HEAL: A framework for health equity assessment of machine learning performance
Google AI Blog
March 15, 2024
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).
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 health outcomes for the condition(s) the model is meant to address? This goal anchors on the principle that health equity should prioritize and measure model performance with respect to disparate health outcomes, which may be due to a number of factors that include structural inequities (e.g., demographic, social, cultural, political, economic, environmental and geographic).
The health equity framework (HEAL)
Framework for Health Equity Assessment of machine Learning performance (HEAL). Our guiding principle is to avoid exacerbating health inequities, and these steps help us identify disparities and assess for inequitable model performance to move towards better outcomes for all.
Case study on a dermatology model
prior work. This example dermatology model was trained to classify 288 skin conditions using a development dataset of 29k cases. The input to the model consists of three photos of a skin concern along with demographic information and a brief structured medical history. The output consists of a ranked list of possible matching skin conditions.
Using the HEAL framework, we evaluated this model by assessing whether it prioritized performance with respect to pre-existing health outcomes. The model was designed to predict possible dermatologic conditions (from a list of hundreds) based on photos of a skin concern and patient metadata. Evaluation of the model is done using a top-3 agreement metric, which quantifies how often the top 3 output conditions match the most likely condition as suggested by a dermatologist panel. The HEAL metric is computed via the anticorrelation of this top-3 agreement with health outcome rankings.
We used a dataset of 5,420 teledermatology cases, enriched for diversity in age, sex and race/ethnicity, to retrospectively evaluate the model’s HEAL metric. The dataset consisted of “store-and-forward” cases from patients of 20 years or older from primary care providers in the USA and skin cancer clinics in Australia. Based on a review of the literature, we decided to explore race/ethnicity, sex and age as potential factors of inequity, and used sampling techniques to ensure that our evaluation dataset had sufficient representation of all race/ethnicity, sex and age groups. To quantify pre-existing health outcomes for each subgroup we relied on measurements from public databases endorsed by the World Health Organization, such as Years of Life Lost (YLLs) and Disability-Adjusted Life Years (DALYs; years of life lost plus years lived with disability).
HEAL metric for all dermatologic conditions across race/ethnicity subpopulations, including health outcomes (YLLs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance.
(* Higher is better; measures the likelihood the model performs equitably with respect to the axes in this table.)
HEAL metric for all dermatologic conditions across sexes, including health outcomes (DALYs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance. (* As above.)
HEAL metrics for all cancer and non-cancer dermatologic conditions across age groups, including health outcomes (DALYs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance. (* As above.)
Putting things in context
Pareto condition (discussed further in the paper), which restricts model changes so that outcomes for each subpopulation are either unchanged or improved compared to the status quo, and performance does not worsen for any subpopulation.
The HEAL framework, in its current form, assesses the likelihood that an ML-based model prioritizes performance for subpopulations with respect to pre-existing health disparities for specific subpopulations. This differs from the goal of understanding whether ML will reduce disparities in outcomes across subpopulations in reality. Specifically, modeling improvements in outcomes requires a causal understanding of steps in the care journey that happen both before and after use of any given model. Future research is needed to address this gap.
Conclusion
Acknowledgements
The research described here is joint work across many teams at Google. We are grateful to all our co-authors: Terry Spitz, Malcolm Pyles, Heather Cole-Lewis, Ellery Wulczyn, Stephen R. Pfohl, Donald Martin, Jr., Ronnachai Jaroensri, Geoff Keeling, Yuan Liu, Stephanie Farquhar, Qinghan Xue, Jenna Lester, Cían Hughes, Patricia Strachan, Fraser Tan, Peggy Bui, Craig H. Mermel, Lily H. Peng, Yossi Matias, Greg S. Corrado, Dale R. Webster, Sunny Virmani, Christopher Semturs, Yun Liu, and Po-Hsuan Cameron Chen. We also thank Lauren Winer, Sami Lachgar, Ting-An Lin, Aaron Loh, Morgan Du, Jenny Rizk, Renee Wong, Ashley Carrick, Preeti Singh, Annisah Um'rani, Jessica Schrouff, Alexander Brown, and Anna Iurchenko for their support of this project.
Verticals
airesearch
Originally published on Google AI Blog on 3/15/2024