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AI in Healthcare

AI Is Transforming Medicine: Here’s How We Make Sure It Works for Everyone

Tina Hernandez Boussard, Stanford UniversityJune 26, 2022October 31, 2022
health tech
Sections
  • Health
  • Technology
Topics
  • Algorithmic Bias
  • Artificial Intelligence
  • Data Tools
  • Health Technology

What if your doctor could instantly test dozens of different treatments to discover the perfect one for your body, your health and your values? In my lab at Stanford University School of Medicine, we are working on artificial intelligence (AI) technology to create a “digital twin”: a virtual representation of you based on your medical history, genetic profile, age, ethnicity, and a host of other factors like whether you smoke and how much you exercise.

If you’re sick, the AI can test out treatment options on this computerized twin, running through countless different scenarios to predict which interventions will be most effective. Instead of choosing a treatment regimen based on what works for the average person, your doctor can develop a plan based on what works for you. And the digital twin continuously learns from your experiences, always incorporating the most up-to-date information on your health.

AI is personalizing medicine, but for which people?

While this futuristic idea may sound impossible, artificial intelligence could make personalized medicine a reality sooner than we think. The potential impact on our health is enormous, but so far, the results have been more promising for some patients than others. Because AI is built by humans using data generated by humans, it is prone to reproducing the same biases and inequalities that already exist in our healthcare system.

Read the full article at Venture Beat.

This article was produced by Footnote in partnership with Stanford University.

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Contributed by

Tina Hernandez-Boussard

Tina Hernandez-Boussard

Associate Professor, Stanford Medicine Boussard Lab
Stanford University

Dr. Hernandez-Boussard is an Associate Professor at Stanford University in Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health (by courtesy).

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