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
Open accessAbstract
Individual subscribers can store articles and searches using a feature on the Journal's website (NEJM.org)called "My Account."Each article and search result links to this feature.Users can create personal folders and move articles into them for convenient retrieval later.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
- 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.
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.
- 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 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.
- 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
“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.
- 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 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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
- 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.
- 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.
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.
- 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.
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.
- 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.
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.
- 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.
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.
- 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.
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.
- Checkable: fails — It may be too vague to check 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. 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.
- 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.
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Based: fails — The paper may give no reason why it holds.
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.
- 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.
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.
- 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.
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.
- 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
“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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- 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.
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.
- 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.
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.
- 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.
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.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
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.
- 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.
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.
- 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.
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.
- 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. 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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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.
- Checkable: fails — It may be too vague to check against evidence.
- 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. 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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- 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.
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.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Checkable: fails — It may be too vague to check against evidence.
- 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. 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.
- 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.
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.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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.
- Checkable: fails — It may be too vague to check against evidence.
- 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. 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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- 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
Items 301–400 of 1,003
Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning
Celeste, C., Ming, D., Broce, J., Ojo, D. P., Drobina, E., Louis‐Jacques, A. F., Gilbert, J. E., Fang, R., & Parker, I. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00953-1 · Cited by 32
Haraldsen, I., Hatlestad‐Hall, C., Marra, C., Renvall, H., Maestú, F., Acosta-Hernández, J., Alfonsín, S., Andersson, V., Anand, A., Ayllón, V., et al. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1289406 · Cited by 34
Wang, Y., He, Y., Shi, Y., Qian, D. C., Gray, K. J., Winn, R. A., & Martin, A. R. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.04.002 · Cited by 34
Revisiting performance metrics for prediction with rare outcomes
Adhikari, S., Normand, S. T., Bloom, J. P., Shahian, D. M., & Rose, S. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211038754 · Cited by 31
Green, K.-A., Wolinsky, R., Parnell, S. J., Campo, D. D., Nathan, A. S., Garg, P. S., Kaplan, S. E., & Dasgupta, S. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004531 · Cited by 34
Carmichael, S. L., Abrams, B. F., Ayadi, A. E., Lee, H. C., Liu, C., Lyell, D. J., Lyndon, A., Main, E. K., Mujahid, M. S., Tian, L., et al. (2021) · Women s Health Issues · doi:10.1016/j.whi.2021.11.006 · Cited by 34
Diao, J. A., Wu, G. J., Wang, J. K., Kohane, I. S., Taylor, H. A., Tighiouart, H., Levey, A. S., Inker, L. A., Powe, N. R., & Manrai, A. K. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2022070818 · Cited by 31
Genetics of Alzheimer’s Disease in the African American Population
Logue, M. W., Dasgupta, S., & Farrer, L. A. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12165189 · Cited by 29
Bonner, S. N., Lagisetty, K. H., Reddy, R. M., Engeda, Y., Griggs, J. J., & Valley, T. S. (2023) · JAMA Surgery · doi:10.1001/jamasurg.2023.3239 · Cited by 31
Pfohl, S., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J., & Shah, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533166 · Cited by 30
Listen to Black Women: Do Black Feminist and Womanist Health Policy Analyses
Barlow, J. N., & Breya, J. (2020) · Women s Health Issues · doi:10.1016/j.whi.2020.11.001 · Cited by 30
Madara, J., Miyamoto, S., Farley, J. E., Gong, M. N., Gorham, M., Humphrey, H. J., Irons, M., Mehrotra, A., Resneck, J., Rushton, C. H., et al. (2021) · NAM Perspectives · doi:10.31478/202105b · Cited by 32
Cohen, S., Dagan, N., Cohen-Inger, N., Ofer, D., & Rokach, L. (2021) · IEEE Access · doi:10.1109/access.2021.3091622 · Cited by 30
Spector‐Bagdady, K., Tang, S., Jabbour, S., Price, W. N., Bracic, A., Creary, M., Kheterpal, S., Brummett, C. M., & Wiens, J. (2021) · Health Affairs · doi:10.1377/hlthaff.2021.01197 · Cited by 29
Bundy, J. D., Mills, K. T., Anderson, A. H., Yang, W., Chen, J., He, J., & CRIC Study Investigators* (2022) · Annals of Internal Medicine · doi:10.7326/m21-2928 · Cited by 32
Algorithm Fairness in AI for Medicine and HealthcarePreprint
Chen, R. J., Chen, T., Lipková, J., Wang, J. J., Williamson, D. F. K., Lu, M. Y., Sahai, S., & Mahmood, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00603 · Cited by 31
Promoting inclusion, diversity, and equity in pain science
Palermo, T. M., Davis, K. D., Bouhassira, D., Hurley, R. W., Katz, J. D., Keefe, F. J., Schatman, M. E., Turk, D. C., & Yarnitsky, D. (2023) · Pain · doi:10.1097/j.pain.0000000000002847 · Cited by 30
Body Mass Index Thresholds for Asians: A Race Correction in Need of Correction?
