August 22, 2018
Registration and Breakfast
Artificial Intelligence (AI) and Machine Learning (ML) in Medicine 101
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
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 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?
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.
Session: Levers and Possibilities for Inclusive AI in Healthcare
Introductions and hosted by Margaret Levi
Automating Austerity - High-Tech Challenges to Wellness and Care
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
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
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.
Marc Tessier-Lavigne President, Stanford University
Session: Legal and Institutional Challenges of Implementing Equitable AI in Medicine
Introductions and hosted by Roberta Katz
Mapping the Social Dilemmas of Law and AI
In law and public policy, the issue of how we regulate emerging forms (and applications) of AI is of increasing importance. Some of the really interesting questions are just now going from medium-term dilemmas to completely present-tense problems. Many of the legal/governance questions are neither entirely new (though the context and some of the policy implications are different) nor likely to be easily resolved anytime soon. What distinctive ethical, policy, and legal issues arise in the use of AI in the medical context? How do broader issues associated with AI –– including, for example, the “hand-off” problem, explainability, and long-term erosion of organizational competence –– arise in the medical context? How do we handle issues of human-machine interaction, system safety, bias, and even "personhood" in connection with the use of AI in the medical context?
Irreproducible Research in the AI Era
This session will discuss the challenges of irreproducible science, how the AI era creates new challenges and opportunities for reproducibility and what might this mean for equity and inclusion.
Healthcare Big Data, Predictive Analytics, and Machine Learning: Legal and Ethical Issues
This talk will present a quick tour of legal and ethical issues raised by the use of machine learning and predictive analytics in healthcare. Among the topics we may touch on are AI and equitable healthcare: data and equality; physician role disruption, training gaps and reasons given; risks of automation bias; liability; scarcity and the inevitability of distributive choices; privacy threats; transparency; and trade secrecy.
Machine Learning for Personalizing Medicine: Estimating Individual Effects of Treatment for a More Diverse Patient Base
Machine learning methods offer a unique opportunity to communicate between the results of randomized trials and the questions we need to answer for our patients in clinical settings. This session will discuss the prospects and challenges of translating the results of our most important source of scientific data into personalized clinical practice.
Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare
While there are multiple challenges in clinical use of ML models, imagine how a patient's experience would change if we could predict risks of specific events and take proactive action. In this talk, we will review Stanford Medicine's Program for Artificial Intelligence (AI) in healthcare, with the mission of bringing AI technologies to the clinic, safely, cost effectively and ethically. Using our experience in deploying a prediction model to improve access to palliative care services, we will discuss potential solutions to issues relating to model correctness, interpretability, fairness, and equity as well as issues such as autonomy of decision making and fiduciary responsibility. We will review how keeping the human in the loop can enable fair use of ML in healthcare and why doing so is a necessary form of “presence."
Program Wrap-up: Frameworks for an Inclusive Future of AI in Healthcare
Moderator Tina Hernandez- Boussard
Scholars from Medicine, Engineering and the Humanities will explore frameworks for an inclusive future of AI in healthcare.