Ethical Considerations Regarding the Use of Race in Pulmonary Function Testing

Brems, J.H.; Ferryman, Kadija; McCormack, Meredith C.; Sugarman, Jeremy

doi:10.1016/j.chest.2022.05.006

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…This practice may foster disparities in healthcare as it will miss patients with isolated diffusion defect – a phenotype not only associated with higher mortality but also known to occur more commonly in Black individuals. In addition, use of race-based pulmonary function equations has also been shown to misclassify and underdiagnose Black individuals with severe disease and should be discouraged [32,33 ▪▪ ,34 ▪▪ ,35 ▪ ,36].…”

Section: Diversity and Disparity In The Clinical Presentation Of Sarc...mentioning

confidence: 99%

Diversity and disparity in the treatment and care of sarcoidosis

Ndili

2023

Current Opinion in Pulmonary Medicine

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Purpose of review Clinical presentation and outcomes in patients with sarcoidosis vary by race, gender, ethnicity, and geolocation. African Americans and female individuals have the highest incidence of disease. They are also more likely to present with advanced and more severe forms of disease, and to die from sarcoidosis. African American female individuals have the highest disease-associated mortality, yet this varies by geolocation. The diverse presentation and outcomes in sarcoidosis have often been attributed to genetics and biology, yet this may not be entirely so. Recent findings Several studies have shown that African Americans and female individuals are more likely to earn less and be more socioeconomically disadvantaged in society. Patients with sarcoidosis earning in the lowest income strata presented with the most severe disease and reported more barriers to care. It is plausible that the racial, gender, and geospatial differences in sarcoidosis are more reflective of healthcare disparities than genetics or biology alone. Summary Preventable differences in the burden of disease and in the opportunities to achieve optimal health outcomes that are differentially experienced by groups of people disadvantaged by race, gender, ethnicity, or socioeconomic background should be identified and addressed.

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Section: Diversity and Disparity In The Clinical Presentation Of Sarc...mentioning

confidence: 99%

Diversity and disparity in the treatment and care of sarcoidosis

Ndili

2023

Current Opinion in Pulmonary Medicine

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Use of the Spirometric “Fixed-Ratio” Underdiagnoses COPD in African-Americans in a Longitudinal Cohort Study

et al. 2023

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A translational perspective towards clinical AI fairness

Liu,

Ning,

Teixayavong

et al. 2023

npj Digit. Med.

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Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.g., age, gender/sex, race/ethnicity, socio-economic status, etc.). Various fairness measurements have been developed to detect differences between subgroups as evidence of bias, and bias mitigation methods are designed to reduce the differences detected. This perspective of fairness, however, is misaligned with some key considerations in clinical contexts. The set of sensitive variables used in healthcare applications must be carefully examined for relevance and justified by clear clinical motivations. In addition, clinical AI fairness should closely investigate the ethical implications of fairness measurements (e.g., potential conflicts between group- and individual-level fairness) to select suitable and objective metrics. Generally defining AI fairness as “equality” is not necessarily reasonable in clinical settings, as differences may have clinical justifications and do not indicate biases. Instead, “equity” would be an appropriate objective of clinical AI fairness. Moreover, clinical feedback is essential to developing fair and well-performing AI models, and efforts should be made to actively involve clinicians in the process. The adaptation of AI fairness towards healthcare is not self-evident due to misalignments between technical developments and clinical considerations. Multidisciplinary collaboration between AI researchers, clinicians, and ethicists is necessary to bridge the gap and translate AI fairness into real-life benefits.

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Ethical Considerations Regarding the Use of Race in Pulmonary Function Testing

Cited by 7 publications

References 6 publications

Diversity and disparity in the treatment and care of sarcoidosis

Diversity and disparity in the treatment and care of sarcoidosis

Use of the Spirometric “Fixed-Ratio” Underdiagnoses COPD in African-Americans in a Longitudinal Cohort Study

A translational perspective towards clinical AI fairness

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