Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Irene Y. Chen · Peter Szolovits B · Marzyeh Ghassemi

399 citations0/0 files storedThe AMA Journal of Ethic

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare

Abstract

Background: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations.Simply put, as health care improves for some, it might not improve for all.Methods: Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status.Results: Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings.Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission.Conclusions: This analysis can provide a framework for assessing and identifying disparate impacts of artificial intelligence in health care. Bias in Machine Learning ModelsWhile health care is an inherently data-driven field, most clinicians operate with limited evidence guiding their decisions.Randomized trials estimate average treatment effects for a trial population, but participants in clinical trials often aren't representative of the patient population that ultimately receives the treatment with respect to race and gender.1,2 As a result, drugs and interventions are not tailored to historically mistreated groups; for example, women, minority groups, and obese patients tend to have generally poorer treatment options and longitudinal health outcomes.[3][4][5][6][7][8][9] Advances in artificial intelligence (AI) and machine learning offer the potential to provide personalized care by taking into account granular patient differences.Machine learning using images, clinical notes, and other electronic health record (EHR) data has been

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References

2012Fairness through awareness 2016Equality of Opportunity in Supervised Learning 2016Why is depression more common among women than among men?2013Learning Fair Representations 2018Word embeddings quantify 100 years of gender and ethnic stereotypes 2018AI can be sexist and racist — it’s time to make it fair 2017On Fairness and Calibration 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

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2021Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults 2024Mental-LLM 2022Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review 2022Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework 2023An adversarial training framework for mitigating algorithmic biases in clinical machine learning 2023Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

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