patients are more likely to receive kidneys from black donors, anything that reduces the likelihood of donation from black people could contribute to the wait-time disparity.29”
What the sources say
- Partly supportedcites [29] sourceExperimental — accuracy not yet measured
The Results section, “Differences in donor risk index by recipient race” (page unavailable), reports that African American recipients were more likely than Caucasian recipients to receive a kidney from an African American donor. The Discussion encourages increased living donation in the African American community, but the paper does not establish that reduced donation from black people contributes to the wait-time disparity. (LLM, conf=0.84)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Use of a calculator that lowers the estimate of VBAC success for people of color could exacerbate these disparities.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Rests on an assumption: Nonwhite women in the United States have higher cesarean-section rates than white women.
- Rests on an assumption: Successful vaginal deliveries have health benefits, including fewer surgical complications, faster recovery, and fewer complications in later pregnancies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When used preoperatively to assess risk, these calculations could steer minority patients, deemed to be at higher risk, away from surgery.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The American Heart Association (AHA) Get with the Guidelines-Heart Failure Risk Score predicts the risk of death in patients admitted to the hospital.9”
What the sources say
- Not checked yetcites [9] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most race corrections implicitly, if not explicitly, operate on the assumption that genetic difference tracks reliably with race.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Studies of the genetic structure of human populations continue to find more variation within racial groups than between them.34,35”
What the sources say
- Not checked yetcites [34] source
- Not checked yetcites [35]
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The developers of the KDRI do not provide possible explanations for this difference.12”
What the sources say
- Not checked yetcites [12] source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Meanwhile, black patients in the United States still have longer wait times for kidney transplants than nonblack patients.29”
What the sources say
- Not checked yetcites [29] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“The AHA does not provide a rationale for this adjustment.”
What the sources say
- Not checked yetcites [9] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“The race adjustment is based on an empirical finding that black donors' kidneys perform worse than nonblack donors' kidneys, regardless of the recipient's race.28”
What the sources say
- Not checked yetcites [28] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 23
- It may state two separate claims as one. 17
None of the sources cited for this claim have been checked yet.
In the paper
“Explanations that have been given for this finding include the notion that black people release more creatinine into their blood at baseline, in part because they are reportedly more muscular.11,25”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“It assigns three additional points to any patient identified as “nonblack,” thereby categorizing all black patients as being at lower risk.”
What the sources say
- Not checked yetcites [9] source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Epidemiologists continue to debate how to responsibly make causal inferences based on race.33”
What the sources say
- Not checked yetcites [33] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, despite mounting evidence that race is not a reliable proxy for genetic difference, the belief that it is has become embedded, sometimes insidiously, within medical practice.”
plain- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“Clinicians are advised to use this risk score to guide decisions about referral to cardiology and allocation of health care resources.”
What the sources say
- Not checked yetcites [9] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Vaginal Birth after Cesarean (VBAC) algorithm predicts the risk posed by a trial of labor for someone who has previously undergone cesarean section.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such situations, however, are exceedingly unlikely.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“while black people already have higher rates of end-stage kidney disease and death due to kidney failure than the overall population.25”
What the sources say
- Not checked yetcites [25] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our understanding of race and human genetics has advanced considerably since 2003, yet these insights have not led to clear guidelines on the use of race in medicine.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Physicians use these algorithms to individualize risk assessment and guide clinical decisions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Analyses have cast doubt on this claim, 26 but the “race-corrected” eGFR remains the standard.”
What the sources say
- Not checked yetcites [26] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Some algorithm developers offer no explanation of why racial or ethnic differences might exist.Others offer rationales, but when these are traced to their origins, they lead to outdated, suspect racial science or to biased data.22,30,31”
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“troubling because black people already have higher rates of maternal mortality.30”
What the sources say
- Not checked yetcites [30] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given this complexity, it is insufficient to translate a data signal into a race adjustment without determining what race might represent in the particular context.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They examined data sets of clinical outcomes and patient characteristics and then performed regression analyses to identify which patient factors correlated significantly with the relevant outcomes.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Researchers then decided that it was appropriate -even essential -to adjust for race in their model.”
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The Society of Thoracic Surgeons produces elaborate calculators to estimate the risk of death and other complications during surgery.10”
What the sources say
- Not checked yetcites [10] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Use of the KDRI may do just that.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Changing to any other race or ethnicity does not increase the estimated risk of death as compared with a white patient, but it does change the risk of renal failure, stroke, or prolonged ventilation.”
