The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; none of the 48 sources its claims cite is checked for support yet.
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
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Claims the paper makes
41 Not checked yet · 24 Own interpretation · 11 No basis given
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.
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
“Instead, if adjustments deter clinicians from offering clinical services to certain patients, they risk baking inequity into the system.”
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. 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.
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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.
- 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.
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- 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
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Items 601–700 of 1,003
Artificial Intelligence and Algorithmic Bias
Williams, N. H. (2023) · The International Library of Bioethics · doi:10.1007/978-3-031-48262-5_1 · Cited by 15
Miano, T. A., Barreto, E. F., McNett, M., Martin, N. D., Sakhuja, A., Andrews, A., Basu, R. K., & Ablordeppey, E. (2024) · Critical Care Medicine · doi:10.1097/ccm.0000000000006237 · Cited by 13
The complexities of race and health
Danso, A., & Danso, Y. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0225 · Cited by 13
Camacho, E., Brady, R. O., Lizano, P. L., Keshavan, M. S., & Torous, J. B. (2021) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2021.100032 · Cited by 11
A Review of Research on Disparities in the Care of Black and White Patients With Cancer in Detroit
Simon, M. S., Raychaudhuri, S., Hamel, L. M., Penner, L. A., Schwartz, K., Harper, F. W. K., Thompson, H. S., Booza, J. C., Coté, M. L., Schwartz, A. G., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.690390 · Cited by 13
Removing Race from Kidney Disease Diagnosis
Quaggin, S. E., & Palevsky, P. M. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021091284 · Cited by 13
McAdams‐DeMarco, M., Chu, N. M., & Segev, D. L. (2020) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2020.07.010 · Cited by 13
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 Medicine · doi:10.1093/pm/pnac204 · Cited by 13
New Recommendations for Breast Cancer Screening—In Pursuit of Health Equity
Pace, L. E., & Keating, N. L. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.11638 · Cited by 13
Artificial Intelligence in migrant health: a critical perspective on opportunities and risks
Matlin, S. A., Claron, I. M. M., Merone, J., Netto, G., Takian, A., Zaman, M. H., & Saso, L. (2025) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2025.101421 · Cited by 16
Hamsal, M., & Binsar, F. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2566441 · Cited by 12
Bias and Oversight in Clinical AI: A Review of Decision Support Tools and Equity Frameworks
Adegunle, F., Chhatwal, K., Arab, S., Alabdaljabar, M. S., Raslan, M. A., Sayed, O., & Goldsweig, A. M. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10229-5 · Cited by 13
Malinowska, J. K., & Żuradzki, T. (2022) · Synthese · doi:10.1007/s11229-022-04004-2 · Cited by 19
Primary care physician use of patient race and polygenic risk scores in medical decision-making
Kerman, B. J., Brunette, C. A., Harris, E. J., Antwi, A. A., Lemke, A. A., & Vassy, J. L. (2023) · Genetics in Medicine · doi:10.1016/j.gim.2023.100800 · Cited by 12
Rokoff, L. B., Seshasayee, S. M., Carwile, J. L., Rifas‐Shiman, S. L., Botelho, J. C., Gordon, C. M., Hauser, R. B., James-Todd, T. M., Young, J. G., Rosen, C. J., et al. (2023) · Environmental Research · doi:10.1016/j.envres.2023.115629 · Cited by 12
Disparities in anterior cervical discectomy and fusion provision and outcomes for cervical stenosis
Das, A., Vazquez, S. M., Stein, A., Greisman, J. D., Ng, C., Ming, T., Vaserman, G., Spirollari, E., Naftchi, A., Dominguez, J. F., et al. (2023) · North American Spine Society Journal (NASSJ) · doi:10.1016/j.xnsj.2023.100217 · Cited by 12
Mousa, J., Peterson, M. N., Crowson, C. S., Achenbach, S. J., Atkinson, E. J., Amin, S., Khosla, S., Davis, J. M., & Myasoedova, E. (2023) · The Journal of Rheumatology · doi:10.3899/jrheum.2022-1293 · Cited by 12
Commodore-Mensah, Y. Y., Wang, D., Jeon, Y., Foti, K. E., McEvoy, J. W., Coresh, J., Tang, O., Echouffo-Tcheugui, J. B., Christenson, R. H., Ndumele, C. E., et al. (2023) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2023.100526 · Cited by 12
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Kahouadji, N. (2024) · Information · doi:10.3390/info15050252 · Cited by 13
Incorporating Sex-Diverse and Gender-Inclusive Perspectives in Higher Education Biology Courses
Harris, B. N., Lewis, A. K., Sharpe, S. L., Orr, T. J., Martine, C. T., & Josefson, C. C. (2024) · Integrative and Comparative Biology · doi:10.1093/icb/icae054 · Cited by 12
Cerdeña, J. P., Plaisime, M. V., & Borrell, L. N. (2024) · Journal of General Internal Medicine · doi:10.1007/s11606-024-08919-z · Cited by 16
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MLHOps: Machine Learning Health Operations
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Underrepresented populations in genomic research: a qualitative study of researchers’ perspectives
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Ethnic diversity in precision medicine: a reality or an aspiration?
