Registration and Breakfast

Welcome

Trene Hawkins and Sonoo Thadaney

Artificial Intelligence (AI) and Machine Learning (ML) in Medicine 101

Jonathan Chen

So-called AI technologies can automatically label every cat video on the internet and fill your social media feeds with the perfect clickbait to keep you distracted. Medicine is ripe for the application of AI, given enormous volumes of real world data and ballooning healthcare costs.

This talk will provide a brief introduction to demystify buzzwords from AI to big data to deep learning and machine learning, by drawing analogies to well-established medical tools like risk prediction scores and observational research, and contrasting why mastering Go and operating self-driving cars has not been the same as solving the unique challenges of medicine.

Stanford Center on Poverty & Inequality Panel: A Big Data Approach to Reducing Poverty and Increasing Social Mobility in the United States

Moderator David Grusky

Panelists Geoffrey Cohen and Emma Brunskill

The U.S. has had extremely high rates of poverty for decades. The government spends almost $1 trillion per year, nearly one-fourth of the total federal budget, on means-tested programs to reduce economic hardship and improve social welfare. It is altogether unclear that we are securing anything approaching an optimal return on this investment. To the contrary, the U.S. compares unfavorably to other well-off countries on such key outcomes as poverty, economic mobility, and educational outcomes.

What accounts for this state of affairs? The premise of this work is that substantial headway can be made once we have built credible predictive models of poverty that tell us who is at risk and who will benefit from interventions. In conventional social science models of poverty, a shockingly small amount of the variability is explained, yet no one believes that poverty is a truly random affair. The field lurches from one flavor-of-the-day account to another, and fails to deliver the comprehensive model we need. Meanwhile, scholars study the problem within their independent disciplinary silos and seldom build synergistic collaborations. We propose to harness the power of machine learning to analyze new troves of big data at multiple disciplinary levels of analysis and develop the first powerful predictive models of poverty.

Future of AI in Health and Equity: A Perspective from the National Academy of Medicine

Victor Dzau

Victor Dzau will discuss the current state of health disparities in the United States as well as the impact of social determinants on health outcomes. Furthermore, he will discuss how AI may transform health and medicine and highlight the potential for AI to address or exacerbate existing health disparities.

A Stanford Perspective: Big Data and AI: Will They Reduce Disparities?

Mark Cullen

Despite the promise, the legacy of new technological advances in medicine has not typically been associated with uniform diffusion into practice. As a result, most of the initial benefit of these technologies is conferred to those who already enjoy the most advanced forms of care, widening rather than narrowing health disparities in the population. In this brief talk, Dr. Cullen will present a Stanford perspective on reasons for concern as well as optimism as the benefits of AI are realized by the medical and public health communities.

Break

Session: Levers and Possibilities for Inclusive AI in Healthcare

Introductions and hosted by Margaret Levi

Automating Austerity - High-Tech Challenges to Wellness and Care

Virginia Eubanks

In her new book Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, Virginia Eubanks reports on three new automated decision-making systems having profound impacts on poor and working communities — and frontline care workers — across the United States. In this session, she will suggest lessons that automated eligibility in welfare, algorithmic resource matching in homeless services, and predictive models in child welfare offer to the sustainable and just practice of medicine.

An Inclusive Moral Political Economy for Healthcare

Margaret Levi

In the inclusive moral political economy the value of equity and inclusion is foremost. How do we ensure that a majority of the population has access to the benefits of AI in healthcare, and do not bear a burdensome cost? How do we potentially achieve this?

Panel: Public Health Implications 

Moderator Nirav Shah

Panelists John Auerbach, David Buckeridge, and Chad Heilig

Issues include: infrastructure, priorities, needed reconciliations for incentives, predictive medicine and potential problems for preventive medicine; opportunities for the most out-of-date public health infrastructure systems i.e. at highest risk of failure (e.g. water supply in Flint) that might benefit from AI-facilitated tech-enabled solutions; current AI used today for surveillance, prevention, emergency response; and other public health priorities.

Closing panel discussion: Levers and Possibilities for Inclusive AI in Healthcare

Moderator Margaret Levi

Panelists Virginia Eubanks, Nirav Shah, John Auerbach, Chad Heilig and David Buckeridge

Lunch