What the sources say
- Not checked yetcites [10] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the empirical differences seen between racial groups were actually due to genetic differences, then race adjustment might be justified: different coefficients for different bodies.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since it is cumbersome to measure kidney function directly, researchers have developed equations that determine the estimated glomerular filtration rate (eGFR) from an accessible measure, the serum creatinine level.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The calculators include race and ethnicity because of observed differences in surgical outcomes among racial and ethnic groups; the authors acknowledge that the mechanism underlying these differences is not known.”
What the sources say
- Not checked yetcites [10] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Conversely, race adjustments that yield higher estimates of kidney function in black patients might delay their referral for specialist care or transplantation and lead to worse outcomes”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Since the health care system has spent more money, on average, on white patients than on black patients, the tool returned higher risk scores for white patients than for black patients.”
What the sources say
- Not checked yetcites [36] source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This decision reflects a default assumption in medicine: it is acceptable to use race adjustment even without understanding what race represents in a given context.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Some tools, including eGFR and the VBAC calculator, have already been challenged; clinicians have advocated successfully for their institutions to remove the adjustment for race.43,44”
What the sources say
- Not checked yetcites [43]
- Not checked yetcites [44]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, when clinicians insert race into their tools, they risk interpreting racial disparities as immutable facts rather than as injustices that require intervention.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The criminal justice system, for instance, uses recidivism-prediction tools to guide decisions about bond amounts and prison sentences.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Clinicians and medical researchers typically use the categories recommended by the Office of Management and Budget: five races and two ethnicities.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Guidelines are silent on such issues -an indication of their inadequacy.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But if the underlying data reflect racist social structures, then their use in predictive tools cements racism into practice and policy.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When these tools influence high-stakes decisions, whether in the clinic or the courtroom, they propagate inequity into our future.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The Society of Thoracic Surgeons acknowledged concerns raised by clinicians and policymakers “that inclusion of SES factors in risk models may 'adjust away' disparities in quality of care.”Nonetheless, it proceeded to consider “all preoperative factors that are independently and significantly associated with outcomes””
What the sources say
- Not checked yetcites [10] source
plainconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our understanding of race has advanced considerably in the past two decades.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“One tool, COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), while not using race per se, uses many factors that correlate with race and returns higher risk scores for black defendants.40”
What the sources say
- Not checked yetcites [40]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A second problem arises from the ways in which racial and ethnic categories are operationalized.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores may well have led to more referrals for white patients to specialty services, perpetuating both spending discrepancies and race bias in health care.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Nonwhite women in the United States have higher cesarean-section rates than white women.
“Nonwhite U.S. women continue to have higher rates of cesarean section than white U.S. women.”
The paper relies on this without support.
Successful vaginal deliveries have health benefits, including fewer surgical complications, faster recovery, and fewer complications in later pregnancies.
“The health benefits of successful vaginal deliveries are well known, including lower rates of surgical complications, faster recovery time, and fewer complications during subsequent pregnancies.”
The paper relies on this without support.
Minority patients routinely have different health outcomes from white patients.
“Since minority patients routinely have different health outcomes from white patients, race and ethnicity often correlated with the outcome of interest.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Such “corrections” are used in a variety of areas of medicine, from endocrinology and nephrology to oncology and obstetrics, which often have the effect of requiring patients of color to be sicker before they qualify for the same level of care as a White patient.24–26”
From Challenging Race-Based Medicine Through Historical Education About the Social Construction of Race (2023) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“If a classifier deployed in a clinical pipeline mistakenly underdiagnosed a certain subgroup (for example, Black patients) more than others due to the lower prevalence of the disease, this still leads to disadvantage for members of that group and could lead to serious ethical concerns 8 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“The choice of these subgroups is motivated by the clear history, in both traditional medicine and AI algorithms, of bias for subgroups on these axes 6,8,10,11 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“We note that some of our observed differences in underdiagnosis have been established in other areas in clinical care, such as underdiagnosis of female patients 9,10 , Black patients 5,8,9 and patients with a low socioeconomic status 36 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“However, algorithmic bias has harmed minoritized communities in housing, banking, and education, and health care is no different.1”
From Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care (2023) · cites this paper as [1]
In the citing paper
“In reality, however, AI models are frequently found to be biased toward certain patient groups, leading to disparities not only in performance but also in potential or actual clinical benefits [7–9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“In addition to racial and ethnic imbalances in training data, there has been increased scrutiny on the use of race and ethnicity in predictive clinical algorithms [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Indeed, there is mounting evidence that race is not a reliable proxy for genetic differences and, despite this lack of evidence, it has become acceptable to adjust for race even without understanding what it represents in a given context [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Indeed, there is mounting evidence that race is not a reliable proxy for genetic differences and, despite this lack of evidence, it has become acceptable to adjust for race even without understanding what it represents in a given context [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Race and ethnicity are social constructs, not biological constructs [9,90].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Unethically and irresponsibly designed AI/ML solutions deployed in the healthcare setting can exacerbate and perpetuate systematic biases and disparities for those from marginalized groups [27–29].”