Misra, S. (2025) · Diabetologia · doi:10.1007/s00125-025-06513-4 · Cited by 14
Powell‐Wiley, T. M. (2020) · JAMA Cardiology · doi:10.1001/jamacardio.2020.6573 · Cited by 12
Ethical implications of emotion mining in medicine
Straw, I. (2020) · Health Policy and Technology · doi:10.1016/j.hlpt.2020.11.006 · Cited by 8
Hong, M., Seymour, S., Stopka, T. J., Bandanza, L., Crocker, E., Morgan, A., & Beletsky, L. A. (2021) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000000901 · Cited by 12
Reporting on Race and Racial Disparities in Breast Cancer
Loehrer, A. P., Cevallos, P. C., Jiménez, R. T., & Wong, S. L. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000005191 · Cited by 12
Zahran, S., Bourdiec, A., Zhang, X., & Sapir‐Pichhadze, R. (2021) · Human Immunology · doi:10.1016/j.humimm.2021.11.007 · Cited by 12
The End of Race Correction in Spirometry for Pulmonary Function Testing and Surgical Implications
Bonner, S. N., & Wakeam, E. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005431 · Cited by 12
Trainee Perspectives on Race, Antiracism, and the Path toward Justice in Kidney Care
Heffron, A. S., Khazanchi, R., Nkinsi, N. T., Bervell, J. A., Cerdeña, J. P., Diao, J. A., Eisenstein, L. G., Gillespie, N. J., Hongsermeier-Graves, N., Kane, M., et al. (2022) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.02500222 · Cited by 12
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Pavlakis, M. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000198 · Cited by 12
Leslie, W. D. (2023) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4896 · Cited by 12
Lamb, L. R., Mercaldo, S. F., Ghaderi, K. F., Carney, A. S., & Lehman, C. D. (2023) · Radiology · doi:10.1148/radiol.223077 · Cited by 14
Historic Use of Race-Based Spirometry Values Lowered Transplant Priority for Black Patients
Hidalgo, D. C., Ramos, K. J., Harlan, E. A., Holguín, F., Forno, E., Weiner, D. J., & Griffith, M. F. (2023) · CHEST Journal · doi:10.1016/j.chest.2023.10.009 · Cited by 12
Streja, E., Norris, K. C., Budoff, M. J., Hashemi, L., Akbilgiç, O., & Kalantar‐Zadeh, K. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000672 · Cited by 12
Yan, G., Nee, R., Scialla, J. J., Greene, T., Yu, W., Cheung, A. K., & Norris, K. C. (2022) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2021.12.004 · Cited by 12
The future of AI in critical care is augmented, not artificial, intelligence
Liu, V. X. (2020) · Critical Care · doi:10.1186/s13054-020-03404-5 · Cited by 16
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) · Canadian Journal of Pain · doi:10.1080/24740527.2022.2161272 · Cited by 12
Uncoupling inequality: Reflections on the ethics of benchmarks for digital media
Washington, A. L., Rhue, L. A., Nakamura, L., & Stevens, R. C. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.352 · Cited by 11
A Prediction Model for Severe Maternal Morbidity and Mortality After Delivery Hospitalization
Frey, H. A., Ashmead, R., Farmer, A., Kim, Y. H., Shellhaas, C., Oza‐Frank, R. B., Jackson, R. D., Costantine, M. M., & Lynch, C. D.-J. (2023) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000005281 · Cited by 11
Race as a Component of Cardiovascular Disease Risk Prediction Algorithms
Vasan, R. S., Rao, S. G., & van den Heuvel, E. R. (2023) · Current Cardiology Reports · doi:10.1007/s11886-023-01938-y · Cited by 11
Evaluating Similarity Variables for Peer Matching in Digital Health Storytelling
Saksono, H., Morris, V., Parker, A. G., & Gajos, K. Z. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610060 · Cited by 11