Bajaj, S., Zhong, A., Zhang, A., & Stanford, F. C. (2024) · Annals of Internal Medicine · doi:10.7326/m24-0161 · Cited by 25
Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine
Cho, M. K., & Martinez‐Martin, N. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2146785 · Cited by 32
AACC/NKF Guidance Document on Improving Equity in Chronic Kidney Disease Care
Pierre, C. C., Marzinke, M. A., Ahmed, S. B., Collister, D., Colón-Franco, J. M., Hoenig, M. P., Lorey, T. S., Palevsky, P. M., Palmer, O. M. P., Rosas, S. E., et al. (2023) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfad022 · Cited by 29
Ackerson, B. K., Tartof, S. Y., Chen, L. H., Contreras, R., Reyes, I., Ku, J. H., Pellegrini, M., Schmidt, J., & Bruxvoort, K. J. (2024) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiae331 · Cited by 29
Tailoring Risk Prediction Models to Local Populations
Zinzuwadia, A. N., Mineeva, O., Li, C., Farukhi, Z. M., Giulianini, F., Cade, B. E., Chen, L., Karlson, E. W., Paynter, N. P., Mora, S., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2024.2912 · Cited by 31
Essentialism and Exclusion: Racism in Cancer Risk Prediction Models
Waters, E. A., Colditz, G. A., & Davis, K. L. (2021) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djab074 · Cited by 29
Hoenig, M. P., Mann, A., & Pavlakis, M. (2021) · Clinical Transplantation · doi:10.1111/ctr.14467 · Cited by 29
How Hypertension Guidelines Address Social Determinants of Health
Razon, N., Hessler, D., Bibbins-Domingo, K. B., & Gottlieb, L. M. (2021) · Medical Care · doi:10.1097/mlr.0000000000001649 · Cited by 32
Grantz, K. L., Grewal, J., Kim, S., Grobman, W. A., Newman, R., Owen, J., SCISCIONE, A. C., Skupski, D. W., Chien, E. K., Wing, D. A., et al. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.12.006 · Cited by 29
How to Act Upon Racism—not Race—as a Risk Factor
Chokshi, D. A., Foote, M., & Morse, M. E. (2022) · JAMA Health Forum · doi:10.1001/jamahealthforum.2022.0548 · Cited by 29
Advancing Anti-Racism and Anti-Ableism in Transition: Equity-Oriented Indicators for Research
Scott, L. A., & Shogren, K. A. (2023) · Career Development and Transition for Exceptional Individuals · doi:10.1177/21651434231189665 · Cited by 33
FEV 1 Q: a race-neutral approach to assessing lung function
Balasubramanian, A., Wise, R. A., Stanojevic, S., Miller, M. R., & McCormack, M. C. (2024) · European Respiratory Journal · doi:10.1183/13993003.01622-2023 · Cited by 29
Elbasheir, Z., & Fouad, M. A. H. (2002) · PubMed · Cited by 29
Tumor Mutations Across Racial Groups in a Real-World Data Registry
Kamran, S. C., Xie, J., Cheung, A. T. M., Mavura, M. Y., Song, H., Palapattu, E. L., Madej, J., Gusev, A., Allen, E. M. V., & Huang, F. W. (2021) · JCO Precision Oncology · doi:10.1200/po.21.00340 · Cited by 28
Bias in Reinforcement Learning: A Review in Healthcare Applications
Smith, B., Khojandi, A., & Vasudevan, R. K. (2023) · ACM Computing Surveys · doi:10.1145/3609502 · Cited by 29
Hypertension Guidelines and Interventions
Bludorn, J., & Railey, K. M. (2023) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2023.07.002 · Cited by 28
Allareddy, V., Oubaidin, M., Rampa, S., Venugopalan, S. R., Elnagar, M. H., Yadav, S., & Lee, M. K. (2023) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12721 · Cited by 31
Paccoud, I., Leist, A. K., Schwaninger, I., van Kessel, R., & Klucken, J. (2024) · Digital Health · doi:10.1177/20552076241277705 · Cited by 31
Race, science and (im)precision medicine
