Google Health Researchers Propose HEAL: A Methodology to Quantitatively Assess whether Machine Learning-based Health Technologies Perform Equitably

      

Health equity is a pressing global concern characterized by persistent and widening health disparities. These disparities, rooted in multifaceted barriers across society, include limited access to healthcare, differential clinical treatment, and variations in diagnostic effectiveness. The integration of artificial intelligence (AI) into clinical decision-making processes offers promise in addressing healthcare challenges, but there’s a recognized risk that AI implementation may exacerbate existing inequities. Academic, clinical, and regulatory sectors are thus calling for a thorough assessment and mitigation of these potential effects through a health equity lens.

The concept of health equity, as defined by public health organizations, centers on providing everyone with a fair opportunity to achieve optimal health outcomes. Unlike equality, health equity acknowledges that individuals facing greater barriers to health improvement may require different or additional efforts to attain fairness in health outcomes. Furthermore, health equity differs from fairness in AI for healthcare, which often prioritizes equal performance across patient populations rather than addressing existing health disparities.

To address the imperative for assessing health equity in AI technologies, a methodology called the Health Equity Assessment for Machine Learning Performance (HEAL) framework is proposed by Researchers from Google Health. This framework offers a quantitative approach to determining whether an AI tool’s performance is equitable, assessing whether the AI model performs better for groups with worse average health outcomes compared to others. By prioritizing and measuring model performance relative to disparate health outcomes influenced by various structural inequities, the HEAL framework aims to ensure health equity considerations are integrated into AI development processes.

The HEAL framework is applied to a dermatology AI model to illustrate its utility. This application demonstrates how the framework can evaluate health equity considerations in AI technologies, offering insights into how these technologies may impact different patient populations. Through this illustrative example, the HEAL framework showcases its potential utility in evaluating and addressing health equity concerns in AI development processes.

Moving forward, there’s a need to encourage explicit assessment of health equity implications in AI development processes. By prioritizing efforts to address health inequities for subpopulations disproportionately affected by structural barriers, the framework aims to reduce disparities in health outcomes. While the HEAL metric may not capture causal relationships or quantify the direct impact of new AI technologies on reducing health outcome disparities, it serves as a valuable tool for identifying instances where model performance may not align with priorities to address pre-existing health disparities.

In conclusion, the HEAL framework represents a significant step forward in addressing health equity considerations in AI technologies. Continued research and development are necessary to refine and expand the application of this framework across various healthcare domains. Integrating equity assessments into AI model development processes has coordinated the exacerbation of health disparities and promoted more equitable healthcare outcomes for all individuals.

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