President Biden’s Executive Order on Artificial Intelligence—Implications for Health Care Organizations

Mello, Michelle M.; Shah, Nigam H.; Char, Danton S.

doi:10.1001/jama.2023.25051

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…President Biden’s executive order on artificial intelligence (AI) highlighted the importance of developing AI tools that do not contribute to discrimination in health care. 11 Approaches include examining the data used in development to identify issues in subgroup data quality that may limit model generalizability; measuring performance metrics to ensure there are no between-group discrepancies 8 ; and calculating fairness metrics, which provide additional measurements of the extent of bias, 12 with the most common metrics being equal opportunity, equalized odds, and disparate impact. Equal opportunity asserts that the true-positive rates (TPRs) between groups should be equal.…”

Section: Introductionmentioning

confidence: 99%

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Ganta,

Kia,

Parchure

et al. 2024

JAMA Netw Open

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ImportanceMachine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated.ObjectiveTo evaluate whether racial bias exists in a predictive machine learning model that identifies 180-day cancer mortality risk among patients with solid malignant tumors.Design, Setting, and ParticipantsIn this cohort study, a machine learning model to predict cancer mortality for patients aged 21 years or older diagnosed with cancer between January 2016 and December 2021 was developed with a random forest algorithm using retrospective data from the Mount Sinai Health System cancer registry, Social Security Death Index, and electronic health records up to the date when databases were accessed for cohort extraction (February 2022).ExposureRace category.Main Outcomes and MeasuresThe primary outcomes were model discriminatory performance (area under the receiver operating characteristic curve [AUROC], F1 score) among each race category (Asian, Black, Native American, White, and other or unknown) and fairness metrics (equal opportunity, equalized odds, and disparate impact) among each pairwise comparison of race categories. True-positive rate ratios represented equal opportunity; both true-positive and false-positive rate ratios, equalized odds; and the percentage of predictive positive rate ratios, disparate impact. All metrics were estimated as a proportion or ratio, with variability captured through 95% CIs. The prespecified criterion for the model’s clinical use was a threshold of at least 80% for fairness metrics across different racial groups to ensure the model’s prediction would not be biased against any specific race.ResultsThe test validation dataset included 43 274 patients with balanced demographics. Mean (SD) age was 64.09 (14.26) years, with 49.6% older than 65 years. A total of 53.3% were female; 9.5%, Asian; 18.9%, Black; 0.1%, Native American; 52.2%, White; and 19.2%, other or unknown race; 0.1% had missing race data. A total of 88.9% of patients were alive, and 11.1% were dead. The AUROCs, F1 scores, and fairness metrics maintained reasonable concordance among the racial subgroups: the AUROCs ranged from 0.75 (95% CI, 0.72-0.78) for Asian patients and 0.75 (95% CI, 0.73-0.77) for Black patients to 0.77 (95% CI, 0.75-0.79) for patients with other or unknown race; F1 scores, from 0.32 (95% CI, 0.32-0.33) for White patients to 0.40 (95% CI, 0.39-0.42) for Black patients; equal opportunity ratios, from 0.96 (95% CI, 0.95-0.98) for Black patients compared with White patients to 1.02 (95% CI, 1.00-1.04) for Black patients compared with patients with other or unknown race; equalized odds ratios, from 0.87 (95% CI, 0.85-0.92) for Black patients compared with White patients to 1.16 (1.10-1.21) for Black patients compared with patients with other or unknown race; and disparate impact ratios, from 0.86 (95% CI, 0.82-0.89) for Black patients compared with White patients to 1.17 (95% CI, 1.12-1.22) for Black patients compared with patients with other or unknown race.Conclusions and RelevanceIn this cohort study, the lack of significant variation in performance or fairness metrics indicated an absence of racial bias, suggesting that the model fairly identified cancer mortality risk across racial groups. It remains essential to consistently review the model’s application in clinical settings to ensure equitable patient care.

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Section: Introductionmentioning

confidence: 99%

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Ganta,

Kia,

Parchure

et al. 2024

JAMA Netw Open

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“…The National Academies of Medicine (NAM) defines patient safety as “the prevention of harm to patients.” Despite 2 decades of heightened focus on this area, the effectiveness of these efforts remains a topic of debate among many experts . On October 30, 2023, President Biden issued a executive order on AI that mandates federal agencies to develop standards for AI application in health care . The Department of Health and Human Services (HHS) has created a task force and the various agencies are responsible for ensuring AI deployment that minimizes patient harm and ensures continuous learning including the US Food and Drug Administration’s (FDA) role in approval or clearance of software as a medical device, the Office of the National Coordinator (ONC) for Health Information Technology for inclusion of AI within electronic health records, and the Office for Civil Rights (OCR) for ensuring AI algorithms do not violate civil rights rules.…”

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confidence: 99%

Artificial Intelligence Can Be Regulated Using Current Patient Safety Procedures and Infrastructure in Hospitals

Fleisher,

Economou-Zavlanos

2024

JAMA Health Forum

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Digital Humans to Combat Loneliness and Social Isolation: Ethics Concerns and Policy Recommendations

Jecker,

Sparrow,

Lederman

et al. 2024

Hastings Center Report

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Social isolation and loneliness are growing concerns around the globe that put people at increased risk of disease and early death. One much‐touted approach to addressing them is deploying artificially intelligent agents to serve as companions for socially isolated and lonely people. Focusing on digital humans, we consider evidence and ethical arguments for and against this approach. We set forth and defend public health policies that respond to concerns about replacing humans, establishing inferior relationships, algorithmic bias, distributive justice, and data privacy.

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President Biden’s Executive Order on Artificial Intelligence—Implications for Health Care Organizations

Abstract: This Viewpoint discusses a recent executive order by US President Joe Biden about the development and implementation of AI, including the role of government vs the private sector and how the order may affect health care.

Cited by 9 publications

References 5 publications

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Artificial Intelligence Can Be Regulated Using Current Patient Safety Procedures and Infrastructure in Hospitals

Digital Humans to Combat Loneliness and Social Isolation: Ethics Concerns and Policy Recommendations

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