Adigbli, G. (2020) · Nature Medicine · doi:10.1038/s41591-020-1115-x · Cited by 28
Randomised controlled trials in medical AI: ethical considerations
Grote, T. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107166 · Cited by 32
Acosta, D. A., & Skorton, D. J. (2021) · The American Journal of Medicine · doi:10.1016/j.amjmed.2021.04.034 · Cited by 28
Nonyel, N. P., Wisseh, C., Riley, A. C., Campbell, H. E., Butler, L. M., & Shaw, T. E. (2021) · American Journal of Pharmaceutical Education · doi:10.5688/ajpe8584 · Cited by 28
Rethinking the Race Adjustment in Pulmonary Function Testing
Schluger, N. W., Dozor, A. J., & Jung, Y. E. G. (2021) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202107-890ps · Cited by 28
Russo, R. G., Li, Y., Ðoàn, L. N., Ali, S. H., Siscovick, D. S., Kwon, S. C., & Yi, S. S. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.121.022721 · Cited by 28
Revising the atherosclerotic cardiovascular disease calculator without race
Vyas, D. A., James, A., Kormos, W., & Essien, U. R. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00258-2 · Cited by 28
Self-citationRiaz, I. B., Islam, M., Khan, A. M., Naqvi, S. A. A., Siddiqi, R. L., Khakwani, K. Z. R., Asghar, N., Ikram, W., Hussain, S. A., Singh, P. P., et al. (2022) · The American Journal of Medicine · doi:10.1016/j.amjmed.2022.03.042 · Cited by 28
Magudia, K. A., Bridge, C. P., Bay, C. P., Farah, S., Babić, A., Fintelmann, F. J., Brais, L. K., Andriole, K. P., Wolpin, B. M., & Rosenthal, M. H. (2022) · American Journal of Roentgenology · doi:10.2214/ajr.22.27977 · Cited by 27
Shogren, K. A. (2023) · Intellectual and developmental disabilities · doi:10.1352/1934-9556-61.2.172 · Cited by 31
Recommendations on screening for primary prevention of fragility fractures
Thériault, G., Limburg, H., Klarenbach, S. W., Reynolds, D. L., Riva, J. J., Thombs, B. D., Tessier, L. A., Grad, R. M., & Wilson, B. J. (2023) · Canadian Medical Association Journal · doi:10.1503/cmaj.221219 · Cited by 32
Assessing racial bias in type 2 diabetes risk prediction algorithms
Cronjé, H. T., Katsiferis, A., Elsenburg, L. K., Andersen, T. O., Rod, N. H., Nguyen, T., & Varga, T. V. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001556 · Cited by 34
Racial/Ethnic Differences in Bone Mineral Density for Osteoporosis
Wu, Q. F., & Dai, J. (2023) · Current Osteoporosis Reports · doi:10.1007/s11914-023-00838-y · Cited by 31
Clinical prediction models using machine learning in oncology: challenges and recommendations
Collins, G. S., Chester-Jones, M., Gerry, S., Ma, J., Matos, J., Sehjal, J., Tsegaye, B., & Dhiman, P. (2025) · BMJ Oncology · doi:10.1136/bmjonc-2025-000914 · Cited by 27
Tran, T. T. L., Yun, G., & Kim, S. (2024) · BMC Nephrology · doi:10.1186/s12882-024-03793-7 · Cited by 34
Using race to estimate glomerular filtration and its impact in kidney transplantation
Kuppachi, S. C., Norman, S. P., Lentine, K. L., & Axelrod, D. A. (2020) · Clinical Transplantation · doi:10.1111/ctr.14136 · Cited by 27
Horton, W. B., Barros, A. J., Andris, R. T., Clark, M. T., & Moorman, J. R. (2021) · Critical Care Medicine · doi:10.1097/ccm.0000000000005171 · Cited by 27
Appropriate use of machine learning in healthcare
Ozaydin, B., Berner, E. S., & Cimino, J. J. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100041 · Cited by 30
Health equity considerations in guideline development: a rapid scoping review
