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 health outcomes for the condition(s) the model is meant to address?
The health equity framework (HEAL) proposes a 4-step process to estimate the likelihood that an ML-based health technology performs equitably:
- Identify factors associated with health inequities and define tool performance metrics,
- Identify and quantify pre-existing health disparities,
- Measure the performance of the tool for each subpopulation,
- Measure the likelihood that the tool prioritizes performance with respect to health disparities.
The final step’s output is termed the HEAL metric, which quantifies how anticorrelated the ML model’s performance is with health disparities. In other words, does the model perform better with populations that have the worse health outcomes? This 4-step process is designed to inform improvements for making ML model performance more equitable, and is meant to be iterative and re-evaluated on a regular basis.
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.
With this work, we take a step towards encouraging explicit assessment of the health equity considerations of AI technologies, and encourage prioritization of efforts during model development to reduce health inequities for subpopulations exposed to structural inequities that can precipitate disparate outcomes. We should note that the present framework does not model causal relationships and, therefore, cannot quantify the actual impact a new technology will have on reducing health outcome disparities. However, the HEAL metric may help identify opportunities for improvement, where the current performance is not prioritized with respect to pre-existing health disparities.
Case study on a dermatology model
As an illustrative case study, we applied the framework to a dermatology model, which utilizes a convolutional neural network similar to that described in 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 analysis estimated that the model was 80.5% likely to perform equitably across race/ethnicity subgroups and 92.1% likely to perform equitably across sexes. However, while the model was likely to perform equitably across age groups for cancer conditions specifically, we discovered that it had room for improvement across age groups for non-cancer conditions.
Putting things in context
For holistic evaluation, the HEAL metric cannot be employed in isolation. Instead this metric should be contextualized alongside many other factors ranging from computational efficiency and data privacy to ethical values, and aspects that may influence the results (e.g., selection bias or differences in representativeness of the evaluation data across demographic groups).
As an adversarial example, the HEAL metric can be artificially improved by deliberately reducing model performance for the most advantaged subpopulation until performance for that subpopulation is worse than all others. For illustrative purposes, given subpopulations A and B where A has worse health outcomes than B, consider the choice between two models: Model 1 (M1) performs 5% better for subpopulation A than for subpopulation B. Model 2 (M2) performs 5% worse on subpopulation A than B. The HEAL metric would be higher for M1 because it prioritizes performance on a subpopulation with worse outcomes.
