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Evaluation ViewAttendees 96
Panel Discussion

Clinical Bioethics

Clinical Research

Diversity, Equity, and Inclusion

Global Neonatal & Children's Health

Health Equity/Social Determinants of Health

Health Services Research

Neonatology

Public Health
Session Type: Panel Discussion

Machine Learning and Artificial Intelligence in Global Neonatal and Child Health: Promise, progress and challenges

Saturday, May 4, 2024
8:00 AM – 9:30 AM ET
Location: Metro Toronto Convention Centre: 206 BD
CE: Live CME: 1.5

Selected By: CCGCH
Co-sponsored By: PIDS

    Chair(s)

  • Gary Darmstadt, MD, MS (he/him/his)

    Associate Dean for Maternal and Child Health, Professor
    Department of Pediatrics, Stanford University School of Medicine
    Palo Alto, California, United States

Disclosure(s):
Gary L. Darmstadt, MD, MS: I do not have any relevant financial / non-financial relationships with any proprietary interests.

Session

Description: Machine Learning (ML) and Artificial Intelligence (AI) have potential to improve maternal and child health outcomes. Through efficient analysis of vast amounts of data, ML and AI algorithms can enable accurate early prediction of adverse outcomes that can help guide prenatal, neonatal and child healthcare; provide insights into biological pathways and pathology involved in pregnancy complications, and lead to the development of personalized treatment options based on patient health profiles.

This session will provide an overview of current state-of-the-art ML and AI approaches for maternal and child healthcare, present several AI applications that focus on low- and middle-income countries (LMICs), and discuss ethical issues and challenges faced in LMICs. We will also explore research directions in ML/AI that could resolve these challenges, as well as steps/policies that could facilitate faster, equitable adoption of ML and AI in LMICs.

Learning Objectives:

  • Describe state-of-the-art ML and AI applications for maternal and child health care in global settings.
  • Outline current challenges for progress of ML and AI approaches for maternal and child health in LMICs.
  • List promising directions in terms of methodology and policy to facilitate equitable progress in adoption of ML and AI in LMICs.

Presentations:

  • 8:00 AM – 8:05 AM ET
    Transforming health systems and health outcomes through AI/machine learning: framing the challenge for LMICs

    Speaker: Gary L. Darmstadt, MD, MS (he/him/his) – Department of Pediatrics, Stanford University School of Medicine

  • 8:05 AM – 8:25 AM ET
    Introduction of Artificial Intelligence for Global Maternal and Child Health: Data, Challenges and a Path Forward

    Speaker: Ivana Maric, PhD – Stanford University School of Medicine

  • 8:25 AM – 8:45 AM ET
    Machine Learning for Integrative Analysis of the Immune System in Mothers and Newborns

    Speaker: Nima Aghaeepour, PhD – Stanford University School of Medicine

  • 8:45 AM – 9:10 AM ET
    Global Health Research: It's time to focus on the 'who' and the 'how' instead of the 'what'

    Speaker: Leo Anthony Celi, MD MS MPH (he/him/his) – Massachusetts Institute of Technology