Huang, J., Li, H., Xu, Y., Qian, C., Wei, Y., & Sun, M. (2024) · Renal Failure · doi:10.1080/0886022x.2023.2294155 · Cited by 11
Global perspectives on governing healthcare AI: prioritising safety, equity and collaboration
Laein, G. D. (2024) · BMJ Leader · doi:10.1136/leader-2023-000904 · Cited by 13
Understanding Disparities in Post Hoc Machine Learning Explanation
Mhasawade, V., Rahman, S., Haskell-Craig, Z., & Chunara, R. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659043 · Cited by 14
Rokoff, L. B., Rifas‐Shiman, S. L., Aris, I. M., Lin, P. D., Rosen, C. J., Calafat, A. M., Gordon, C. M., Oken, E., & Fleisch, A. F. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c08480 · Cited by 11
Advancing equitable access to innovation in breast cancer
O’Reilly, S., Vaz-Luís, I., Adam, V. S., Razis, E. D., Urruticoechea, A., Arahmani, A., Carrasco, E., Chua, B. H., Bliss, J. M., Straehle, C., et al. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00768-1 · Cited by 11
What Is Fair? Defining Fairness in Machine Learning for Health
Gao, J., Chou, B., McCaw, Z. R., Thurston, H., Varghese, P., Hong, C., & Gronsbell, J. L. (2025) · Statistics in Medicine · doi:10.1002/sim.70234 · Cited by 17
Will Losing Black Physicians Be a Consequence of the COVID-19 Pandemic?
Filut, A., & Carnes, M. L. (2020) · Academic Medicine · doi:10.1097/acm.0000000000003651 · Cited by 11
A Critical Look at Race‐Based Practices in Rheumatology Guidelines
Olson, R. M., & Feldman, C. H. (2021) · Arthritis Care & Research · doi:10.1002/acr.24645 · Cited by 11
Kidney Disease among People Who Are Incarcerated
Murphy, M., Ding, A., Berk, J., Rich, J., & Bayliss, G. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.01910221 · Cited by 11
Predicted Cure and Survival Among Transplant Recipients With a Previous Cancer Diagnosis
Engels, E. A., Haber, G. B., Hart, A., Lynch, C. F., Li, J., Pawlish, K. S., Qiao, B., Yu, K. J., & Pfeiffer, R. M. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.21.01195 · Cited by 11
Housten, A. J., Hoover, D. S., Britton, M., Bevers, T. B., Street, R. L., McNeill, L. H., Strong, L. L., Hersch, J. K., McCaffery, K. J., & Volk, R. J. (2022) · Journal of General Internal Medicine · doi:10.1007/s11606-021-07336-w · Cited by 12
Black Lives Matter: Moving from passion to action in academic medical institutions
Nguyen, B., Guh, J., & Freeman, B. D. (2022) · Journal of the National Medical Association · doi:10.1016/j.jnma.2021.12.009 · Cited by 13
Chan, A. K.-H., Shahrestani, S., Ballatori, A. M., Orrico, K. O., Manley, G. T., Tarapore, P. E., Huang, M. C., Dhall, S. S., Chou, D., Mummaneni, P. V., et al. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001980 · Cited by 11
Race does matter: venous thromboembolism in trauma patients with isolated severe pelvic fractures
Liasidis, P., Benjamin, E., Jakob, D. A., Ding, L., Lewis, M. R., & Δημητριάδης, Δ. (2022) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-022-02044-4 · Cited by 11
Legha, R. K., Richards, M., Mabeza, R. M., Gordon-Achebe, K., & Kataoka, S. (2023) · MedEdPORTAL · doi:10.15766/mep_2374-8265.11349 · Cited by 11
Lee, A., & Padilla, C. (2022) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001142 · Cited by 11
Eneanya, N. D., Kostelanetz, S., & Mendu, M. L. (2021) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2020.12.001 · Cited by 12
Medical School Admissions — A Movable Barrier to Ending Health Care Disparities?
