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
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
0 of 48 cited sources checked · 0 supported · 0 flagged
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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearNone 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]
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The AHA does not provide a rationale for this adjustment.”
What the sources say
- Not checked yetcites [9] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- 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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Flagged first, then by citations
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 101–200 of 1,003
Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI
Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Mäntymäki, M., & Pappas, I. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10365-3 · Cited by 113
Diao, J. A., Shi, I., Murthy, V. L., Buckley, T. A., Patel, C. J., Pierson, E., Yeh, R. W., Kazi, D. S., Wadhera, R. K., & Manrai, A. K. (2024) · JAMA · doi:10.1001/jama.2024.12537 · Cited by 104
World report on social determinants of health equity: executive summary
World Health Organization (2025) · doi:10.2471/b09387 · Cited by 99
Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models
Ashana, D. C., Anesi, G. L., Liu, V. X., Escobar, G. J., Chesley, C., Eneanya, N. D., Weissman, G. E., Miller, W. D., Harhay, M. O., & Halpern, S. D. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202012-4383oc · Cited by 98
Clinical Impact of the Refit CKD-EPI 2021 Creatinine-Based eGFR Equation
Meeusen, J. W., Kasozi, R. N., Larson, T. S., & Lieske, J. C. (2022) · Clinical Chemistry · doi:10.1093/clinchem/hvab282 · Cited by 101
Global, Race-Neutral Reference Equations and Pulmonary Function Test Interpretation
Moffett, A. T., Bowerman, C., Stanojevic, S., Eneanya, N. D., Halpern, S. D., & Weissman, G. E. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.16174 · Cited by 102
Abubakar, I. I., Gram, L., Lasoye, S., Achiume, E. T., Bécares, L., Bola, G. K., Dhairyawan, R., Lasco, G., McKee, M., Paradies, Y., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01989-4 · Cited by 96
Precision public health in the era of genomics and big data
Roberts, M. C., Holt, K. E., Fiol, G. D., Baccarelli, A., & Allen, C. G. (2024) · Nature Medicine · doi:10.1038/s41591-024-03098-0 · Cited by 99
NIH must confront the use of race in science
Yudell, M., Roberts, D. E., DeSalle, R., Tishkoff, S. A., & signatories, 7. (2020) · Science · doi:10.1126/science.abd4842 · Cited by 95
Fiftieth Anniversary of Uncovering the Tuskegee Syphilis Study: The Story and Timeless Lessons
Tobin, M. J. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202201-0136so · Cited by 113
Cary, M. P., Zink, A., Wei, S., Olson, A., Yan, M., Senior, R., Bessias, S., Gadhoumi, K., Jean-Pierre, G., Wang, D., et al. (2023) · Health Affairs · doi:10.1377/hlthaff.2023.00553 · Cited by 94
Eliminating health care inequities through strengthening access to care
Jindal, M., Chaiyachati, K. H., Fung, V., Manson, S. M., & Mortensen, K. (2023) · Health Services Research · doi:10.1111/1475-6773.14202 · Cited by 127
Dailah, H. G., Koriri, M. D., Sabei, A., Kriry, T., & Zakri, M. (2024) · Healthcare · doi:10.3390/healthcare12242555 · Cited by 114
Lewis, A. C. F., Perez, E. F., Prince, A. E. R., Flaxman, H. R., Gomez, L., Brockman, D., Chandler, P. D., Kerman, B. J., Lebo, M. S., Smoller, J. W., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01117-8 · Cited by 89
Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Alzubaidi, L., Al-Sabaawi, A., Bai, J., Dukhan, A., Alkenani, A. H., Al‐Asadi, A., Alwzwazy, H. A., Manoufali, M., Fadhel, M. A., Albahri, A. S., et al. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4459198 · Cited by 116
Reichman, V., Brachio, S. S., Madu, C. R., Montoya‐Williams, D., & Peña, M.-M. (2021) · Seminars in Fetal and Neonatal Medicine · doi:10.1016/j.siny.2021.101198 · Cited by 89
Anderson, T. S., Wilson, L. M., & Sussman, J. B. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.1302 · Cited by 89
Harnessing EHR data for health research
Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03074-8 · Cited by 87
Straw, I. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101965 · Cited by 86
Race and Pharmacogenomics—Personalized Medicine or Misguided Practice?
