How to Evaluate the Ethics of AI

Precision medicine can be viewed as a double-edged sword. While new data analytics and methodologies promise to revolutionize personalized therapies, there is a risk that these innovations could unintentionally perpetuate health disparities or restrict access to care.

Historically, the conversation on precision medicine has neglected the unique challenges of emergency care. However, a study led by Christian Rose, MD, and Jennifer Newberry, MD, JD, of Stanford Department of Emergency Medicine, in collaboration with colleagues from Brown University, Loyola University Chicago, and UCSF, aims to reset the narrative.

This study explores the techno-ethical complexities of applying precision medicine in the volatile emergency care environment. The authors also introduced a groundbreaking framework designed to steer healthcare providers and policymakers toward the ethical and equitable deployment of precision medicine in emergency settings.

The Stanford-led team employed a qualitative, nominal group technique to identify 91 ethical quandaries that impede the smooth implementation of precision medicine in emergency settings. The study outlines three core ethical themes:

Values — The alignment of patient values with systemic priorities raises concerns, particularly regarding cost-effectiveness and research directions.

Privacy — Data-centric healthcare calls for stringent protocols to protect patient autonomy and data while leveraging the power of precision medicine.

Justice — Justice in the context of precision medicine addresses disparities in healthcare and ensures equitable access to advanced medical treatments and interventions tailored to individual genetic, environmental, and lifestyle factors.

These themes translate into challenges across three pivotal phases:

  • Data acquisition
  • Clinical application
  • Long-term impact

A three-by-three matrix was constructed to map themes to phases, serving as an actionable guide for future innovation in precision emergency medicine.

The study further underscores the necessity for a multidisciplinary approach, advocating for the synergetic involvement of both data science and social emergency medicine teams to bridge gaps in research and practical application.


Updated Spring 2024