AI Usage at a Glance
Sep 28, 2021
Data AnalysisPractice documented: Boston Children's Hospital deployed predictive AI models that forecast emergency department admissions and hospital bed availability, helping clinical staff plan capacity for elective procedures and respond to surges in patient demand. During the 'tripledemic' of RSV, COVID-19, and flu, the models accurately predicted bed availability.
Practice DocumentedView practice →Dec 14, 2022
Data AnalysisPractice documented: Boston Children's Hospital deployed VirtualHip, a fully automated AI tool integrated across its Adolescent and Young Adult Hip Preservation Program clinic, which converts routine 2D medical images into detailed 3D hip models and provides clinicians with diagnostic and treatment guidance within about one hour. It is described as the first fully automated AI tool of its kind deployed across a US hip clinic.
Practice DocumentedView practice →May 10, 2023
Data AnalysisPractice documented: Boston Children's Hospital, in collaboration with MITRE, deployed machine learning algorithms in its pediatric cardiac catheterization lab that predict the risk of patient harm before procedures, allowing clinical staff to assess risk levels and make pre-procedure decisions. The framework, called GRACE (Generating Risk Reduction Analytics for Complex Cardiac Care Environments), was used the day before catheter procedures.
Practice DocumentedView practice →Jan 1, 2024
Data AnalysisPractice documented: Boston Children's Hospital uses HealthMap, an AI-powered disease surveillance system it founded in 2006, which scrapes the internet hourly across 17 languages to detect emerging infectious disease outbreaks in real time. In April 2025, the hospital co-launched BEACON, a new open-source platform that feeds HealthMap signals into a large language model to prioritize and contextualize global biothreat alerts.
Practice DocumentedView practice →Jan 30, 2024
Data AnalysisNew evidence: Boston Children's Hospital Deploys Automated AI Tool for Hip Diagnosis
Evidence AddedView practice →May 21, 2024
Data AnalysisPractice documented: Boston Children's Hospital tested an AI system in its radiology department that automatically examines X-rays and MRI scans, identifies the most diagnostically valuable images, and flags discrepancies for radiologist review. The pilot, using Red Hat OpenShift via the ChRIS Research Integration Service platform, began in May 2024 and focuses on fetal and pediatric imaging.
Practice DocumentedView practice →Nov 5, 2024
Data AnalysisNew evidence: How AI is Transforming Care and Operational Efficiency at Boston Children's Hospital
Evidence AddedView practice →Nov 5, 2024
Data AnalysisNew evidence: Boston Children's Researchers, in Joint Effort, Deploy AI Across Their Hip Clinic to Support Patients, Doctors
Evidence AddedView practice →Dec 17, 2024
Data AnalysisNew evidence: Generating Risk Reduction Analytics in Complex Cardiac Care Environments (GR2AC3E): Risk Prediction in Congenital Catheterization
Evidence AddedView practice →Apr 23, 2025
Data AnalysisNew evidence: BU Launches an Open-Source Infectious Diseases Monitoring Tool Powered by AI and Human Experts
Evidence AddedView practice →Apr 24, 2025
Data AnalysisNew evidence: BEACON Launches Today, Delivering AI-powered Global Disease Surveillance
Evidence AddedView practice →Nov 1, 2025
Data AnalysisNew evidence: Open source and AI are transforming healthcare at Boston Children's Hospital
Evidence AddedView practice →Jan 8, 2026
OtherPractice documented: Boston Children's Hospital integrated OpenAI's ChatGPT for Healthcare, an enterprise-grade, HIPAA-compliant AI workspace, rolling it out to clinical, research, and administrative staff as one of the platform's earliest adopters at launch in January 2026. The platform is powered by GPT-5 models evaluated through physician-led testing.
Practice DocumentedView practice →Jan 9, 2026
OtherNew evidence: OpenAI launches ChatGPT for Healthcare at several large health systems
Evidence AddedView practice →Jan 9, 2026
OtherNew evidence: OpenAI rolls out ChatGPT for Healthcare, a gen AI workspace for hospitals and clinics
Evidence AddedView practice →May 1, 2026
ProductivityPractice documented: Boston Children's Hospital deployed an AI system that analyzes clinical notes and estimates how sick each patient is to improve how operating room time is allocated. This allows surgical schedules to be planned further in advance, increasing the number of patients who can receive timely care.
Practice DocumentedView practice →May 1, 2026
ProductivityPractice documented: Boston Children's Hospital deployed a secure, internal ChatGPT-based enterprise AI environment used daily by more than one-third of its employees across clinical, research, and administrative teams. The platform supports tasks such as drafting documents, synthesizing medical literature, coding, and improving operational workflows.
Practice DocumentedView practice →May 30, 2026
ProductivityNew evidence: Boston Children's uses AI to unlock new diagnoses
Evidence AddedView practice →May 30, 2026
ProductivityNew evidence: Boston Children's uses AI to unlock new diagnoses
Evidence AddedView practice →May 30, 2026
ProductivityPractice documented: Boston Children's Hospital deployed an AI system to manage supply chain invoice intake, routing, and responses, replacing a high-volume manual processing task handled by administrative staff. This is one of more than 50 workflow automations the hospital has launched using OpenAI-powered tools.
Practice DocumentedView practice →May 30, 2026
Data AnalysisPractice documented: Boston Children's Hospital deployed an AI system it calls a 'co-pilot geneticist' that analyzes genetic data, patient symptom descriptions, and global medical literature to surface candidate diagnoses for children with rare, previously undiagnosed diseases. As of mid-2026, the system has contributed to more than 40 confirmed diagnoses that specialists had not been able to make through conventional analysis.
