OVERVIEW

American Board of Artificial Intelligence in Medicine (ABAIM) AI in Pediatrics and Neonatology is organized by American Board of Artificial Intelligence in Medicine (ABAIM) and will be held on Oct 03, 2026.

Description:
Artificial Intelligence in Pediatrics and Neonatology is a cutting-edge, one-day virtual seminar designed for pediatricians, neonatologists, advanced practice providers, and child health professionals who want to lead the AI transformation in clinical care. This highly interactive course explores the most relevant and practical applications of AI across the pediatric and neonatal spectrum — from early diagnostics and developmental screening to imaging, predictive analytics, and precision treatment planning.

Whether you’re just beginning to explore AI or looking to deepen your expertise, this course will equip you with actionable insights, frameworks, and tools to responsibly and effectively integrate AI into your pediatric and neonatal practice.

This fast-paced, case-based course will answer all of the following questions and more:

How is AI being used to improve early diagnosis, developmental surveillance, and risk stratification in children and newborns?
What are the strengths and limitations of model evaluation metrics like sensitivity, specificity, and predictive value in pediatric and neonatal populations?
How can AI tools support clinical decision-making in neonatology, pediatric emergency care, and chronic disease management?
What population-specific challenges arise in AI development, including small datasets, age-dependent norms, and issues of consent and data privacy?
What ethical, legal, and equity considerations should be accounted for when using AI with infants, children, and families?

Learning Objectives include:

Understanding core AI concepts such as machine learning (ML), deep learning (DL), and large language models (LLMs) in the context of pediatric and neonatal care
Identifying current and emerging AI applications in neonatology, developmental pediatrics, emergency medicine, radiology, and chronic disease management
Evaluating model performance and addressing child- and infant-specific concerns such as age-related bias, data scarcity, and generalizability
Navigating ethical, legal, and regulatory frameworks around pediatric/neonatal data, privacy, consent, and explainability
Exploring how AI-enabled tools like chatbots, autonomous agents, and generative models can augment clinical workflows in pediatrics and NICUs
Analyzing real-world AI research and vendor tools from a pediatric and neonatal lens
Collaborating effectively with data science teams to improve care delivery and outcomes for infants and children
Preparing for the future of AI in child health — including precision pediatrics, neonatal predictive models, digital phenotyping, and AI-augmented developmental care