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.

At the Mount Sinai Health System, we use artificial intelligence (AI) tools to create health information content and other communications in a safe, ethical, and responsible way, and have created a guidelines document toward that goal.

We also believe that transparency about our use of AI to our patients, readers, and stakeholders is key to building trust internally and externally. Here is a broad list of how we may use AI tools in our work.

What we do not do with AI

• At no point will any material generated, altered, or enhanced by AI be published or go live without human oversight to check for accuracy, plagiarism, or biases. This applies to all use cases listed below.
• We do not use information generated by AI tools without checking it with relevant experts, resources, and stakeholders for accuracy.
• We do not enter any sensitive information, including patient information or confidential information, into any AI platform that has not been vetted and approved by the Health System.

Where we might use AI

• We might use generative AI tools to create web utility copy (descriptions, calls to action, contact information, headers, etc.), email newsletter copy, or boilerplates.
• We might use AI to help optimize copy to make it effective in reaching a targeted audience.
• We might use generative AI to create generic visual assets, including background textures, single objects, data visualizations and graphs, illustrations, and animations.
• Where AI tools are used to modify images or video, we ensure we do not infringe any trademarks or copyrighted material, violate or infringe on the terms of any licensed content, and our use is in accordance with any applicable Fair Use and Creative Commons license.
• We also ensure that we do not violate individuals’ rights of publicity, and we do not create images about individuals without their consent.
• We might use generative AI to brainstorm story ideas or speaking points, and create summaries of complex/lengthy subjects.

As AI technology evolves and changes, we anticipate this list might change accordingly. We welcome you to revisit this list from time to time. If you have any questions about AI use in Marketing/Communications, please email AIMarketing@MountSinai.org.

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.