Williams, W. W. (2021) · New England Journal of Medicine · doi:10.1056/nejme2102832 · Cited by 12
Exploring biases in predictive modelling across diverse populations
Swami, N. S., Corti, C., Curigliano, G., Celi, L. A., & Dee, E. C. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00307-x · Cited by 11
Snell, L., Valiquette, C. R., Avery, E., Moltaji, S., & Forrest, C. R. (2022) · Plastic Surgery · doi:10.1177/22925503221083287 · Cited by 10
Understanding Health Disparities in Preeclampsia: A Literature Review
Conklin, M. B., Wells, B. M., Doe, E. M., Strother, A. M., Tarasiewicz, M. E. B., Via, E. R., Conrad, L. B., & Farias‐Eisner, R. (2023) · American Journal of Perinatology · doi:10.1055/a-2008-7167 · Cited by 10
The NICHD Fetal 3D Study: A Pregnancy Cohort Study of Fetal Body Composition and Volumes
Grantz, K. L., Lee, W., Chen, Z., Hinkle, S. N., Mack, L. M., Cortés, M. S., Gonçalves, L. F., Espinoza, J., Gore‐Langton, R. E., Sherman, S. J., et al. (2023) · American Journal of Epidemiology · doi:10.1093/aje/kwad210 · Cited by 10
Young, C. D., Katki, H. A., Cheung, L. C., Rivera, M. P., Robbins, H. A., Aldrich, M. C., Blume, J. D., Chaturvedi, A. K., & Landy, R. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.2172 · Cited by 10
Khalid, S., Kim, H., & Kim, H. S. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112221 · Cited by 10
McElrath, T. F., Jeyabalan, A., Khodursky, A. B., Moe, A. B., Lee, M., Jain, M., Goetzl, L. M., Sutton, E. F., Simmons, P. M., Saade, G. R., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.21792 · Cited by 12
Real-World Data and Causal Machine Learning to Enhance Drug Development
Anagnostopoulos, C., van der Schaar, M., Collet, J., & Vecino, R. H. (2025) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-025-00849-0 · Cited by 10
Cowling, T. E., Bellot, A., Boyle, J. M., Walker, K., Kuryba, A., Galbraith, S., Aggarwal, A. K., Braun, M., Sharples, L. D., & van der Meulen, J. (2020) · British Journal of Cancer · doi:10.1038/s41416-020-01034-w · Cited by 10
Cerdeña, J. P., Jaswaney, R., & Plaisime, M. V. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-021-06843-0 · Cited by 10
Future developments in training
Last, K., Power, N. R., Dellière, S., Velikov, P., Šterbenc, A., Antunovic, I. A., Lopes, M. J., Schweitzer, V. A., & Barac, A. M. (2021) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2021.06.032 · Cited by 10
Singh, S. (2021) · Health An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · doi:10.1177/13634593211061215 · Cited by 10
Tran, A. T., Hart, A. J., Spertus, J. A., Jones, P. G., McNally, B., Malik, A. O., & Chan, P. S. (2021) · Resuscitation · doi:10.1016/j.resuscitation.2021.12.021 · Cited by 10
Guppy, M., Glasziou, P., Beller, E., Flavel, R. J., Shaw, J. E., Barr, E., & Doust, J. A. (2021) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2021-111767 · Cited by 10
How the University of Washington Implemented a Change in eGFR Reporting
Nkinsi, N. T., & Young, B. A. (2022) · Kidney360 · doi:10.34067/kid.0006522021 · Cited by 10
Reflexivity, Cultural Safety, and improving the health of racially minoritised communities
Gopal, D. P., Douglass, C. W., Wong, S., Khan, I., & Lokugamage, A. U. (2022) · The Lancet · doi:10.1016/s0140-6736(22)00459-7 · Cited by 10
R.E.A.C.T: A framework for role modeling anti-racism in the clinical learning environment
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A Role for History in Inclusive Nursing Education
Tobbell, D. A. (2022) · Journal of Nursing Education · doi:10.3928/01484834-20220602-08 · Cited by 13
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Wang, S. X. Y., Chi, K., Shankar, M., Israni, S. T., Verghese, A., & Zulman, D. M. (2022) · The American Journal of Medicine · doi:10.1016/j.amjmed.2022.12.014 · Cited by 10
Understanding Disparities in Healthcare: Implications for Health Systems and AI Applications
Koski, E., Scheufele, E. L., Karunakaram, H., Foreman, M. A., Felix, W., & Dankwa‐Mullan, I. (2022) · Health informatics · doi:10.1007/978-3-031-07912-2_25 · Cited by 10
External Validation of the UTICalc with and Without Race for Pediatric Urinary Tract Infection
Smith, A. G., Kshetrapal, A., Boles, L. H., Simon, N.-J. E., Kurs‐Lasky, M., Shope, T. R., Shaikh, N., & Ramgopal, S. (2023) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2023.113681 · Cited by 10
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Race, Ethnicity, and Ancestry in Clinical Pathways: A Framework for Evaluation
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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).
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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