From ChatGPT and large language models in academia: opportunities and challenges (2023) · cites this paper as [27]
Citations
Cites
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Race after technology. Abolitionist tools for the new Jim Code
Merz, S. (2020) · Ethnic and Racial Studies · doi:10.1080/01419870.2020.1715454 · Cited by 1,889
The Importance of Race and Ethnic Background in Biomedical Research and Clinical Practice
Burchard, E. G., Ziv, E., Coyle, N. E., Gomez, S. L., Tang, H., Karter, A. J., Mountain, J. L., Pérez‐Stable, E. J., Sheppard, D., & RISCH, N. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmsb025007 · Cited by 1,104
A Comprehensive Risk Quantification Score for Deceased Donor Kidneys: The Kidney Donor Risk Index
Rao, P. S., Schaubel, D. E., Guidinger, M. K., Andreoni, K. A., Wolfe, R. A., Merion, R. M., Port, F. K., & Sung, R. S. (2009) · Transplantation · doi:10.1097/tp.0b013e3181ac620b · Cited by 1,079
Fatal invention: how science, politics, and big business re-create race in the twenty-first century
Roberts, D. E. (2011) · Choice Reviews Online · doi:10.5860/choice.49-2029 · Cited by 1,028
Methods for the Scientific Study of Discrimination and Health: An Ecosocial Approach
Krieger, N. (2012) · American Journal of Public Health · doi:10.2105/ajph.2011.300544 · Cited by 854
Taking race out of human genetics
Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016) · Science · doi:10.1126/science.aac4951 · Cited by 631
Cooper, R., Kaufman, J. S., & Ward, R. H. (2003) · New England Journal of Medicine · doi:10.1056/nejmsb022863 · Cited by 628
Use of race and ethnicity in biomedical publication
Kaplan, J. B. (2003) · JAMA · doi:10.1001/jama.289.20.2709 · Cited by 609
Research works printed but not linked: 2
- Jones DS, Dorr GM, Pollock A. Symposium: race, pharmaceuticals, and medical technology. J Law Med Ethics 2008;36:439-545.
- Kanis JA. Assessment of osteoporosis at the primary health care level. WHO Scientific Group technical report. Sheffield, United Kingdom: World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, 2007.
Cited by
Sorted by citations
KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease
Stevens, P. E., Ahmed, S. B., Carrero, J. J., Foster, B., Francis, A., Hall, R. K., Herrington, W. G., Hill, G., Inker, L. A., Kazancıoğlu, R., et al. (2024) · Kidney International · doi:10.1016/j.kint.2023.10.018 · Cited by 8,530
New creatinine- and cystatin C-based equations to estimate GFR without race
Inker, L. A., Eneanya, N. D., Coresh, J., Tighiouart, H., Wang, D., Sang, Y., Crews, D. C., Doria, A., Estrella, M. M., Froissart, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2102953 · Cited by 4,695
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
Toward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734
Development and validation of the American Heart Association’s PREVENT equations
Khan, S. S., Matsushita, K., Sang, Y., Ballew, S. H., Grams, M. E., Surapaneni, A., Blaha, M. J., Carson, A. P., Chang, A. R., Ciemins, E., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067626 · Cited by 1,234
Churchwell, K., Elkind, M. S. V., Benjamin, R. M., Carson, A. P., Chang, E. K., Lawrence, W., Mills, A., Odom, T. M., Rodriguez, C. J., Rodriguez, F., et al. (2020) · Circulation · doi:10.1161/cir.0000000000000936 · Cited by 984
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928
Evaluation of clinical prediction models (part 1): from development to external validation
Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-074819 · Cited by 798
Khan, S. S., Coresh, J., Pencina, M. J., Ndumele, C. E., Rangaswami, J., Chow, S. L., Palaniappan, L. P., Sperling, L. S., Virani, S. S., Ho, J. E., et al. (2023) · Circulation · doi:10.1161/cir.0000000000001191 · Cited by 797
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era
Kim, W. R., Mannalithara, A., Heimbach, J. K., Kamath, P. S., Asrani, S. K., Biggins, S. W., Wood, N. L., Gentry, S. E., & Kwong, A. J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.08.050 · Cited by 654
A framework for digital health equity
Richardson, S., Lawrence, K., Schoenthaler, A. M., & Mann, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00663-0 · Cited by 607
