Overall Winner

The  Integrated Medical Management and Educational Gateway (IMMEG) Venous Management System (I-VMS) is an AI-enabled, standards-based platform that projects a vectorized atlas of the deep thoracic venous system onto routine chest radiographs. Using deep-learning landmark detection and HL7 FHIR®/DICOM interoperability, the system lets clinicians visualize the catheter trajectory and tip position in patient-specific anatomy, record planned versus actual placement, and build a reusable, longitudinal venous access record across organizations. The project was developed for Vanguard with support from Xzyos.ai. 

Clinical Problem and Context 

Central venous access is essential for chemotherapy, parenteral nutrition, dialysis and critical care, yet malposition and related complications—venous injury, thrombosis, infection, and device dysfunction—remain common and costly. Post-procedure assessment usually relies on plain chest X-rays, which do not directly visualize venous structures. As a result, clinicians infer anatomy indirectly; documentation is inconsistent; and comparing procedures over time is difficult. There is no consolidated, spatially normalized record of a patient’s venous history to guide future decisions.

Core Innovation

 I-VMS predicts anatomical landmarks (e.g., carina, first thoracic vertebra T1, lateral edge of the right rib) on a radiograph with a modified DenseNet121 model implemented in MONAI. These coordinates establish a patient-specific basis for an affine transformation that overlays a standardized, vector-based venous atlas onto the image. Clinicians can accept or adjust landmarks and annotate intended and actual entry and tip positions. Because annotations are stored in a normalized coordinate space, results are comparable across encounters and over time, enabling longitudinal analysis and population-level learning.

Reflections from the ADAPT Chief AI Officers on Innovation Panel Discussion, November 2025

After decades of working toward seamless health data interoperability, we find ourselves at another pivotal moment. The rapid adoption of Artificial Intelligence (AI) in healthcare presents us with a familiar challenge wearing a new face: how do we ensure these powerful new tools work together transparently, accountably, and in the service of better health for people everywhere?

At a recent ADAPT conference panel, I had the opportunity to reflect on what our interoperability journey can teach us as we venture into standardizing intelligence, not just data. Here are some key insights from that conversation.

The Journey Continues

First, a grounding perspective: this is a journey, not a destination. Despite all the progress we've made in healthcare interoperability, too often, people still move faster and further than their health information. The ability for any digital tool—including AI—to help people make better health decisions is always limited by the scope of data in its purview and its capability to make sense of it.

Even the most powerful AI we can imagine must overcome the same boundaries we've always faced: technical, organizational, business, and jurisdictional barriers that prevent us from seeing the complete picture of health information relevant for individuals or populations.

However, HL7's decade-plus journey with Fast Health Interoperability Resources (FHIR® ) has taught us something crucial: open standards are a potent fuel for innovation. The vibrant, open, collaborative community around FHIR wasn't just a nice byproduct—it was the key force that created a well-tuned specification and enabled it to flourish in the marketplace.

Open standards level the playing field, reduce barriers to participation, and free organizations from proprietary formats. They unlock new connectivity, preserve data sovereignty, and most fundamentally, enable new digital freedoms. As we approach AI standardization, maintaining this commitment to openness isn't guaranteed, but it's the future we're fighting for.

 Winner of the Transformative Impact in Healthcare Award

What do you do when the people who taught you everything start to forget? My father is fighting Parkinson's and early dementia. My mother manages diabetes and congestive heart failure. My three siblings and I are a team — a family armed with love but disarmed by the chaos of a dozen medications, forgotten instructions, and missed questions. We were losing the battle against complexity, and that feeling is where this story truly begins.

 The seed for what would become Let's Talk Doc was planted six or seven years ago. My friend and partner, Diane Nguyen, and I saw the cracks in the system through our own eyes. I saw it in my parents' home, and she saw it as an immigrant facing the silent fear that a single misunderstood word on a form could alter her family's care. We tried to build something back then, a small solution born from our shared frustrations. But the technology wasn't ready. The idea was a spark, but we couldn't yet build the engine.  

Years passed. Then, earlier this year, Diane reached out. The world had changed. Technology had finally caught up to our ambition. "It's time," she said. "Let's try again."

 This is not a business venture for us. It’s a mission.

Winners Recognized for Standards-Based AI Innovation, Collaboration and Real-World Impact

 Today, HL7 announced the winners of its first-annual HL7 AI Challenge, spotlighting innovators developing AI applications powered by open, standards-based frameworks in healthcare. The announcement was made during the morning session of HL7’s 39th Annual Plenary and Working Group Meeting (WGM) in Pittsburgh.

 Judges selected nine honorees from across multiple categories, recognizing innovation, collaboration and real-world impact.

 AI Challenge Overall Awardee Winners

• Robert Lario, PhD (Xzyos.ai) and Kevin Baskin, MD (Vanguard): AI-Conformable Venous Atlas: A Novel Solution for Clinical-Structural Correlation and Medical Device Surveillance
• Verto Health: VERTO Connect: A Solution to Healthcare's Unstructured Data Problem
• Quantek Systems, Inc: DynaMap AI – Active Inference-driven Clinical Workflow Engine

And six honorees were recognized for their contributions in the following specialized categories.

AI Challenge Category Winners

• Pioneer in Healthcare Innovation: (Ignyte Group and Appian) – Bring AI to Work(flow) – Provides robust process improvement throughout the patient lifecycle via AI agentic assistance
• Excellence in AI Transparency & Trust: (Trisotech) – Standardizing Clinical Autonomy: BPM+ Determinism and HL7 Integration for AI Agents
• Interoperability Leadership Award: (Whitefox Cloud Consulting) – Whitefox FHIR Converter
• Open Solution Award: (Omni Health Nexus) – The Intelligent Medical Assistant – Revolutionizing Information Management for Better Care
• Clinical Data Quality & Outcomes Award: (Aidbox Forms from Health Samurai)– AI Assistant for FHIR SDC and Analytics
• Transformative Impact in Healthcare Award: (Let’s Talk Doc) – AI Avatar Patient Communication Platform

Innovators worldwide invited to demonstrate how HL7 standards and
AI can revolutionize healthcare

HL7 is proud to announce the launch HL7 AI Challenge 2025, a global innovation competition designed to spotlight the transformative potential of artificial intelligence (AI) when powered by open health data standards. Selected winners will be recognized on stage during HL7’s 39th Annual Plenary, Working Group Meeting, September 13-19, 2025, in Pittsburgh.

The HL7 AI Challenge is open to any individual, team or organization across academia, industry, and government using HL7 standards to power AI applications that solve real-world clinical, operational or equity-focused problems. HL7 membership is not required, and there is no cost to participate.

 The competition builds on HL7’s long-standing commitment to enabling interoperable, standards-based healthcare innovation and reflects growing global interest in aligning AI adoption with ethical, explainable and secure data practices.

“As AI becomes more deeply embedded in healthcare, we must ensure it advances—not undermines—trust, transparency, and patient outcomes,” said Dr. Charles Jaffe, CEO of HL7 International. “The HL7 AI Challenge invites technologists, researchers and clinicians worldwide to showcase how open standards can anchor responsible, scalable AI innovation, bridging the health IT and AI communities for the benefit of all.”