Lee Sanders, MD, MPH

Physician Scientist, Policy Analyst, and AI-Solutions Designer

    1. AI and Health Solutions: The Role of the Human in the Loop

    2. Trust and Respect: Anchors for Desigining AI health Solutions

    3. AI and Health Equity

    4. Ai and Child Health

    5. AI and Behavioral Health

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  • Bay Area, California

Dr. Lee Sanders is a general pediatrician and Professor of Pediatrics and Health Policy at Stanford University, where he serves as the Chief of the Division of General Pediatrics. He holds joint appointments in the Department of Epidemiology and Population Health, and the Freeman Spogli Institute for International Studies. Dr. Sanders teaches undergraduate and graduate students in the Human Biology Program, in Health Policy and at the Hasso Plattner Institute of Design (Stanford d.School). A national expert in health literacy, Dr. Sanders applies a literacy lens to advance maternal and child health equity. He directs the Stanford Health Literacy Lab, which addresses child health and educational disparities through innovative redesigns of primary care involving youth and families. Recognized as a Robert Wood Johnson Foundation Generalist Physician Faculty Scholar, he leads foundational research in health literacy to enhance maternal and child health outcomes.

Dr. Sanders has advised numerous prestigious organizations, including the NIH, CDC, FDA, and the American Academy of Pediatrics. He leads a multi-disciplinary research team that provides analytic guidance to policymakers at various levels, focusing on improving care for children with medical complexity (CMC) through health-services research and human-centered design. As Principal Investigator on multiple federally funded studies, Dr. Sanders explores the efficacy of AI-driven behavioral interventions, including the Greenlight Study Team, which has developed an efficacious, AI-driven platform to prevent early childhood obesity, and GoalKeeper, an AI-driven tool to advance care coordination in chronic-illness care. He collaborates with the Graduate School of Education on a unique virtual birth cohort, linking health and educational data to address child health disparities; with the Immigration Policy Lab on research to understand the impact of immigration policy on child health; and with Biomedical Data Sciences, on AI-driven platforms that apply computer-vision algorithms to improve relational health. Dr. Sanders earned a BA in History and Science from Harvard University, an MD from Stanford University, and an MPH from the University of California, Berkeley.

Previously, he was on faculty at the University of Miami, where he directed the Jay Weiss Center for Social Medicine and Health Equity, and served as Medical Director for Children’s Medical Services South Florida, coordinating care for over 10,000 low-income children with special health care needs. Since returning to Stanford, he has held several leadership roles, including Co-director for the Center for Policy, Outcomes and Prevention, which conducts health services research and digital health research in primary care, and Co-director of the Family Advocacy Program, which addresses social determinants of community health. Fluent in Spanish, Dr. Sanders co-directs the Complex Primary Care Clinic at Stanford Children’s Health, providing multi-disciplinary care for children with complex chronic conditions. Outside of his professional life, he is a father of two daughters who remind him to listen more and talk less.

More on Dr. Lee Sanders

More Background On Lee’s Talks

1. AI and Health Solutions: The Role of the Human in the Loop

Description:

This topic explores the crucial interplay between artificial intelligence and human oversight in healthcare. It addresses how AI can enhance clinical decision-making, improve patient outcomes, and streamline workflows while emphasizing the importance of human judgment, empathy, and ethical considerations. The discussion includes case studies highlighting successful integrations of AI with healthcare professionals and the potential risks of over-reliance on technology.

Audience:

Healthcare professionals (doctors, nurses, and administrators), AI developers, healthcare policymakers, and medical students interested in the evolving landscape of technology in healthcare.

2. Trust and Respect: Anchors for Designing AI Health Solutions

Description:

This session delves into the foundational principles of trust and respect in the development and implementation of AI health solutions. It highlights the necessity of building trustworthy AI systems that prioritize patient safety, data privacy, and ethical standards. Participants will learn how to engage stakeholders effectively and create AI solutions that are not only innovative but also respect patient autonomy and promote collaborative care.

Audience:

Healthcare innovators, tech developers, ethicists, patient advocacy groups, and policymakers focused on ethical AI practices in healthcare.

3. AI and Health Equity

Description:

This topic addresses the role of AI in promoting health equity and reducing disparities in healthcare access and outcomes. It explores how AI can be utilized to identify at-risk populations, inform resource allocation, and enhance patient engagement. The discussion will also cover potential biases in AI algorithms and strategies for ensuring that AI tools serve diverse communities fairly and effectively.

Audience:

Public health professionals, researchers, community health advocates, policymakers, and data scientists working on health equity initiatives.

4. AI and Child Health

Description:

This session focuses on the application of AI technologies to improve child health outcomes, from predictive analytics for early diagnosis to personalized treatment plans. It examines how AI can support pediatricians in managing chronic conditions, enhancing preventive care, and engaging families in the health management process. The talk will also touch on ethical considerations and the importance of child-centric AI design.

Audience:

Pediatricians, child health researchers, AI developers, public health officials, and parents interested in advancements in child healthcare.

5. AI and Behavioral Health

Description:

This topic investigates the intersection of AI and behavioral health, highlighting how AI tools can aid in diagnosing mental health conditions, predicting treatment responses, and facilitating therapeutic interventions. It emphasizes the importance of integrating human touch in AI applications and the ethical implications of using AI in sensitive areas of mental health care. Case studies will illustrate successful AI implementations in behavioral health settings.

Audience:

Mental health professionals (psychologists, psychiatrists, and counselors), AI researchers, policymakers in mental health, and advocates for mental health technology.

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