AI recognition of patient race in medical imaging: a modelling study
Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00063-2 · Cited by 611
Bias in medical AI: Implications for clinical decision-making
Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000651 · Cited by 607
ChatGPT and large language models in academia: opportunities and challenges
Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O’Connor, K., Li, R., Peng, P.-C., Bright, T. J., Tatonetti, N., Won, K. J., Gonzalez-Hernandez, G., et al. (2023) · BioData Mining · doi:10.1186/s13040-023-00339-9 · Cited by 569
Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning
Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025) · Modern Pathology · doi:10.1016/j.modpat.2024.100686 · Cited by 535
From race-based to race-conscious medicine: how anti-racist uprisings call us to act
Cerdeña, J. P., Plaisime, M. V., & Tsai, J. W. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32076-6 · Cited by 437
Pierce, C. B., Muñoz, Á., Ng, D. K., Warady, B. A., Furth, S. L., & Schwartz, G. J. (2020) · Kidney International · doi:10.1016/j.kint.2020.10.047 · Cited by 434
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad493 · Cited by 359
Fawzy, A., Wu, T. D., Wang, K., Robinson, M. L., Farha, J., Bradke, A., Golden, S. H., Xu, Y., & Garibaldi, B. T. (2022) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.1906 · Cited by 326
Misrepresenting Race — The Role of Medical Schools in Propagating Physician Bias
Amutah, C., Greenidge, K., Mante, A., Munyikwa, M. R., Surya, S. L., Higginbotham, E. J., Jones, D. S., Lavizzo‐Mourey, R. J., Roberts, D. E., Tsai, J. W., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmms2025768 · Cited by 324
Self-citationDesigning deep learning studies in cancer diagnostics
Kleppe, A., Skrede, O.-J., Raedt, S. D., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-020-00327-9 · Cited by 328
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462571 · Cited by 300
Implicit bias in healthcare: clinical practice, research and decision making
Gopal, D. P., Chetty, U., O'Donnell, P., Gajria, C., & Blackadder-Weinstein, J. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0233 · Cited by 298
Bhakta, N. R., Bime, C., Kaminsky, D. A., McCormack, M. C., Thakur, N., Stanojevic, S., Baugh, A. D., Braun, L., Lovinsky‐Desir, S., Adamson, R., et al. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202302-0310st · Cited by 316
The limits of fair medical imaging AI in real-world generalization
Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03113-4 · Cited by 285
Chin, M. H., Afsar-Manesh, N., Bierman, A. S., Chang, C., Colón-Rodríguez, C. J., Dullabh, P., Duran, D. G., Fair, M., Hernandez-Boussard, T., Hightower, M., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.45050 · Cited by 275
Race, genetic ancestry, and estimating kidney function in CKD
Hsu, C.-Y., Yang, W., Parikh, R. V., Anderson, A. H., Chen, T. K., Cohen, D. L., He, J., Mohanty, M. J., Lash, J. P., Mills, K. T., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2103753 · Cited by 241
Why Diverse Clinical Trial Participation Matters
Schwartz, A. L., Alsan, M. M., Morris, A. A., & Halpern, S. D. (2023) · New England Journal of Medicine · doi:10.1056/nejmp2215609 · Cited by 224
Miller, W. G., Kaufman, H. W., Levey, A. S., Straseski, J. A., Wilhelms, K. W., Yu, H. Y. (., Klutts, J. S., Hilborne, L. H., Horowitz, G. L., Lieske, J., et al. (2022) · Clinical Chemistry · doi:10.1093/clinchem/hvab278 · Cited by 222
Machine learning and algorithmic fairness in public and population health
Mhasawade, V., Zhao, Y., & Chunara, R. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00373-4 · Cited by 233
Getting genetic ancestry right for science and society
Lewis, A. C. F., Molina, S. J., Appelbaum, P. S., Dauda, B., Rienzo, A. D., Fuentes, A., Fullerton, S. M., Garrison, N. A., Ghosh, N., Hammonds, E. M., et al. (2022) · Science · doi:10.1126/science.abm7530 · Cited by 231
Self-citationKöttgen, A., Gall, É. C.-L., Halbritter, J., Kiryluk, K., Mallett, A. J., Parekh, R. S., Rasouly, H. M., Sampson, M. G., Tin, A., Antignac, C., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.03.019 · Cited by 217
