BCH AI and Machine Learning Working Group


The Boston Children’s Hospital Artificial Intelligence and Machine Learning working group gives our clinicians and investigators a forum for sharing knowledge and collaborating across the many facets of artificial intelligence and machine learning.

Core objectives:

  • create a forum for Boston Children's Hospital investigators to find like-minded collaborators
  • foster an environment of knowledge exchange
  • collaborate on funding options to improve infrastructure
  • create a unified body for industry discussions

Focus areas:

  • clinical decision making
  • image processing and interpretation
  • hospital administrative functions and capacity planning
  • basic methods
  • life sciences and drug development
  • omics research and omics-informed medicine

Participating programs and sponsors include:

We host:

  • quarterly workgroup meetings
  • seminars
  • journal clubs

Please send an email to register your interest in joining.

Previous lectures


Date: July 17, 2020
Speaker: Yangming Ou, PhD; Assistant Professor of Radiology; Affiliate Faculty, Computational Health Informatics Program; Faculty, Fetal-Neonatal Neuroimaging Data Science Center
Event: BCH AI and Machine Learning Working Group Lecture
Talk Title: AI in 3D Medical Images: Concepts, Milestones, and Opportunities

Dr. Ou briefly reviewed some major concepts and milestones of AI in medical images. The focus of Dr. Ou’s talk was on 3D medical images, for AI's application in disease diagnosis, outcome prediction, early screening, neuroscience, and others. Dr. Ou then discussed some major challenges and potential opportunities, including further improving accuracy in detecting small diffuse lesions, and facilitating AI in small sample sizes.


Date: June 30, 2020
Speaker: Tim Miller, PhD; Assistant Professor of Pediatrics, Computational Health Informatics Program
Event: BCH AI and Machine Learning Journal Club

Dr. Miller discussed articles that he recently published on natural language processing of computerized text.


Date: May 8, 2020
Speaker: Arjun (Raj) Manrai, PhD; Faculty, Computational Health Informatics Program (CHIP); Director, Laboratory for Probabilistic Medical Reasoning; Assistant Professor, Harvard Medical School
Event: BCH AI and Machine Learning Working Group Journal Club
Talk Title: Machine Learning Reveals Widespread Demographic Structure in Laboratory Data

Blood laboratory measures such as glucose and hemoglobin are the basis for much of clinical decision making, yet baseline variation for many laboratory measures remains incompletely characterized across age, gender, and race groups. I will introduce foundational techniques from machine learning and statistical genetics and show how they can be applied to systematically unpack variation in blood laboratory data across population groups. These analyses reveal widespread demographic structure in blood laboratory data.