Press Release
FOR IMMEDIATE RELEASE
Cynthia Adinig Accepts Patient Advisor Role for TOPx HHS Tech Sprint Focused on AI and Invisible Illness
Federal health policy advisor and CYNAERA founder continues national work spanning Long COVID, research standards, artificial intelligence, patient-led research, and invisible illness across presidential administrations
WASHINGTON, D.C., September 2026 — Researcher, federal health policy advisor, and AI systems architect Cynthia Adinig has accepted an invitation to serve as a Patient Advisor for the TOPx HHS Tech Sprint focused on AI and Invisible Illness.
The appointment extends Adinig’s national work across patient advocacy, federal health policy, National Academies research standards, Long COVID definition development, patient-led research, and the emerging use of artificial intelligence to improve healthcare research and decision systems. Adinig previously served on the U.S. Department of Health and Human Services Secretary’s Advisory Committee on Long COVID, where she contributed patient-centered expertise to federal discussions surrounding Long COVID research, care, disability, and policy. Her selection for the TOPx HHS Tech Sprint represents a continuation of federal advisory work spanning the Biden and Trump administrations while expanding her focus beyond Long COVID into broader invisible illness, Lyme disease innovation, artificial intelligence, and cost of illness strategy.
I have spent the last several years moving wherever the gaps are,” Adinig said. “When Long COVID was not being counted properly, I worked on prevalence. When patients were being missed diagnostically, I built models around that. When research design was failing complex patients, I moved into evidence standards and clinical trial systems. Now AI and invisible illness are converging at the federal level, and I already have years of work sitting at that intersection.”
In 2026, she was invited by the National Academies of Sciences, Engineering, and Medicine to participate in Finding What Works in Health Care: Updating Standards for Systematic Reviews, contributing an end-user perspective to discussions involving systematic review methodology, artificial intelligence, patient-centered evidence generation, and how research is translated into healthcare decision making. She subsequently completed a technical review of the National Academies’ published proceedings to help ensure the accuracy and completeness of her contributions. Her work with the National Academies also includes contributing to the stakeholder process surrounding development of the national Long COVID definition, including reviewing stakeholder presentation materials, helping recruit patients for focus groups, and participating in stakeholder engagement activities. That progression has increasingly moved Adinig’s work upstream, from identifying failures experienced by patients to helping influence how disease is defined, how evidence is evaluated, how health policy is shaped, and how new computational systems may be designed.
Adinig is the founder of CYNAERA, an independent research institute and AI systems organization developing computational approaches to complex health, diagnostic, economic, and policy challenges. Through CYNAERA, she has developed modeling frameworks examining disease prevalence, diagnostic delay, healthcare utilization, clinical trial design, patient risk, economic impact, and infection-associated chronic conditions including Long COVID, ME/CFS, dysautonomia, Lyme disease, and overlapping chronic illness populations. Several of these systems have now moved beyond theoretical development into patient-centered research applications.
One recent example is the Eve Research Project, a patient-led research initiative examining the intersection of chronic illness, autoimmune symptoms, hormonal change, and complex multisystem health patterns. The project recently completed its first full cohort, using AI assisted analysis of participant generated health data to help identify patterns that may otherwise be difficult to recognize across traditional clinical silos. Participants were able to better understand relationships among symptoms, sleep, activity, hormonal changes, autonomic symptoms, environmental factors, treatment response, and recovery over time. The project also helped surface possible undiagnosed conditions and areas for further clinical evaluation, giving participants additional information they could bring into conversations with their healthcare providers. At the same time, the cohort provided an opportunity to validate and refine several CYNAERA modeling modules against longitudinal, real-world patient data.
The Eve Research Project was designed and conducted through a patient-led team, demonstrating a model of AI enabled research in which people living with complex illness are not simply subjects of research, but active participants in shaping the questions, interpreting patterns, and identifying where existing healthcare systems may be missing important signals. The project reflects the type of patient centered application Adinig has expertise in from University of South Carolina and recently University of Maryland programs. Creating AI systems that do not replace clinicians, but help patients, researchers, and healthcare teams identify patterns that can be missed when chronic multisystem illness is examined one symptom or one specialty at a time.
“Patients are often treated as the final recipients of healthcare innovation,” Adinig said. “Some of the most important innovation actually begins when patients help identify the system failures that everyone else has learned to work around.” Adinig’s work at the intersection of research, health policy, artificial intelligence, and patient advocacy grew in part from her own experience developing Long COVID in 2020 and navigating severe multisystem illness during the earliest period of the pandemic.
She later became involved in national Long COVID policy efforts, research collaborations, federal advisory work, and the development of AI driven systems designed to address gaps she observed across healthcare, research, and public policy. Her work has since expanded beyond Long COVID toward the larger infrastructure challenges affecting people living with complex, chronic, frequently overlapping, and often underdiagnosed illnesses. Her innovative platform, CRISPR Remission™ was presented at CRISPRMED26, making her the first in the world to develop and present for Long COVID ad ME/CFS at the conference.
The TOPx HHS Tech Sprint brings together technologists, researchers, patients, innovators, and other stakeholders to explore new approaches to health challenges. Adinig is serving as a Patient Advisor across areas that include invisible illness, Lyme innovation, and cost of illness.
For Adinig, the selection represents both continuity and expansion. “The committee I served on may have ended, but the problems we were working on did not,” Adinig said. “I kept building and expanded from Long COVID to include Lyme, autoimmune, oncology, and more. Being able to bring a comprehensive perspective into another federal initiative, particularly one involving AI and invisible illness, feels like the next phase of the same work.”
About Cynthia Adinig
Cynthia Adinig is a researcher, federal health policy advisor, author, healthcare advocate, and founder of CYNAERA. Her work focuses on artificial intelligence, infection-associated chronic conditions, health access, diagnostic systems, research methodology, evidence infrastructure, and healthcare policy. Her national work has included service on the HHS Secretary’s Advisory Committee on Long COVID, participation in National Academies initiatives involving Long COVID and systematic review standards, PCORI Merit Reviewer, and advisory work surrounding emerging federal health innovation initiatives. Adinig also has worked bipartisan and internationally across research, policy, advocacy, and technology to improve how complex health conditions and patient populations are represented in evidence and decision making.
About CYNAERA
CYNAERA is a modular intelligence infrastructure platform designed for enterprise integration across healthcare, government, climate resilience, and population risk systems. The platform combines predictive systems modeling, AI-driven analytics, and multi-sector data integration to support decision making in complex, dynamic environments. See our Case Studies.
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Email: cynthia@cynaera.com
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