Human-Centered Design to Address Biases in Artificial Intelligence
Chen, Y., Clayton, E. W., Novak, L. L., Anders, S. H., & Malin, B. (2023) · Journal of Medical Internet Research · doi:10.2196/43251 · Cited by 226
Risk Factors for Atonic Postpartum Hemorrhage
Ende, H. B., Lozada, M. J., Chestnut, D. H., Osmundson, S. S., Walden, R. L., Shotwell, M. S., & Bauchat, J. R. (2021) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000004228 · Cited by 220
Grobman, W. A., Sandoval, G. J., Rice, M. M., Bailit, J. L., Chauhan, S. P., Costantine, M. M., Gyamfi‐Bannerman, C., Metz, T. D., Parry, S. I., Rouse, D. J., et al. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.05.021 · Cited by 194
Giovanola, B., & Tiribelli, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01455-6 · Cited by 193
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Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186
Coronavirus Disease 2019 and the Athletic Heart
Kim, J. H., Levine, B. D., Phelan, D. M. J., Emery, M. S., Martinez, M. W., Chung, E. H., Thompson, P. D., & Baggish, A. L. (2020) · JAMA Cardiology · doi:10.1001/jamacardio.2020.5890 · Cited by 182
Structural Racism: The Rules and Relations of Inequity
Gee, G. C., & Hicken, M. T. (2021) · Ethnicity & Disease · doi:10.18865/ed.31.s1.293 · Cited by 181
AI-driven healthcare: A review on ensuring fairness and mitigating bias
Chinta, S. V., Wang, Z., Palikhe, A., Zhang, X., Kashif, A., Smith, M. A., Liu, J., & Zhang, W. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000864 · Cited by 268
Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues
Lewis, A. C. F., & Green, R. C. (2021) · Genome Medicine · doi:10.1186/s13073-021-00829-7 · Cited by 188
Artificial intelligence-based clinical decision support in pediatrics
Ramgopal, S., Sanchez-Pinto, L. N., Horvat, C. M., Carroll, M. S., Luo, Y., & Florin, T. A. (2022) · Pediatric Research · doi:10.1038/s41390-022-02226-1 · Cited by 187
Advances in Artificial Intelligence for Infectious-Disease Surveillance
Brownstein, J. S., Rader, B. M., Astley, C. M., & Tian, H. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2119215 · Cited by 195
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164
Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework
Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100702 · Cited by 170
Genome-wide polygenic score to predict chronic kidney disease across ancestries
Khan, A., Turchin, M. C., Patki, A., Srinivasasainagendra, V., Shang, N., Nadukuru, R., Jones, A. C., Małolepsza, E., Dikilitas, O., Kullo, I. J., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01869-1 · Cited by 168
Artificial Intelligence in mental health and the biases of language based models
Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164
Screening for Osteoporosis to Prevent Fractures
US Preventive Services Task Force, Nicholson, W. K., Silverstein, M., Wong, J. B., Chelmow, D. P., Coker, T. R., Davis, E. M., Jaén, C. R., Krousel-Wood, M. A., Lee, S., et al. (2025) · JAMA · doi:10.1001/jama.2024.27154 · Cited by 163
Racial and Ethnic Disparities in Bone Health and Outcomes in the United States
Noel, S. E., Santos, M. P., & Wright, N. C. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4417 · Cited by 159
Medical Artificial Intelligence and Human Values
Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K. (2024) · New England Journal of Medicine · doi:10.1056/nejmra2214183 · Cited by 149
Ahmed, S., Nutt, C. T., Eneanya, N. D., Reese, P. P., Sivashanker, K., Morse, M., Sequist, T., & Mendu, M. L. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06280-5 · Cited by 147
Race, Ethnicity, Hypertension, and Heart Disease
Ogunniyi, M. O., Commodore-Mensah, Y. Y., & Ferdinand, K. C. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.06.017 · Cited by 168
Diversity in Alzheimer's disease drug trials: the importance of eligibility criteria
Franzen, S., Smith, J. E., van den Berg, E., Rivera Mindt, M., van Bruchem‐Visser, R. L., Abner, E. L., Schneider, L. S., Prins, N. D., Babulal, G. M., & Papma, J. M. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12433 · Cited by 146
Delgado, C., Baweja, M., Burrows, N. R., Crews, D. C., Eneanya, N. D., Gadegbeku, C. A., Inker, L. A., Mendu, M. L., Miller, W. G., Moxey‐Mims, M., et al. (2021) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2021.03.008 · Cited by 150