Goodman, C. W., & Brett, A. S. (2021) · JAMA · doi:10.1001/jama.2020.25473 · Cited by 86
Artificial Intelligence–enabled Decision Support in Surgery
Loftus, T. J., Altieri, M. S., Balch, J. A., Abbott, K. L., Choi, J., Marwaha, J. S., Hashimoto, D. A., Brat, G. A., Raftopoulos, Y., Evans, H. L., et al. (2023) · Annals of Surgery · doi:10.1097/sla.0000000000005853 · Cited by 92
Advancing healthcare equity through dissemination and implementation science
Baumann, A. A., Shelton, R. C., Kumanyika, S., & Haire-Joshu, D. L. (2023) · Health Services Research · doi:10.1111/1475-6773.14175 · Cited by 99
The Impact of Health Care Algorithms on Racial and Ethnic Disparities
Siddique, S. M., Tipton, K. N., Leas, B. F., Jepson, C., Aysola, J., Cohen, J. B., Flores, E. J., Harhay, M. O., Schmidt, H., Weissman, G. E., et al. (2024) · Annals of Internal Medicine · doi:10.7326/m23-2960 · Cited by 88
Sarkar, R., Martin, C., Mattie, H., Gichoya, J. W., Stone, D. J., & Celi, L. A. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00022-4 · Cited by 85
Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome
Kindschuh, W. F., Baldini, F., Liu, M. C., Gerson, K. D., Liao, J., Lee, H. H., Anton, L., Gajer, P., Ravel, J., Levy, M., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-022-01293-8 · Cited by 90
Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction
Li, F., Wu, P., Ong, H. H., Peterson, J. F., Wei, W., & Zhao, J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104294 · Cited by 91
A translational perspective towards clinical AI fairness
Liu, M., Ning, Y., Teixayavong, S., Mertens, M., Xu, J., Ting, D. S. W., Cheng, L. T.-E., Ong, J. C. L., Teo, Z. L., Tan, T. F., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00918-4 · Cited by 82
Racial Disparity in Oxygen Saturation Measurements by Pulse Oximetry: Evidence and Implications
Jamali, H., Castillo, L. T., Morgan, C. C., Coult, J., Muhammad, J. L., Osobamiro, O. O., Parsons, E. C., & Adamson, R. (2022) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202203-270cme · Cited by 83
Eckardt, K., Delgado, C., Heerspink, H. J. L., Pecoits‐Filho, R., Ricardo, A. C., Stengel, B., Tonelli, M. A., Cheung, M., Jadoul, M. Y., Winkelmayer, W. C., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.05.013 · Cited by 87
Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023) · JAMA Health Forum · doi:10.1001/jamahealthforum.2023.1197 · Cited by 79
Digital health equity frameworks and key concepts: a scoping review
Kim, K. K., & Backonja, U. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf017 · Cited by 84
When non-Whiteness becomes a condition
Merz, L. E., & Achebe, M. (2020) · Blood · doi:10.1182/blood.2020008600 · Cited by 80
Inker, L. A., Couture, S. J., Tighiouart, H., Abraham, A. G., Beck, G. J., Feldman, H. I., Greene, T., Gudnason, V. G., Karger, A. B., Eckfeldt, J. H., et al. (2020) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2020.11.005 · Cited by 80
Conformal Prediction in Clinical Medical Sciences
Vazquez, J., & Facelli, J. C. (2022) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00113-8 · Cited by 92
London, A. J. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100622 · Cited by 76
The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024) · Life · doi:10.3390/life14060652 · Cited by 76
Disparities in peripheral artery disease care: A review and call for action
Demsas, F., Joiner, M. M., Telma, K., Flores, A. M., Teklu, S., & Ross, E. (2022) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2022.05.003 · Cited by 77
Czech, H., Hildebrandt, S., Reis, S. P., Chelouche, T., Fox, M. A., López, E. G., Lepicard, É., Ley, A., Offer, M., Ohry, A., et al. (2023) · The Lancet · doi:10.1016/s0140-6736(23)01845-7 · Cited by 88
Artificial Intelligence in Cardiovascular Care—Part 2: Applications
Jain, S. S., Elias, P., Poterucha, T., Randazzo, M., Lopez Jimenez, F., Khera, R., Perez, M., Ouyang, D., Pirruccello, J., Salerno, M., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.03.401 · Cited by 74
Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model.