Shaver, N., Bennett, A., Beck, A., Skidmore, B., Traversy, G., Brouwers, M. C., Little, J., Moher, D., Moore, A. E., & Persaud, N. (2023) · CMAJ Open · doi:10.9778/cmajo.20220130 · Cited by 28
Racial disparities, FRAX, and the care of patients with osteoporosis
Lewiecki, E. M., Wright, N. C., & Singer, A. J. (2020) · Osteoporosis International · doi:10.1007/s00198-020-05655-y · Cited by 26
Thompson, H. M. (2020) · Health Education & Behavior · doi:10.1177/1090198120963102 · Cited by 26
The Emerging Hazard of AI‐Related Health Care Discrimination
Hoffman, S. (2020) · The Hastings Center Report · doi:10.1002/hast.1203 · Cited by 26
Bragg‐Gresham, J. L., Zhang, X., Le, D., Heung, M., Shahinian, V. B., Morgenstern, H., & Saran, R. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.35636 · Cited by 26
Busby, J., Heaney, L. G., Brown, T., Chaudhuri, R., Dennison, P., Gore, R. B., Jackson, D. J., Mansur, A. H., Menzies-Gow, A. N., Message, S. D., et al. (2021) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2021.09.034 · Cited by 27
Jones, K. F., Laury, E. R., Sanders, J. J., Starr, L. T., Rosa, W. E., Booker, S. Q., Wachterman, M. W., Jones, C. A., Hickman, S. E., Merlin, J. S., et al. (2021) · Journal of Palliative Medicine · doi:10.1089/jpm.2021.0502 · Cited by 26
Deconstructing the Way We Use Pulmonary Function Test Race-Based Adjustments
Ramsey, N., Apter, A. J., Israel, E., Louisias, M., Noroski, L. M., Nyenhuis, S. M., Ogbogu, P. U., Perry, T. T., Wang, J., & Davis, C. M. (2022) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2022.01.023 · Cited by 27
Heiden, B. T., Eaton, D. B., Chang, S., Yan, Y., Baumann, A. A., Schoen, M. W., Patel, M. R., Kreisel, D., Nava, R. G., Meyers, B. F., et al. (2022) · CHEST Journal · doi:10.1016/j.chest.2022.03.045 · Cited by 26
Improving Cancer Care for Patients With CKD: The Need for Changes in Clinical Trials
Sprangers, B., Perazella, M. A., Lichtman, S. M., Rosner, M. H., & Jhaveri, K. D. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.06.005 · Cited by 26
Buery-Joyner, S. D., Baecher-Lind, L. E., Clare, C. A., Hampton, B. S., Moxley, M. D., Ogunyemi, D. A., Pradhan, A. A., Sims, S. M., Whetstone, S., Woodland, M. B., et al. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.09.014 · Cited by 26
Ahmed, Y., van Bakel, P. A. J., Hou, H., Sukul, D., Likosky, D. S., van Herwaarden, J. A., Watkins, D. C., Ailawadi, G., Patel, H. J., & Thompson, M. P. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281811 · Cited by 26
Multidisciplinary considerations of fairness in medical AI: A scoping review
Wang, Y., Song, Y., Ma, Z., & Han, X. (2023) · Int. J. Medical Informatics · doi:10.1016/j.ijmedinf.2023.105175 · Cited by 20
Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification
Ghosh, A. K., Venkatraman, S., Nanna, M. G., Safford, M. M., Colantonio, L. D., Brown, T. M., Pinheiro, L. C., Peterson, E. D., Návar, A. M., Sterling, M. R., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.4520 · Cited by 26
Preschool impulse oscillometry predicts active asthma and impaired lung function at school age
Knihtilä, H. M., Stubbs, B. J., Carey, V. J., Laranjo, N., Zeiger, R. S., Bacharier, L. B., O’Connor, G. T., Weiss, S. T., & Litonjua, A. A. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.12.025 · Cited by 27
Kanis, J. А., Harvey, N. C., Lorentzon, M., Liu, E., Schini, M., Abrahamsen, B., Adachi, J. D., Alokail, M. S., Borgström, F., Bruyère, O., et al. (2024) · Osteoporosis International · doi:10.1007/s00198-024-07162-w · Cited by 29
