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 Events

Harsha Nori PhD

Speaker: Harsha Nori PhD, Director of Research Engineering for Aether, (internal group on AI, Engineering and Ethics) at Microsoft.

Date: March 12, 2024 at 2:15pm - 3:00pm


CHIP Monthly AI Journal Club

Dr. Nori will discuss his work at Microsoft and two journal articles:

Capabilities of GPT-4 on Medical Challenge Problems ( [2303.13375]

Leveraging ML for Fetal Acquisition and Analysis

Speaker: Ellen Grant, MD, Director, Fetal-Neonatal Neuroimaging and Developmental Science Center; Borjan Gagoski, PhD, Faculty MR Physician, Fetal-Neonatal Neuroimaging and Developmental Science Center; Junshen Xu, PhD, Student, at MIT

Date: February 11, 2022 at 09:30AM - 10:30AM

Fetal magnetic resonance imaging is challenging to perform as the fetus continually moves during image acquisition. As a result, the technologist must know how to "chase the fetus" to get images orthogonal to the fetal brain and repeat these acquisitions until images without motion are obtained. In this talk we will discuss how ML approaches are being used to accelerate and automate fetal imaging acquisition. Fetal imaging can be a challenge, but fetal motion also provides insight into the neurological and musculoskeletal development of the fetus.

BCH AI and Machine Learning Working Group Journal Club

Speaker: Ben Reis, PhD and Ilkin Bayramli, at Boston Children's Hospital

Date: February 1, 2022 at 2:00PM - 3:00PM

Dr. Ben Reis and Ilkin Bayramli will present their paper in Journal of the American Medical Informatics Association (JAMIA) entitled "Temporally informed random forests for suicide risk prediction":

Fireside Chat At Decision Points in Clinical Pathways

Speaker: Drs. Arjun Manrai, Amy Starmer, and Robert Rosen, at Boston Children's Hospital

Date: January 21, 2022 at 09:30AM - 10:30AM

A discussion among Drs. Arjun Manrai, Amy Starmer, and Robert Rosen, moderated by Dr. Ken Mandl.

Contemporary Symbolic Regression Methods for Interpretable Machine Learning

Speaker: William La Cava, PhD, Faculty, Computational Health Informatics Program at Boston Children's Hospital

Date: September 17, 2021 at 09:30AM - 10:30AM

Most interpretable machine learning research focuses on explaining the outputs of black-box models. A different, and promising, approach is to use machine learning to find the simplest possible model that meets certain performance criteria; this is the pursuit of symbolic regression. In this talk I will discuss the concepts of interpretability and explainability, and how they are used in the machine learning world. I will then discuss a pre-print that will be published in the Neurips Datasets and Benchmarks track later this year.

BCH AI and Machine Learning Journal Club: Andrew Beam, PhD

Speaker: Andrew Beam, PhD, Assistant Professor, Department of Epidemiology at the Harvard T.H. Chan School of Public Health

Date: April 13, 2021 at 2:00PM - 3:00PM

Dr. Beam led a discussion on the following article: Tom B Brown, Benjamin Mann, Nick Ryder, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 [cs], 2020. Dr. Beam also discussed results from his group that evaluates this model on medical applications.