Röösli, E., Bozkurt, S., & Hernandez-Boussard, T. (2022) · Scientific Data · doi:10.1038/s41597-021-01110-7 · Cited by 73
Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank
Kolin, D. A., Kulm, S., Christos, P. J., & Elemento, O. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241264 · Cited by 80
Peek, M. E., Vela, M. B., & Chin, M. H. (2020) · Academic Medicine · doi:10.1097/acm.0000000000003710 · Cited by 72
Student-Led Efforts to Advance Anti-Racist Medical Education
Afolabi, T., Borowsky, H. M., Cordero, D. M., Paul, D. W., Said, J. T., Sandoval, R. S., Davis, D. L. F., Ölveczky, D. D., & Chatterjee, A. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004043 · Cited by 75
Race Correction and Spirometry
Braun, L. (2020) · CHEST Journal · doi:10.1016/j.chest.2020.10.046 · Cited by 70
From Diversity and Inclusion to Antiracism in Medical Training Institutions
Argueza, B. R., Saenz, S. R., & McBride, D. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004017 · Cited by 70
Senthil, R., Anand, T., Somala, C. S., & Saravanan, K. M. (2024) · Future Healthcare Journal · doi:10.1016/j.fhj.2024.100182 · Cited by 86
Payne-Sturges, D., Gee, G. C., & Cory‐Slechta, D. A. (2021) · Environmental Health Perspectives · doi:10.1289/ehp8186 · Cited by 71
Racism and Older Black Americans’ Health: a Systematic Review
LaFave, S. E., Suen, J. J., Seau, Q., Bergman, A., Fisher, M. C., Thorpe, R. J., & Szanton, S. L. (2022) · Journal of Urban Health · doi:10.1007/s11524-021-00591-6 · Cited by 79
Zhang, Z., Sun, Z., Fu, J., Lin, Q., Banu, K., Chauhan, K., Planoutene, M., Wei, C., Salem, F. E., Yi, Z., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci146643 · Cited by 67
Diversity Is Not Enough: Advancing a Framework for Antiracism in Medical Education
Solomon, S. R., Atalay, A. J., & Osman, N. Y. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004251 · Cited by 71
Greenberg, M., Gandhi, M., Davidson, C. M., & Carter, E. B. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.04.001 · Cited by 67
Polygenic risk scores in the clinic: Translating risk into action
Lewis, A. C. F., Green, R. C., & Vassy, J. L. (2021) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2021.100047 · Cited by 66
Díaz, A., O’Reggio, R., Norman, M., Thumma, J. R., Dimick, J. B., & Ibrahim, A. M. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000005195 · Cited by 66
Disparities in Osteoporosis Prevention and Care: Understanding Gender, Racial, and Ethnic Dynamics
Onizuka, N., & Onizuka, T. (2024) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-024-09909-8 · Cited by 65
Gender and race interact to influence survival disparities in head and neck cancer
Mazul, A. L., Naik, A. N., Zhan, K. Y., Stepan, K. O., Old, M. O., Kang, S. Y., Nakken, E. R., & Puram, S. V. (2020) · Oral Oncology · doi:10.1016/j.oraloncology.2020.105093 · Cited by 64
Kanj, A., Scanlon, P. D., Yadav, H., Smith, W. T., Herzog, T. L., Bungum, A. O., Poliszuk, D., Fick, E., Lee, A. S., & Niven, A. S. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202303-0613oc · Cited by 65
Roberts, D. E. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32716-1 · Cited by 63
Sex is a defining feature of neuroimaging phenotypes in major brain disorders
Salminen, L. E., Tubi, M. A., Bright, J. K., Thomopoulos, S. I., Wieand, A., & Thompson, P. M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25438 · Cited by 61
Revisiting the impact of race/ethnicity in endometriosis
Bougie, O., Nwosu, I., & Warshafsky, C. (2022) · Reproduction and Fertility · doi:10.1530/raf-21-0106 · Cited by 68
Race, ethnicity, and racism in the nutrition literature: an update for 2020
Duggan, C., Kurpad, A. V., Stanford, F. C., Sunguya, B. F., & Wells, J. C. K. (2020) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqaa341 · Cited by 61
Exploring the 175-year history of spirometry and the vital lessons it can teach us today
Kouri, A., Dandurand, R. J., Usmani, O. S., & Chow, C. (2021) · European Respiratory Review · doi:10.1183/16000617.0081-2021 · Cited by 68
