# Algorithm Fairness in AI for Medicine and Healthcare

**Authors:** Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F. K. Williamson, Ming Y. Lu, Sharifa Sahai, Faisal Mahmood  
**arXiv:** [2110.00603](https://arxiv.org/abs/2110.00603)  
**Date:** 2021-10-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2110.00603v1/

## Key points

- Algorithm fairness in AI systems in healthcare is a critical challenge to provide equitable care
- Recent assessments have revealed significant disparities in diagnosis, treatment, and billing based on race sub-populations
- Origins of algorithmic biases include image acquisition discrepancies, genetic variations, and labeling practices
- Factors contribute to healthcare inequalities and hinder efforts towards fair and unbiased care
- Novel approaches like decentralized learning methods and disentanglement techniques aim to mitigate bias in AI algorithms
- Enhancing model explainability is crucial for promoting fairer outcomes in medical decision-making and patient care delivery

## Summary

In the realm of artificial intelligence (AI) systems in healthcare, ensuring algorithm fairness is a critical challenge to provide equitable care. Recent assessments of AI models segmented by race sub-populations have uncovered significant disparities in how patients are diagnosed, treated, and billed for medical services. This article delves into the intricate domain of fairness in machine learning within the context of contemporary healthcare issues. It elucidates on the origins of algorithmic biases within current clinical processes, such as image acquisition discrepancies, genetic variations, and inconsistencies in intra-observer labeling practices. Ultimately, these factors contribute to healthcare inequalities and hinder efforts towards providing fair and unbiased care to all patients. To address this issue, novel approaches have been explored such as decentralized learning methods and disentanglement techniques that aim to mitigate bias in AI algorithms. Additionally, enhancing model explainability has also been identified as a crucial aspect in promoting fairer outcomes in medical decision-making and patient care delivery. Through shedding light on these emerging strategies, this perspective offers insights into effectively addressing algorithmic biases to promote fairer outcomes in medicine and healthcare. The authors - Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F. K. Williamson, Ming Y. Lu, Sharifa Sahai, and Faisal Mahmood - present a comprehensive overview of the challenges and opportunities in achieving algorithm fairness within AI applications for medicine and healthcare.