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149
Toward a fine-scale population health monitoring system
Belbin, G. M., Cullina, S., Wenric, S., Soper, E. R., Glicksberg, B. S., Torre, D., Moscati, A., Wojcik, G. L., Shemirani, R., Beckmann, N. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.034 · Cited by 136
Schindler, S. E., Karikari, T. K., Ashton, N. J., Henson, R. L., Yarasheski, K. E., West, T., Meyer, M. R., Kirmess, K. M., Li, Y., Saef, B. A., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000200358 · Cited by 141
Tangri, N., Moriyama, T., Schneider, M. P., Virgitti, J. B. J., Nicola, L. D., Arnold, M., Barone, S., Peach, E. J., Wittbrodt, E. T., Chen, H., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067386 · Cited by 142
Health inequities and the inappropriate use of race in nephrology
Eneanya, N. D., Boulware, L. E., Tsai, J., Bruce, M. A., Ford, C. L., Harris, C., Morales, L. S., Ryan, M. J., Reese, P. P., Thorpe, R. J., et al. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00501-8 · Cited by 140
Eliminating Race-Based Medicine
Wright, J. L., Davis, W. S., Joseph, M. M., Ellison, A. M., Heard-Garris, N. J., Johnson, T. L., & the AAP Board Committee on Equity (2022) · Pediatrics · doi:10.1542/peds.2022-057998 · Cited by 140
Ren, Y., Loftus, T. J., Datta, S., Ruppert, M. M., Guan, Z., Miao, S., Shickel, B., Zheng, F., Giordano, C. R., Upchurch, G. R., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.11973 · Cited by 139
Understanding and Mitigating Bias in Imaging Artificial Intelligence
Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024) · RadioGraphics · doi:10.1148/rg.230067 · Cited by 138
Mapping the regulatory landscape for artificial intelligence in health within the European Union
Schmidt, J., Schutte, N. M., Buttigieg, S., Novillo-Ortiz, D., Sutherland, E., Anderson, M., de Witte, B., Peolsson, M., Unim, B., Pavlova, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01221-6 · Cited by 155
Trustworthy Augmented Intelligence in Health Care
Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., & Irons, M. (2022) · Journal of Medical Systems · doi:10.1007/s10916-021-01790-z · Cited by 132
Considering Biased Data as Informative Artifacts in AI-Assisted Health Care
Ferryman, K. S., Mackintosh, M. E., & Ghassemi, M. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2214964 · Cited by 142
Racial, Gender, and Socioeconomic Disparities in Liver Transplantation
Nephew, L. D., & Serper, M. (2021) · Liver Transplantation · doi:10.1002/lt.25996 · Cited by 132
Multi-ancestry polygenic mechanisms of type 2 diabetes
Smith, K., Deutsch, A. J., McGrail, C., Kim, H., Hsu, S. C., Huerta‐Chagoya, A., Mandla, R., Schroeder, P. H., Westerman, K. E., Szczerbiński, Ł., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02865-3 · Cited by 140
Clinical Implications of Removing Race From Estimates of Kidney Function
Diao, J. A., Wu, G. J., Taylor, H. A., Tucker, J. K., Powe, N. R., Kohane, I. S., & Manrai, A. K. (2020) · JAMA · doi:10.1001/jama.2020.22124 · Cited by 125
Ensuring that biomedical AI benefits diverse populations
Zou, J., & Schiebinger, L. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103358 · Cited by 124
Segar, M. W., Jaeger, B. C., Patel, K. V., Nambi, V., Ndumele, C. E., Correa, A., Butler, J., Chandra, A., Ayers, C. R., Rao, S. G., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.053134 · Cited by 126
Demographic bias in misdiagnosis by computational pathology models
Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Song, A. H., Jaume, G., Yang, Y., Hartvigsen, T., Dyer, E., Lu, M. Y., Lipková, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02885-z · Cited by 148
Narrowing the Gap: Imaging Disparities in Radiology
Waite, S., Scott, J. A., & Colombo, D. (2021) · Radiology · doi:10.1148/radiol.2021203742 · Cited by 130
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Birhane, A., Ruane, E., Laurent, T., Brown, M. S., Flowers, J., Ventresque, A., & Dancy, C. L. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533157 · Cited by 160
