Our AI governance structure establishes policies and standards for the ethical and effective use of artificial intelligence throughout the Health System. Several committees within this structure ensure alignment with our guiding principles of keeping AI safe, effective, responsible, secure, and ethical, while prioritizing organizational goals, regulatory compliance, and risk mitigation.

Our AI governance structure includes the following:

• AI Executive Committee – Provides strategic guidance, determines AI investment strategy, and reports to the Strategy Group and Clinical Chairs committees. This committee oversees all others.
• AI Risks, Ethics, and Policy Committee – Focuses on AI ethics and policy development, proactively identifying risks and facilitating progress across the following functional AI committees:
  • AI Teaching, Learning, Discovery, and Research Committee – Explores AI’s potential to enhance teaching and learning throughout the Health System.
  • AI Review Board – Works to integrate and optimize AI in clinical care and workforce productivity by reviewing AI requests, ensuring selected projects follow appropriate processes, and promoting AI awareness and education throughout the Health System.

As a foundation to all of the above, DTP’s Technology and Enablement Governance provides essential guidance on the technology needs that support AI across Mount Sinai.

DTP’s Technology and Enablement AI Governance advances system-wide clinical and operational AI priorities through standardized rules, guidelines, processes, and requirements that shape how AI is designed, procured, implemented and deployed across the Health System. By enabling seamless, modernized experiences for both patients and employees, the AI Governance Team works to:

• Manage the AI lifecycle to ensure that clinical and operational workforce productivity AI initiatives are safe, effective, responsible, secure, and aligned with Health System objectives.
• Manage AI lifecycle by evaluating solutions, supporting small pilots, and implementing checkpoints to measure success. These steps ensure a structured approach before scaling AI solutions enterprise-wide. 
• Oversee progress, mitigate risks, and drive value realization while fostering transparency, innovation, and a collaborative culture to advance AI capabilities and accelerate impactful AI adoption.

Do you have an idea for an artificial intelligence product or solution? We welcome you to submit your idea using the AI Idea Intake Form, available in Service Now. Your proposal will be reviewed, evaluated, and prioritized by the appropriate members of our AI governance structure. To support a smooth and efficient intake process, please review our guidelines of requirements and qualifications.  

Effective AI lifecycle management ensures that clinical and workforce productivity AI initiatives are safe, effective, responsible, secure, and strategically aligned with health system objectives. This process involves overseeing the entire AI lifecycle, from development to deployment, with a strong emphasis on transparency, innovation, and collaboration. By implementing rigorous oversight, organizations can proactively identify and mitigate risks, ensuring AI solutions contribute meaningful value while maintaining ethical compliance and reliability.

To accelerate impactful AI adoption, it is essential to foster a culture of continuous improvement and accountability. This includes closely monitoring progress, assessing potential risks, and measuring value realization to refine AI strategies over time. By establishing a structured approach, organizations can mature their AI capabilities, maximize return on investment, and enhance patient and provider experiences, while safeguarding the integrity and effectiveness of AI-driven solutions.

DTP provides a centralized list of all clinical, operational, and financial AI models and features, which includes both predictive and generative technologies. This central portfolio helps increase awareness of existing AI tools, thereby reducing duplication and enhancing efficiency, consistency, and collaboration across the Mount Sinai Health System.