# HEAL: A framework for health equity assessment of machine learning performance

DevFeed: [HEAL: A framework for health equity assessment of machine learning performance](<https://devfeed.tech/articles/heal-a-framework-for-health-equity-assessment-of-machine-learning-performance-28559.md>)

Original publisher: [Read original article](<http://blog.research.google/2024/03/heal-framework-for-health-equity.html>)

Author: Google AI (noreply@blogger.com)

Published: 2024-03-15T18:22:00Z

Content type: article

Language: en

Sources: [Google Research](<https://devfeed.tech/sources/google-research.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [fairness](<https://devfeed.tech/tags/fairness.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>)

## AI overview

Google Research presents HEAL, a framework for quantitatively assessing whether machine-learning health technologies perform equitably. The framework evaluates model performance in relation to pre-existing health disparities and focuses on populations with the worst health outcomes, illustrated through a dermatology AI model case study.

## Source excerpt

Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the diagnostic technology. In dermatology for example, skin cancer outcomes are worse for populations such as minorities, those with lower socioeconomic status, or individuals with limited healthcare access. While there is great promise in recent advances in machine learning (ML) and artificial intelligence (AI) to help improve healthcare, this transition from research to bedside must be accompanied by a careful understanding of whether and how they impact health equity. Health equity is defined by public health organizations as fairness of opportunity for everyone to be as healthy as possible. Importantly, equity may be different from equality. For example, people with greater barriers to improving their health may require more or different effort to experience this fair opportunity. Similarly, equity is not fairness as defined in the AI for healthcare literature. Whereas AI fairness often strives for equal performance of the AI technology across different patient populations, this does not center the goal of prioritizing performance with respect to pre-existing health disparities. Health equity considerations. An intervention (e.g., an ML-based tool, indicated in dark blue) promotes health equity if it helps reduce existing disparities in health outcomes (indicated in lighter blue). In "Health Equity Assessment of machine Learning performance (HEAL): a framework and dermatology AI model case study", published in The Lancet eClinicalMedicine, we propose a methodology to quantitatively assess whether ML-based health technologies perform equitably. In other words, does the ML model perform well for those with the worst he