The Trustworthy Technology & Innovation Consortium (TTIC)'s story at AIMed began three years ago, not with a track or a speaking role, but in the audience.
From a seat in the audience to a standing-room-only panel to an AI Champion Award. And this year, from an operating system to proof it holds.
Dr. Arnold Lee encouraged Sherri Douville, CEO of Medigram and Founder & Chair, TTIC, and Dr. Art Douville, Chief Medical Officer of Medigram, to attend AIMed. They did, and were immediately struck by the quality of the content, the seriousness of the clinical AI conversation, and the exceptional community AIMed had assembled. They came to learn. They left wanting to contribute.
The money in medicine has moved
We built this track around the thing nobody says out loud: the money in medicine has moved.
Much of the grant funding is not what it was, and much of it is not returning on the old terms. The checks now come from family offices and from capital funded by private university-adjacent networks, trustee networks, and family capital. Those people evaluate risk in a language most of us never had to learn.
So we are bringing them into the room. Pro sports executives. Investors who hold ownership stakes across different sports franchise types. They have been pricing catastrophic downside for decades. In healthcare, we are only starting.
The 2026 pro sports theme was shaped with Brian Yam, who chairs the Pro Sports Workstream at TTIC.
Closing the gap people are actually feeling
The tone across enterprise and healthcare remains tempered, and in some functions the feeling toward AI has turned negative. The reason is straightforward: most C-suites and boards in the enterprise, including in healthcare, encounter the gaps before they encounter anything that closes them.
The collaborative work represented in this room supplies key missing pieces. Behavioral verification against declared remits. Normalized runtime telemetry. Standards translated into working code. The reframing of security leadership toward active verification and outcome ownership.
Together these produce something that has been scarce: evidence that intended behavior can be checked, observed, and defended. That converts diffuse anxiety into a clearer path, which is why the people who encounter it come away with morale raised rather than lowered.
AcknowledgmentsMitch Parker, Co-Founder of TTIC and Chief Information Security Officer at Indiana University Health, designed the Indiana Executive Council on Cybersecurity AI Security System Architecture Layers. Steve Wilson, who chairs AI Security at TTIC, created Praxen and Observra. Praxen independently verifies that an AI system behaves the way its governance documentation claims it does, while Observra provides normalized runtime telemetry for AI and agentic systems. Both were created by Steve Wilson and released as open-source, sponsored by Exabeam.
We are also grateful to Anthony Lee, Chair of Communications for TTIC and CEO of Heroic Voice Academy, who has been behind every conference and speaking event; to Kris Mednansky at Taylor & Francis, who provided a platform for the work, first through our books, then through our series; and to the leadership team of IEEE/UL 2933, the engine of the technical basis of the work.
On the open-source tools and teamwork this work runs on →
The following year, in 2024, TTIC was thrilled to have the opportunity to bring a multidisciplinary panel to AIMed. It was the first panel of its kind there, bringing security leaders Anahi Santiago and Steven Ramirez, physician leaders Dr. Josh Tamayo-Sarver and Dr. Shaun Garcia, and an executive mix together around the same conversation.
The room was standing room only.
That response mattered because it demonstrated something central to TTIC's founding thesis: healthcare leaders wanted these disciplines in the same room. Clinical AI could not be treated solely as a clinical problem, an AI problem, a cybersecurity problem, or an executive problem. The community was ready for an integrated conversation.
The success of that panel led to an opportunity the following year to lead AIMed's High Reliability AI Module, together with the leadership of Maneesh Goyal, COO, Mayo Clinic Platform, Mitch Parker, and Dr. Apurv Gupta, Dr. Art Douville, module co-designers, together with a roster of incredible academic and industry leaders across medicine, AI, research, ethics, security, governance, and engineering. What had started with attending and learning from an extraordinary community had progressed to contributing one multidisciplinary conversation, and then to taking responsibility for assembling a much larger system of conversations.
At AIMed 2025, the High Reliability AI Module brought clinical, operational, cybersecurity, engineering, legal, governance, business, and executive perspectives together around a larger question: What would it actually take to operate AI reliably in healthcare?
The answer was not another collection of disconnected AI presentations, principles, prototypes, pilots, demos, or frameworks. The module was designed as an operating system, connecting the disciplines required to move trustworthy AI from standards and principles toward real-world implementation. The High Reliability AI Module drew a full room, reinforcing what the standing-room-only panel the year before had suggested: the clinical AI community was looking for a multidisciplinary approach that connected clinical leadership, cybersecurity, engineering, governance, operations, standards, and executive accountability. The work received AIMed's 2025 AI Champion Award.
But building the operating system raised the next question: how do we know it actually works?
By 2026, healthcare was moving beyond experimentation toward AI and agentic systems capable of recommending, coordinating, deciding, and acting. Governance could no longer stop at policies, frameworks, controls, or statements of intent. The requirements had to become demonstrable.
The 2026 track therefore advances proof at two complementary levels. First, governance evidence: can an organization demonstrate what requirements applied to a consequential AI-assisted decision, who had authority, what was evaluated, what decision was made, what conditions or remediation were required, what happened after deployment, and who remained accountable? Second, behavioral verification: can the organization demonstrate that the AI or agent actually behaves according to its intended requirements, including under adversarial conditions, with failures identified, remediated, retested, and monitored as systems change?
One demonstrates the governance of the decision. The other demonstrates the behavior of the technology. Together, they begin closing the gap between what an organization says its AI should do and what it can actually demonstrate.
The progression follows TTIC's founding purpose: standards → integration → implementation → governance → verification → evidence → trust.