Recommendations to promote fairness and inclusion in biomedical AI research and clinical use
Griffin, A. C., Wang, K., Leung, T. I., & Facelli, J. C. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104693 · Cited by 24
Transplant Clinician Opinions on Use of Race in the Estimation of Glomerular Filtration Rate
Doshi, M. D., Singh, N. K., Hippen, B. E., Woodside, K. J., Mohan, P., Byford, H. L., COOPER, M. M., Dadhania, D. M., Ainapurapu, S., & Lentine, K. L. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.05490421 · Cited by 25
Draper, J. K., Feltner, C., Schaaf, E. B. V., & Mieses, A. M. (2022) · Academic Medicine · doi:10.1097/acm.0000000000004718 · Cited by 25
Race-based medicine in the point-of-care clinical resource UpToDate: A systematic content analysis
Cerdeña, J. P., Asabor, E. N., Plaisime, M. V., & Hardeman, R. R. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101581 · Cited by 25
Mayne, G., Buckley, A. B., & Ghidei, L. A. (2023) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000005332 · Cited by 26
Black Boxes and Bias in AI Challenge Autonomy
Klugman, C. M. (2021) · The American Journal of Bioethics · doi:10.1080/15265161.2021.1926587 · Cited by 25
Racial Disparities in Sports Cardiology
Krishnan, S., Guseh, J. S., Chukumerije, M. T., Grant, A. J., Dean, P. N., Hsu, J. J., Husaini, M., Phelan, D. M. J., Shah, A. B., Stewart, K., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2024.1899 · Cited by 25
Race, ethnicity, and considerations for data collection and analysis in research studies
Sharghi, S., Khalatbari, S. H., Laird, A. E., Lapidus, J., Enders, F., Meinzen‐Derr, J. K., Tapia, A. L., & Ciolino, J. D. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.632 · Cited by 24
Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates
Coots, M., Linn, K. A., Goel, S., Navathe, A. S., & Parikh, R. B. (2024) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-071823-112058 · Cited by 25
National egg-freezing trends: cycle and patient characteristics with a focus on race/ethnicity
Katler, Q. S., Shandley, L. M., Hipp, H. S., & Kawwass, J. F. (2021) · Fertility and Sterility · doi:10.1016/j.fertnstert.2021.02.032 · Cited by 24
The « race » correction in estimating glomerular filtration rate: an European point of view
Delanaye, P., Mariat, C., Cavalier, E., Glassock, R. J., Gemenne, F., & Pottel, H. (2021) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000739 · Cited by 24
An Abolitionist Approach to Antiracist Medical Education
Asmerom, B., Legha, R. K., Mabeza, R. M., & Núñez, V. (2022) · The AMA Journal of Ethic · doi:10.1001/amajethics.2022.194 · Cited by 24
Machine learning and health need better values
Ghassemi, M., & Mohamed, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00595-9 · Cited by 27
Stipelman, C. H., Kukhareva, P., Trepman, E., Nguyen, Q., Valdez, L., Kenost, C., Hightower, M., & Kawamoto, K. (2022) · Yearbook of Medical Informatics · doi:10.1055/s-0042-1742518 · Cited by 26
Ancestry: How Researchers Use It, and What They Mean by It
Dauda, B., Molina, S. J., Allen, D. S., Fuentes, A., Ghosh, N., Mauro, M., Neale, B. M., Panofsky, A. L., Sohail, M., Zhang, S., et al. (2022) · Frontiers in Genetics · doi:10.2139/ssrn.4219080 · Cited by 31
Screening for Prediabetes and Diabetes: Clinical Performance and Implications for Health Equity
O’Brien, M. J., Zhang, Y., Bailey, S. C., Khan, S. S., Ackermann, R. T., Ali, M. K., Benoit, S. R., Imperatore, G., Holliday, C. S., & Bullard, K. M. (2023) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2023.01.007 · Cited by 23
AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare
Ossa, L. A., Milford, S. R., Rost, M., Leist, A. K., Shaw, D., & Elger, B. S. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00486-0 · Cited by 24
Cortinois, A. A., & Birn, A. (2021) · Global Policy · doi:10.1111/1758-5899.12982 · Cited by 23
Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging
van Assen, M., Beecy, A., Gershon, G., Newsome, J. M., Trivedi, H., & Gichoya, J. W. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01190-x · Cited by 25
Colorblind Racial Ideology and Physician Use of Race in Medical Decision-Making
Okah, E. D., Thomas, J., Westby, A., & Cunningham, B. A. (2021) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-021-01141-1 · Cited by 23
The Presence 5 for Racial Justice Framework for anti‐racist communication with Black patients
Brown‐Johnson, C. G., Cox, J., Shankar, M., Baratta, J., Leon, G. D., Garcia, R., Hollis, T., Verano, M., Henderson, K., Upchurch, M., et al. (2022) · Health Services Research · doi:10.1111/1475-6773.14015 · Cited by 25
Re-Aiming Equity Evaluation in Clinical Decision Support
Ingraham, N. E., Jones, E., King, S., Dries, J., Phillips, M., Loftus, T. J., Evans, H. L., Melton, G. B., & Tignanelli, C. J. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005661 · Cited by 23
Deceased Donor Characteristics and Kidney Transplant Outcomes
Sharif, A. (2022) · Transplant International · doi:10.3389/ti.2022.10482 · Cited by 23
Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?
Binkley, C. E., Kemp, D. S., & Scully, B. B. (2022) · The AMA Journal of Ethic · doi:10.1001/amajethics.2022.773 · Cited by 23
Bindler, R. J., Haverkamp, H. C., O’Flanagan, H., Whicker, J., Rappold, A. G., Walden, V. P., & Postma, J. M. (2022) · Journal of Asthma · doi:10.1080/02770903.2022.2160345 · Cited by 24
Attitudes and beliefs regarding race‐targeted genetic testing of Black people: A systematic review
Iltis, A. S., Rolf, L., Yaeger, L., Goodman, M. S., & DuBois, J. M. (2023) · Journal of Genetic Counseling · doi:10.1002/jgc4.1653 · Cited by 23
Osei-Tutu, K. B., Duchesne, N., Barnabe, C. C. M., Richardson, L., Razack, S., Thoma, B., & Maniate, J. M. (2023) · Canadian Medical Education Journal · doi:10.36834/cmej.75844 · Cited by 25
‘I'm still here, I'm alive and breathing’: The experience of Black Americans with long COVID
Bergmans, R. S., Chambers‐Peeple, K., Yu, C., Xiao, L. Z., Wegryn‐Jones, R., Martin, A., Dell’Imperio, S., Aboul‐Hassan, D., Williams, D. A., Clauw, D. J., et al. (2023) · Journal of Clinical Nursing · doi:10.1111/jocn.16733 · Cited by 24
Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility
Landy, R., Gomez, I., Caverly, T. J., Kawamoto, K., Rivera, M. P., Robbins, H. A., Young, C. D., Chaturvedi, A. K., Cheung, L. C., & Katki, H. A. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.31155 · Cited by 25
Race adjustments in clinical algorithms can help correct for racial disparities in data quality
Zink, A., Obermeyer, Z., & Pierson, E. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2402267121 · Cited by 32
Separate and Unequal: Race-Based Algorithms and Implications for Nephrology
Schmidt, I. M., & Waikar, S. S. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020081175 · Cited by 23
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 ›
Are you an author, or do you see a problem with this page? Request a review