NEWS · RESEARCH · #229
HEAL: a 4-step framework and metric to assess health equity of ML-based health tools
Google researchers (Schaekermann and Horn) propose HEAL, a four‑step framework and accompanying HEAL metric to quantify whether machine‑learning health technologies prioritize performance for populations with worse pre‑existing health outcomes. The paper—published in The Lancet eClinicalMedicine—defines steps to identify equity‑relevant factors and metrics, quantify pre‑existing disparities, measure model performance across subpopulations, and compute how anticorrelated performance is with health disparities; a dermatology CNN trained on ~29k cases (288 conditions) is presented as an illustrative case study.
KEY POINTS
- Google researchers (Schaekermann and Horn) propose HEAL, a four‑step framework and accompanying HEAL metric to quantify whether machine‑learning health technologies prioritize performance for populations with worse pre‑existing health outcomes.
- The paper—published in The Lancet eClinicalMedicine—defines steps to identify equity‑relevant factors and metrics, quantify pre‑existing disparities, measure model performance across subpopulations, and compute how anticorrelated performance is with health disparities; a dermatology CNN trained on ~29k cases (288 conditions) is presented as an illustrative case study.
- HEAL provides a practical, quantitative way to detect whether ML tools may exacerbate or help address pre‑existing health disparities, guiding model evaluation and improvement even though it does not establish causal impact.
WHY IT MATTERS
HEAL provides a practical, quantitative way to detect whether ML tools may exacerbate or help address pre‑existing health disparities, guiding model evaluation and improvement even though it does not establish causal impact.