Meredith C.B. Adams, MD, MS will present "Building the Future of Clinical Data: From Standardization to AI-Powered Federation" from 12:00 p.m.–1:00 p.m. EST.

About the Seminar

This presentation will examine how clinical researchers developed systematic data standardization, AI-powered automation, and federated architectures are transforming clinical research infrastructure. Drawing from leadership across multiple NIH HEAL Initiative networks, Dr. Adams will present a roadmap from evidence-based research tools (including the NIH HEAL MME Calculator) to real-world data standardization frameworks covering nearly half the US population through claims data integration, with resources to make them more accessible. The presentation will demonstrate how large language models are removing barriers for complex data operations, making sophisticated analyses accessible to non-technical researchers. This talk illustrates how architectural thinking, rather than incremental improvements, can fundamentally alter the economics and scalability of clinical data integration, offering a vision for global research collaboration that maintains both security and accessibility through standardized, automated clinical researcher friendly tools. The session will be of particular interest to data scientists, clinical researchers, and policy leaders working on data infrastructure challenges in biomedical research.

About the Speaker

Meredith C.B. Adams, MD, MS, FASA, FAMIA is Associate Professor of Anesthesiology, Artificial Intelligence, Translational Neuroscience, and Public Health Sciences at Wake Forest University School of Medicine and Chair of the NIH HEAL Data Ecosystem Collective Board. As Principal Investigator of multiple NIH HEAL Initiative programs (including MIRHIQL Resource Center, Wake Forest DISC, and IMPOWR IDEA-CC), she leads transformative data infrastructure development for chronic pain and opioid use disorder research. Dr. Adams serves as Secretary/Treasurer of the International Anesthesia Research Society and has received numerous honors including the 2024 NIH HEAL Golden Neuron Pain Rising Star Researcher Award and 2023 NIH HEAL Director's Trailblazer Award. Her innovative tools, including the NIH HEAL MME Calculator, CDE2OMOP mapping system, and Gen3-based federated data commons, have become essential infrastructure for clinical research networks. With expertise spanning clinical informatics, machine learning, and health policy, Dr. Adams bridges clinical domain knowledge with advanced computational methods to create research frameworks that enable access to sophisticated data analytics while maintaining rigorous scientific standards.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Allison Hurst at 301-670-4990. Requests should be made at least five days in advance of the event.

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight examples of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

Andrey Fedorov, Ph.D., will present "Imaging Data Commons and cancer image sharing in today's AI era" from 12:00 p.m.–1:00 p.m. EST.

About the Seminar

Rapid advances in Artificial Intelligence open exciting opportunities to do more with the existing and future imaging datasets. At the same time, they are intricately dependent on the availability and quality of the training data, while heavily relying on public data. In this talk I will discuss National Cancer Institute Imaging Data Commons (IDC), and the role it can play in both enabling breakthroughs in cancer imaging AI and leveraging the latest AI advances to empower its users. IDC is a cloud-based environment offering unrestricted access to a growing amount of harmonized cancer imaging data, primarily from various NCI data sharing initiatives. 

Beyond image archival, IDC is enriching its datasets through secondary analyses, complementing images with AI-derived annotations and quantitative features. I will highlight the specific capabilities of IDC and the datasets it is hosting that can help make cancer imaging AI research more accessible, transparent and explainable. I will also present examples of utilizing IDC for investigating the capabilities of existing imaging AI tools and applying them to IDC data. 

About the Speaker 

Andrey Fedorov, Ph.D., is a researcher at Brigham and Women's Hospital (BWH) and Associate Professor in Radiology at Harvard Medical School. Andrey is one of the leads of the team tasked with building National Cancer Institute Imaging Data Commons (IDC). A computer scientist by training, Andrey spent the past 15 years at the BWH Surgical Planning Lab working on translation and evaluation of image computing tools in clinical research applications. He is dedicated to developing infrastructure and best practices to help imaging researchers improve transparency of their studies, simplify data sharing and make their analyses more easily accessible and reproducible by others. 

About the Seminar Series 

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Allison Hurst at 301-670-4990. Requests should be made at least five days in advance of the event. The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight examples of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.