Practice DocumentedView practice →Jun 1, 2026
Data AnalysisNew evidence: AI helps Boston Children's Hospital diagnose rare diseases in kids
Evidence AddedView practice →Jun 1, 2026
Data AnalysisNew evidence: Using AI to help physicians diagnose rare genetic diseases affecting children
Evidence AddedView practice →Boston Children's Hospital integrated OpenAI's ChatGPT for Healthcare, an enterprise-grade, HIPAA-compliant AI workspace, rolling it out to clinical, research, and administrative staff as one of the platform's earliest adopters at launch in January 2026. The platform is powered by GPT-5 models evaluated through physician-led testing.
ChatGPT for Healthcare is an enterprise workspace for researchers, clinicians, and administrators, powered by GPT-5–based models optimized for clinical, research, and operational workflows. It integrates with enterprise systems such as Microsoft SharePoint to incorporate institutional policies and care pathways. It includes reusable templates for tasks such as discharge summaries, patient instructions, clinical letters, and prior authorization documentation. Patient data shared within the platform is not used to train AI models, and OpenAI offers a Business Associate Agreement for HIPAA compliance. Boston Children's Hospital is listed by OpenAI as one of the initial institutions rolling out the platform.
Boston Children's Hospital deployed VirtualHip, a fully automated AI tool integrated across its Adolescent and Young Adult Hip Preservation Program clinic, which converts routine 2D medical images into detailed 3D hip models and provides clinicians with diagnostic and treatment guidance within about one hour. It is described as the first fully automated AI tool of its kind deployed across a US hip clinic.
VirtualHip uses natural language processing models and computer vision algorithms trained on tens of millions of clinical notes and imaging data from patients seen at Boston Children's over two decades. Clinicians access a web-based portal to submit analysis requests and receive 3D models with a margin of error under one millimeter. The tool assesses morphological abnormalities and movement-related issues such as femoroacetabular impingement and hip dysplasia. It was built using the NVIDIA DGX platform. As of November 2024, researchers were working toward a patient-facing version using large language models and planned to commercialize the tool for use at other hospitals.
Boston Children's Hospital deployed an AI system that analyzes clinical notes and estimates how sick each patient is to improve how operating room time is allocated. This allows surgical schedules to be planned further in advance, increasing the number of patients who can receive timely care.
The system takes clinical notes as input and estimates patient acuity (severity of illness) to optimize allocation of operating room time. According to OpenAI's published case study, this allows schedules to be planned further in advance, increasing utilization rates. The practice is confirmed as active as of May 2026. The prior human scheduling process at this specific hospital is not described in detail in available sources beyond general descriptions of manual coordination.
Boston Children's Hospital deployed VirtualHip, a fully automated AI tool integrated across its Adolescent and Young Adult Hip Preservation Program clinic, which converts routine 2D medical images into detailed 3D hip models and provides clinicians with diagnostic and treatment guidance within about one hour. It is described as the first fully automated AI tool of its kind deployed across a US hip clinic.
Boston Children's Hospital uses HealthMap, an AI-powered disease surveillance system it founded in 2006, which scrapes the internet hourly across 17 languages to detect emerging infectious disease outbreaks in real time. In April 2025, the hospital co-launched BEACON, a new open-source platform that feeds HealthMap signals into a large language model to prioritize and contextualize global biothreat alerts.
Boston Children's Hospital, in collaboration with MITRE, deployed machine learning algorithms in its pediatric cardiac catheterization lab that predict the risk of patient harm before procedures, allowing clinical staff to assess risk levels and make pre-procedure decisions. The framework, called GRACE (Generating Risk Reduction Analytics for Complex Cardiac Care Environments), was used the day before catheter procedures.
Boston Children's Hospital tested an AI system in its radiology department that automatically examines X-rays and MRI scans, identifies the most diagnostically valuable images, and flags discrepancies for radiologist review. The pilot, using Red Hat OpenShift via the ChRIS Research Integration Service platform, began in May 2024 and focuses on fetal and pediatric imaging.
Boston Children's Hospital deployed an AI system it calls a 'co-pilot geneticist' that analyzes genetic data, patient symptom descriptions, and global medical literature to surface candidate diagnoses for children with rare, previously undiagnosed diseases. As of mid-2026, the system has contributed to more than 40 confirmed diagnoses that specialists had not been able to make through conventional analysis.
Boston Children's Hospital deployed predictive AI models that forecast emergency department admissions and hospital bed availability, helping clinical staff plan capacity for elective procedures and respond to surges in patient demand. During the 'tripledemic' of RSV, COVID-19, and flu, the models accurately predicted bed availability.
Boston Children's Hospital deployed an AI system that analyzes clinical notes and estimates how sick each patient is to improve how operating room time is allocated. This allows surgical schedules to be planned further in advance, increasing the number of patients who can receive timely care.
Boston Children's Hospital deployed a secure, internal ChatGPT-based enterprise AI environment used daily by more than one-third of its employees across clinical, research, and administrative teams. The platform supports tasks such as drafting documents, synthesizing medical literature, coding, and improving operational workflows.
Boston Children's Hospital deployed an AI system to manage supply chain invoice intake, routing, and responses, replacing a high-volume manual processing task handled by administrative staff. This is one of more than 50 workflow automations the hospital has launched using OpenAI-powered tools.
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