Systemic lupus erythematosus as a genetic disease
Harley, I. T. W., & Sawalha, A. H. (2022) · Clinical Immunology · doi:10.1016/j.clim.2022.108953 · Cited by 68
Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance
Herington, J., McCradden, M. D., Creel, K., Boellaard, R., Jones, E. C., Jha, A. K., Rahmim, A., Scott, P. J. H., Sunderland, J. J., Wahl, R. L., et al. (2023) · Journal of Nuclear Medicine · doi:10.2967/jnumed.123.266110 · Cited by 61
Iron deficiency anemia among women: An issue of health equity
Tang, G. H., & Sholzberg, M. (2024) · Blood Reviews · doi:10.1016/j.blre.2023.101159 · Cited by 60
Brockman, D., Petronio, L., Dron, J. S., Kwon, B. C., Vosburg, T., Nip, L., Tang, A., O’Reilly, M., Lennon, N. J., Wong, B., et al. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01056-0 · Cited by 62
O'Reilly-Shah, V. N., Gentry, K. R., Walters, A. M., Zivot, J., Anderson, C. T., & Tighe, P. J. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.07.040 · Cited by 62
Addressing bias in prediction models by improving subpopulation calibration
Barda, N., Yona, G., Rothblum, G. N., Greenland, P., Leibowitz, M., Balicer, R. D., Bachmat, E., & Dagan, N. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa283 · Cited by 69
Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible
Loftus, T. J., Tighe, P. J., Ozrazgat‐Baslanti, T., Davis, J. P., Ruppert, M. M., Ren, Y., Shickel, B., Kamaleswaran, R., Hogan, W. R., Moorman, J. R., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000006 · Cited by 59
Schmidt, H., Roberts, D. E., & Eneanya, N. D. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106856 · Cited by 59
Using artificial intelligence to create diverse and inclusive medical case vignettes for education
Bakkum, M. J., Hartjes, M. G., Piët, J. D., Donker, E. M., Likic, R., Sanz, E., de Ponti, F., Verdonk, P., Richir, M. C., van Agtmael, M. A., et al. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15977 · Cited by 56
An Antiracism Approach to Conducting, Reporting, and Evaluating Pediatric Critical Care Research
Zurca, A., Suttle, M. L., & October, T. W. (2022) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000002869 · Cited by 56
Postpartum Hemorrhage—Epidemiology, Risk Factors, and Causes
Patek, K. D., & Friedman, P. (2023) · Clinical Obstetrics & Gynecology · doi:10.1097/grf.0000000000000782 · Cited by 56
Orcutt, X., Chen, K., Mamtani, R., Long, Q., & Parikh, R. B. (2025) · Nature Medicine · doi:10.1038/s41591-024-03352-5 · Cited by 58
Those designing healthcare algorithms must become actively anti-racist
Owens, K., & Walker, A. N. (2020) · Nature Medicine · doi:10.1038/s41591-020-1020-3 · Cited by 56
Racialized algorithms for kidney function: Erasing social experience
Braun, L., Wentz, A. E., Baker, R., Richardson, E., & Tsai, J. W. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113548 · Cited by 56
Materialized Oppression in Medical Tools and Technologies
Liao, S., & Carbonell, V. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2044543 · Cited by 57
Ge, J., Kim, W. R., Lai, J. C., & Kwong, A. J. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.03.003 · Cited by 60
Bavli, I., & Jones, D. S. (2022) · New England Journal of Medicine · doi:10.1056/nejmms2206281 · Cited by 55
Self-citationKhor, S., Haupt, E. C., Hahn, E. E., Lyons, L. J., Shankaran, V., & Bansal, A. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.18495 · Cited by 59
Structural Racism and the COVID-19 Experience in the United States
Dickinson, K. L., Roberts, J. D., Banacos, N., Neuberger, L., Koebele, E. A., Blanch‐Hartigan, D., & Shanahan, E. A. (2021) · Health Security · doi:10.1089/hs.2021.0031 · Cited by 54
O'Reilly, D., McGrath, J., & Martin-Loeches, I. (2024) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2023.10.001 · Cited by 55
Obstetric comorbidity scores and disparities in severe maternal morbidity across marginalized groups
