Summary

HL7 International embraces artificial intelligence as a powerful enabling technology for health data interoperability. Rather than restricting AI use of our standards, we are actively engineering our processes and content to be AI-ready. We believe that AI systems capable of understanding FHIR® and other HL7 standards ultimately serve our mission to improve health and well-being through better data exchange.

Our Perspective: Open Standards = Open Possibilities

HL7 International's mission is to create and promote the adoption of innovative interoperability standards that improve health and well-being while fostering a diverse and inclusive global community. We pursue this so that people everywhere can live in optimal health. Because open standards enable new digital freedoms, we want our standards to be as widely used and as deeply understood as possible. By humans and machines alike. Standards demonstrate the network effect — they become more valuable the more widely they are adopted.

Some standards development organizations (SDOs) restrict or prohibit the use of their intellectual property in AI training and inference. HL7 International takes a different approach. We view AI systems as legitimate and valuable consumers of our standards — partners in the global project of interoperability rather than threats to our business model.

Our licensing choices reflect this view. HL7 publishes our FHIR platform standard and related specifications under the Creative Commons Zero (CC0) public domain dedication, the most permissive licensing approach available. CC0 reflects a deliberate strategic commitment: by removing all licensing barriers, we maximize adoption potential and minimize friction for all implementers, including AI developers and the AI systems they build.

FHIR in the Wild: Standards-Aware AI Systems

HL7's flagship FHIR standard is unique not only as a modern API standard for healthcare, with innovations in open standards development, but also in its structure and publication format. FHIR reflects a fundamentally developer-native approach, setting it apart from most other health IT standards. Rather than static, paywalled PDFs, FHIR is published as a fully navigable website where every resource, data type, and operation has its own structured page — paired with computable, machine-readable definitions in JSON and XML that allow tools to programmatically interrogate the standard itself. Further, the platform specification and derivative implementation guides, along with their computable parts, are distributed as versioned NPM packages via a public registry, enabling reproducible builds and automated dependency resolution familiar to any software developer.

Additionally, we have embraced the expectation of co-developing and testing an open-source (typically licensed under Apache 2.0) reference implementation software alongside the standard. This ensures that the published standard is actually implementable, and gives implementers working software to learn from, test their code against, or even incorporate into their products.

Because FHIR is freely available, extensively documented online, and has corresponding open source reference software code bases on public platforms like Github, FHIR is already embedded in the training corpora of the world's leading large language models. Developers today can ask an AI assistant to generate a FHIR patient resource, write a FHIR search query, or explain the semantics of a SMART on FHIR authorization flow — and receive accurate, useful answers. This is a natural consequence of our open-first standards strategy at work.

In short, we see the standard not as a document to be read, but as an open platform to be built upon.

If you’ve spent any time in healthcare over the past year, you’ve probably felt it: the energy, the urgency, the curiosity around AI. Everywhere you look, teams are experimenting with new models, exploring new use cases and imagining what care could look like if we finally had the right data in the right place at the right time.

But there’s also a shared realization emerging across the industry that AI can’t transform healthcare unless the data behind it is trustworthy, connected and interoperable.

That’s why HL7 International launched the 2026 HL7 AI Challenge, now officially open for submissions through June 30, 2026.

Why an HL7 AI Challenge?

Last year’s inaugural Challenge showed us something powerful: when innovators build on HL7 standards, they can move faster, scale more easily and create solutions that actually work in the messy, real‑world environments where healthcare happens.

Healthcare organizations around the world are experimenting with AI, but many face the same barriers: fragmented data, inconsistent formats, and limited ability to integrate AI outputs into clinical systems. HL7’s standards are designed to address these challenges, making them a natural foundation for safe and effective AI adoption.

