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Daniel Spector

Inside the CHAI Leadership Summit: What’s Next for Responsible AI in Healthcare

Adoption, Data Management, Information Technology, Outcomes
Table of Contents

On June 5, leaders from across healthcare, academia and tech gathered for the Coalition for Health AI (CHAI) Leadership Summit to tackle one of the most pressing challenges in healthcare today: how to move from promising AI prototypes to safe, scalable systems that improve care.

As AI tools become more powerful and more present, training the workforce to understand, trust and use them effectively is essential. Without that foundation, adoption slows and risk grows.

Perhaps the most complex challenge of all is governance. Building strong algorithms is only part of the equation. Health systems need consistent ways to evaluate tools, measure impact and ensure accountability. That is where CHAI continues to lead.

One of its most valuable contributions has been the development of CHAI model cards. These standardized summaries help AI vendors explain how their tools are built, validated and monitored. For health systems, model cards offer a clear, consistent way to assess risk, performance and fit before bringing a solution into patient care. Since the release of the HTI-1 Final Rule, these kinds of structured evaluations have become a critical part of the procurement process.

Now, CHAI is going a step further with the launch of a public registry. This new resource gives vendors a central place to publish model cards and provides governance teams with easier access to the information they need to make informed decisions.

The landscape is shifting fast

Health systems are no longer asking whether to adopt AI, but how to do it responsibly. What once felt like a future-state conversation is now an operational reality. With regulatory pressures increasing and internal governance structures taking shape, the demand for transparency is growing. It is no longer enough to show what a model can do. Vendors must show how it works, where its data comes from, how it is monitored and who it serves best. Tools like model cards and public registries are not just nice to have – they are becoming baseline requirements.

In practical terms, this means health systems can spend less time decoding vendor documentation and more time focusing on clinical value. For example, instead of manually comparing inconsistent risk disclosures across vendors, a governance committee can now refer to a common format that surfaces key information: bias mitigation strategies, training data summaries, performance metrics across demographics and more. This level of clarity speeds up decision-making and builds confidence in the tools being brought into patient care.

This summit highlighted the power of collaboration. We appreciated the chance to help shape the model card framework alongside others who are equally focused on building tools that healthcare organizations can trust and use.

As AI adoption accelerates, the industry needs shared infrastructure – not just at the technical level, but at the level of trust, oversight and common standards. CHAI’s work is helping build that foundation. We look forward to continuing the work with our peers and partners to make responsible, transparent AI the new standard in healthcare.

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Daniel Spector
Daniel Spector is an AI Transformation Consultant at Aidoc. His focus is enabling clinicians and hospital administrators to provide better, more efficient care through the power of AI augmentation for enterprise imaging.
Daniel Spector
Consultant, AI Transformation

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