Leonard, S. A., Main, E. K., Lyell, D. J., Carmichael, S. L., Kennedy, C. J., Johnson, C., & Mujahid, M. S. (2021) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2021.100530 · Cited by 55
Vasan, R. S., & van den Heuvel, E. R. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00236-3 · Cited by 53
Integrating CKD Into US Primary Care: Bridging the Knowledge and Implementation Gaps
Vassalotti, J. A., & Boucree, S. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.01.1066 · Cited by 54
Johnson, R., Ding, Y., Venkateswaran, V., Bhattacharya, A., Boulier, K. M., Chiu, A. M., Knyazev, S., Schwarz, T., Freund, M. K., Zhan, L., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01106-x · Cited by 50
External Validation of the American Heart Association PREVENT Cardiovascular Disease Risk Equations
Scheuermann, B., Brown, A. R., Colburn, T. D., Hakeem, H., Chow, C. H., & Ade, C. J. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.38311 · Cited by 58
McKay, R. R., Sarkar, R. R., Kumar, A., Einck, J. P., Garraway, I. P., Lynch, J. A., Mundt, A. J., Murphy, J. D., Stewart, T. F., Yamoah, K., et al. (2020) · Cancer · doi:10.1002/cncr.33224 · Cited by 53
Justice, diversity, and research ethics review
Strauss, D. H., White, S., & Bierer, B. E. (2021) · Science · doi:10.1126/science.abf2170 · Cited by 52
Nwanaji‐Enwerem, J. C., Jackson, C. L., Ottinger, M. A., Cárdenas, A., James, K. A., Malecki, K. M. C., Chen, J., Geller, A. M., & Mitchell, U. A. (2021) · Environmental Health Perspectives · doi:10.1289/ehp8392 · Cited by 53
Black Patients Matter in Neurology
Robbins, N. M., Charleston, L. I., Saadi, A., Thayer, Z. M., Codrington, W. U., III, Landry, A., Bernat, J. L., & Hamilton, R. H. (2022) · Neurology · doi:10.1212/wnl.0000000000200830 · Cited by 52
Harris, A. H. S., Trickey, A. W., Eddington, H., Seib, C. D., Kamal, R. N., Kuo, A. C., Ding, Q., & Giori, N. J. (2022) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000002294 · Cited by 51
Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes
Almuwaqqat, Z., Hui, Q., Liu, C., Zhou, J. J., Voight, B. F., Ho, Y.-L. A., Posner, D. C., Vassy, J. L., Gaziano, J. M., Cho, K., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.3062 · Cited by 51
Allen, A. O., Mataraso, S. J., Siefkas, A. C., Burdick, H., Braden, G. L., Dellinger, R. P., McCoy, A., Pellegrini, E., Hoffman, J. L., Green‐Saxena, A., et al. (2020) · JMIR Public Health and Surveillance · doi:10.2196/22400 · Cited by 51
Hundemer, G. L., White, C. A., Norman, P. A., Knoll, G. A., Tangri, N., Sood, M. M., Hiremath, S. S., Burns, K. D., McCudden, C. R., & Akbari, A. (2022) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2022.03.014 · Cited by 51
Phạm, Q., Gamble, A., Hearn, J., & Cafazzo, J. A. (2021) · Journal of Medical Internet Research · doi:10.2196/22320 · Cited by 53
Duggal, V., Thomas, I., Montez‐Rath, M. E., Chertow, G. M., & Tamura, M. K. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020121780 · Cited by 50
Algorithmic fairness of machine learning models for Alzheimer disease progression
Yuan, C., Linn, K. A., & Hubbard, R. A. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.42203 · Cited by 49
Social determinants of health as drivers of inequities in pediatric injury
Trinidad, S. M., & Kotagal, M. (2022) · Seminars in Pediatric Surgery · doi:10.1016/j.sempedsurg.2022.151221 · Cited by 51
A normative framework for artificial intelligence as a sociotechnical system in healthcare
McCradden, M. D., Joshi, S. D., Anderson, J. A., & London, A. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100864 · Cited by 59
An Infrastructure Framework for Remote Patient Monitoring Interventions and Research
Claggett, J. L., Petter, S., Joshi, A. M., Ponzio, T. A., & Kirkendall, E. S. (2024) · Journal of Medical Internet Research · doi:10.2196/51234 · Cited by 68
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 ›
Are you an author, or do you see a problem with this page? Request a review