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Breathett, K. K., Spatz, E. S., Kramer, D. B., Essien, U. R., Wadhera, R. K., Peterson, P. N., Ho, P. M., & Nallamothu, B. K. (2021) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.007868 · Cited by 122
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Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac078 · Cited by 119
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Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021) · Journal of Public Health Policy · doi:10.1057/s41271-021-00319-5 · Cited by 117
Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117
Aristizabal, P., Winestone, L. E., Umaretiya, P. J., & Bona, K. O. (2021) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_320499 · Cited by 123
Segar, M. W., Hall, J. L., Jhund, P., Powell-Wiley, T., Morris, A., Kao, D. P., Fonarow, G., Hernandez, R., Ibrahim, N. E., Rutan, C., et al. (2022) · JAMA cardiology · doi:10.1001/jamacardio.2022.1900 · Cited by 109
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Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa‐Mullan, I., Koski, E., & Das, A. K. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.3909 · Cited by 116
Delgado, C., Baweja, M., Burrows, N. R., Crews, D. C., Eneanya, N. D., Gadegbeku, C. A., Inker, L. A., Mendu, M. L., Miller, W. G., Moxey‐Mims, M., et al. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021010039 · Cited by 116
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McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 123
Uche-Anya, E. N., Anyane‐Yeboa, A., Berzin, T. M., Ghassemi, M., & May, F. P. (2022) · Gut · doi:10.1136/gutjnl-2021-326271 · Cited by 122
Elmaleh-Sachs, A., Balte, P., Oelsner, E. C., Allen, N. B., Baugh, A. D., Bertoni, A. G., Hankinson, J., Pankow, J. S., Post, W. S., Schwartz, J. E., et al. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202107-1612oc · Cited by 114
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Diao, J. A., He, Y., Khazanchi, R., Tiako, M. J. N., Witonsky, J. I., Pierson, E., Rajpurkar, P., Elhawary, J. R., Melas-Kyriazi, L., Yen, A., et al. (2024) · New England Journal of Medicine · doi:10.1056/nejmsa2311809 · Cited by 121
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Samal, L., Fu, H. N. C., Camara, D. S., Wang, J., Bierman, A. S., & Dorr, D. A. (2021) · Health Services Research · doi:10.1111/1475-6773.13860 · Cited by 122
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Loftus, T. J., Shickel, B., Ozrazgat‐Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-022-00562-3 · Cited by 115
Straw, I., & Wu, H. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100457 · Cited by 109
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Neophytou, A. M., Kioumourtzoglou, M., Goin, D. E., Darwin, K. C., & Casey, J. A. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa252 · Cited by 101
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Ivanov, O., Wolf, L. A., Brecher, D., Lewis, E., Masek, K., Montgomery, K., Andrieiev, Y., McLaughlin, M., Liu, S., Dunne, R., et al. (2020) · Journal of Emergency Nursing · doi:10.1016/j.jen.2020.11.001 · Cited by 110
Metadata
- Authors
- Vyas, D. A., Eisenstein, L. G., & Jones, D. S.
- Affiliations
- Harvard University (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2020-08-27
- Volume
- 383
- Issue
- 9
- Pages
- 874-882
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W3035885149
- Semantic Scholar
- 07a84399ee2befea66e7ed0560fc32f0e4e8a070
- Cited by
- 1,731
- References
- 35
- Citations by year
- 2026: 191 · 2025: 262 · 2024: 295 · 2023: 337 · 2022: 295 · 2021: 267 · 2020: 78
- FWCI
- 144.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Anthropology › Race, Genetics, and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical algorithms, racial bias, algorithmic fairness, health disparities, algorithmic discrimination
- MeSH terms
- Algorithms, Cardiology, Female, Humans, Male, Nephrology, Obstetrics, Urology, Risk Assessment, Racial Groups, Healthcare Disparities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-04, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-04 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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