UPDATE — MARCH 2026:
Since the Coalition for Health AI (CHAI) released its Assurance Standards Guide in June 2024 and subsequent framework updates in 2025, the organization has continued expanding its efforts to operationalize responsible AI governance in healthcare. A major development came through CHAI’s collaboration with The Joint Commission. This commission is one of the largest healthcare accreditation bodies in the United States. In September 2025, the partnership released “Responsible Use of AI in Healthcare” guidance, a non-binding framework. It outlines practical steps for healthcare organizations adopting AI technologies. The guidance emphasizes governance structures, performance monitoring, bias mitigation, data protection safeguards, and clinician training. These actions support safe and accountable AI deployment.
CHAI has also continued growing its multi-stakeholder network. By late 2025, membership had expanded to include nearly 3,000 organizations and individuals across healthcare systems, technology developers, patient advocacy groups, academic institutions, and startups. This broad participation reflects an effort to ensure that AI governance standards in healthcare incorporate diverse perspectives from across the industry.
In parallel, CHAI has been working to translate its assurance standards into more practical oversight mechanisms. The organization has continued developing assurance reporting checklists and refining proposals for independent evaluation processes. Additionally, it has supported the creation of AI Quality Assurance Labs intended to test and validate healthcare AI systems.
Additional collaborations have also focused on improving AI adoption in underserved healthcare environments. In 2025, CHAI partnered with organizations such as the National Association of Community Health Centers (NACHC). Together, they explored how responsible AI tools can support care delivery in community health systems.
Looking ahead, CHAI and its partners are continuing to refine governance playbooks and voluntary certification approaches.
ORIGINAL NEWS STORY:
CHAI Releases Comprehensive Assurance Standards Guide for AI in Healthcare
The Coalition for Health AI (CHAI) has unveiled its “Assurance Standards Guide,” a pivotal document designed to ensure the safe, effective, and responsible development and deployment of artificial intelligence (AI) solutions in the healthcare sector. This guide, released on June 26, 2024, aims to establish a comprehensive framework that addresses the myriad challenges and opportunities presented by AI technologies in healthcare.
Group Effort
The guide is the result of an extensive year-long collaborative effort by CHAI workgroups, which included clinicians, data scientists, bioinformaticists, ethicists, patient advocates, and professionals from both large and small technology development firms. These workgroups were meticulously formed, considering gender and ethnic diversity. They also included faculty members from Historically Black Colleges and Universities. The iterative process involved weekly meetings, stakeholder feedback, and multiple drafts to create a consensus-driven set of standards that would be widely adopted.
Prior Documents
The Assurance Standards Guide builds on CHAI’s previously established Blueprint for a comprehensive assurance framework. It aims to balance the benefits of AI with the need to mitigate risks related to usability, safety, equity, and security. The guide emphasizes tangible considerations for all stakeholders involved in the health ecosystem, ensuring that AI implementation is fair, transparent, safe, and beneficial.
Lifecycle Approach
A central feature of the guide is its lifecycle framework. It begins with problem definition and planning, moves through ethical design and engineering, and ends with deployment and monitoring. At each stage, the guide stresses reliability, safety, and ethical oversight. Another critical feature is independent review. External experts evaluate AI systems to ensure compliance with safety, ethical, and effectiveness standards. This process helps build trust and wider acceptance in healthcare AI.
Privacy and Cybersecurity
The guide also emphasizes protecting sensitive health data. It incorporates the NIST Privacy Framework and Cybersecurity Framework to help organizations manage risks. Therefore, it provides a structured method for securing patient data and ensuring compliance with laws and regulations.
Practical Use Cases
The guide illustrates its principles with real-world examples. These include predictive EHR models for pediatric asthma, AI-driven mammography diagnostics, and generative AI tools for EHR data queries. For example, the asthma case study shows how AI assurance can protect vulnerable populations while improving care.
A Living Document
CHAI stresses that the guide is not static. As AI evolves, so will the standards. Future versions will integrate new insights, technologies, and feedback. This living framework ensures the guide remains relevant to emerging challenges in healthcare AI.
Need Help?
Keeping track of the everchanging AI landscape can be tough, especially if you have questions and concerns about how it will impact you. Don’t hesitate to reach out to BABL AI. Their Audit Experts are ready to provide valuable assistance.