The HL7 AI Challenge aims to:

• Encourage innovation grounded in open, widely adopted standards
• Demonstrate how structured, interoperable data improves AI performance
• Highlight real‑world solutions that can scale across organizations and borders

New implementation community builds on global collaboration to improve
real-time device data exchange for AI-enabled care

Imagine a patient in an intensive care unit, monitored by a dozen devices generating streams of critical data every second.  From operating rooms and ICUs to ambulatory clinics and patient homes, clinicians and care teams rely on a growing ecosystem of medical and personal health devices. Now imagine that data is siloed, unable to flow into the EHR and unreachable by the analytics platform that might detect a dangerous trend before a clinician does.

This fragmentation is a well-known challenge in healthcare IT. On March 5, 2026, HL7 International took a major step toward solving it with the launch of the Caliper FHIR® Accelerator, a new implementation community dedicated to improving how data from medical and personal health devices is exchanged, integrated and used across healthcare systems. 

Why Caliper, and Why Now?

Caliper builds on HL7’s 2025 work with founding members to define a collaborative community focused on device interoperability. The need is clear: healthcare organizations are generating more high‑frequency device data than ever, but too often this information cannot flow cleanly into EHRs, analytics platforms or AI‑driven applications.

By leveraging HL7 FHIR alongside established device communication frameworks, Caliper aims to create a scalable, standards‑based foundation for real‑time device data integration. The goal is simple but transformative: ensure that data from critical care equipment and patient‑facing technologies can be shared consistently, reliably and safely.

“Healthcare systems are entering a new phase where access to high-quality, real-time data is essential to safely deploying advanced analytics and AI,” said Rachel Dunscombe, CEO of HL7 International. “The Caliper Accelerator represents an important step forward in ensuring that device-generated data, whether from critical care equipment or patient-facing technologies, can be shared and used consistently across care environments worldwide. This kind of foundational interoperability is critical to improving both clinical outcomes and operational resilience.”

 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

Dr. Daniel Vreeman Named Chief AI Officer to Drive Organization's Strategic AI Agenda

HL7 has launched an Artificial Intelligence (AI) Office to establish foundational standards for safe, trustworthy AI in healthcare and convene the global community driving this transformation.

The new office positions HL7 at the forefront of healthcare's AI revolution, creating frameworks that ensure emerging technologies are trusted, explainable, interoperable, and scalable across clinical, operational, and research settings worldwide.

"Artificial intelligence will fundamentally reshape healthcare delivery, evaluation, and payment," said Charles Jaffe, MD, PhD, CEO of HL7 International. "Our new AI Office positions HL7 as the trusted global convener for responsible, standards-driven AI innovation—ensuring these transformative technologies deliver on their promise to improve health for all."

To lead this initiative, HL7 has appointed Daniel Vreeman, DPT, as its first Chief AI Officer (CAIO). Dr. Vreeman will expand his current role as Chief Standards Development Officer to drive HL7's comprehensive AI strategy, including the HL7 AI Challenge, anti-fraud initiatives, and collaborations with regulators and industry partners globally.

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.”

New publication outlines opportunities for accelerating and improving the use of AI to support payment integrity and reduce fraud, waste, and abuse

Last week, HL7 International released a new report outlining the actions needed to fully realize the benefits of artificial intelligence (AI) in improving payment integrity and reducing fraud, waste, and abuse. Reducing Fraud and Improving Payment Integrity in Healthcare Through the Use of AI brings together insights from payers, providers, and technology experts to define AI opportunities and emerging solutions, current challenges, implementation strategies, and standards needed to increase transparency and trust.

“The U.S. spends more than $900 billion annually on administrative complexity, waste, and improper payments,” said Charles Jaffe, MD, PhD, CEO of HL7 International. “This publication identifies practical and standards-based approaches to harnessing AI for one of healthcare’s most persistent and costly challenges.”

Key recommendations from the report include:

• Developing standards for explainable AI in healthcare with specific transparency requirements and bias mitigation protocols.
• Creating standards and infrastructure for trust and verification of AI-generated results.
• Establishing frameworks for human-in-the-loop validation that balance automation with clinical expertise.
• Implementing pilot programs focused on provider-payer collaboration that create mutually beneficial scenarios for all stakeholders.