Opening General Session + College of SIIM Fellows Induction
Share For Healthcare: Privacy-Preserving, Open Access to Data and Models Is Critical in the Age of AI
John Dickerson, PhD
Chief Executive Officer
Mozilla.ai
Session Number: 2001
Medical imaging is generating more data than ever before, but much of its value remains locked within individual hospitals - or departments within, or single-PI research labs. The best-performing AI tools rely on large, diverse datasets, yet sharing patient data across institutions is frequently not possible due to privacy, regulatory, and operational constraints. More than that, these AI tools rely on traces of human interactions with the systems themselves to improve over time - also obviously difficult to share in the imaging informatics space!
This talk focuses on a simple but important idea: we can collaborate on better AI models without sharing raw patient or raw provider usage data.
I will introduce a set of emerging approaches that allow institutions to “learn together” while keeping data local and secure. These include techniques where models, or model metadata - but critically not raw data - are shared, as well as methods that protect sensitive information while still allowing insights to be combined across sites.
The central message is that privacy and collaboration do not have to be in conflict. By adopting new approaches to “learning together” without raw data sharing, imaging informatics can move faster, produce more reliable tools, and ultimately improve patient care.
Following the Opening General Session Keynote lecture, the Society will induct the 2026 SIIM Fellows. Founded in 2000, the College of SIIM Fellows The College of SIIM Fellows (COSF) has inducted individual(s) who have significantly contributed to SIIM and its mission. Fellows of SIIM are nominated and elected by the College in recognition of their achievements and accomplishments in medical imaging, and for their demonstrated personal commitment to improving patient care. All Fellows are active members of SIIM and are the leading thinkers and innovators within the imaging informatics community. The FSIIM distinction is awarded at the SIIM Annual Meeting.
Objectives
- Explain why large, diverse datasets matter for imaging AI, juxtaposed against the difficulty of sharing them (for legal / policy reasons and otherwise). Recognize the practical barriers (privacy, regulation, institutional policy) that limit traditional data sharing but couched in the post-2022 AI era.
- Describe practical ways institutions can collaborate without sharing raw patient data. Understand, at a high level, approaches like “bringing the model to the data” and other privacy-preserving methods in the federated learning space, and where they fit into real-world workflows.
- Identify opportunities to participate in or adopt collaborative, privacy-preserving AI efforts. Recognize concrete steps their organization or role can take to contribute to shared models and benefit from broader data-driven insights.
Privacy-preserving data sharing can unlock the power of open models and accelerate imaging informatics."
Biography
John Dickerson is CEO of Mozilla.ai. He brings a wealth of experience in scaling startups, developing practical and robust machine learning methods, deploying AI-based products into the enterprise, as well as providing broad AI/ML thought leadership in industry, academia, non-profits, and governments.
Previously, John was co-founder and Chief Scientist at Arthur as well as a tenured professor at the University of Maryland in the Washington, DC area.
- At Arthur, he helped scale the company to 50+ employees, a presence in NYC, DC, and the US west coast, and $55 million raised from seed through Series B financing. Arthur develops industry-leading technology in data drift detection and mitigation, bias detection and mitigation, GenAI firewall features such as jailbreak and PII leakage detection, and explainability. Arthur’s ML-based products are deployed at some of the largest regulated enterprises in the US and worldwide.
- At Maryland, he founded and led a large lab researching the intersection of ML and economics, with a core focus of designing incentives that promote “good” participation in complex systems. That lab produced 16 PhD graduates and secured $10M+ in funding from NIST, NSA, DARPA, ARPA-E, NIH, NSF – including an NSF CAREER award – in addition to industry funding.
He has worked extensively on theoretical and empirical approaches to organ exchange where his work has set US-wide policy; worldwide blood donation markets with Meta; game-theoretic approaches to counter-terrorism and negotiation, where his models have been deployed; and market design problems in industry (e.g., online advertising) through various startups.
John holds a BS in mathematics and a BS in computer science from the University of Maryland, as well as a PhD in computer science from Carnegie Mellon University. He splits his time between Seattle, Washington, USA and Western Europe.
Learning Topics
Artificial Intelligence
Specialties
Cardiology
Dermatology
Ophthalmology
Radiology
Pathology
Credit Types
ACCME-MD
ARRT-RT
CAMPEP-MPCEC
SIIM IIP-CIIP
