Why Healthcare Organizations Must Prioritize AI Governance

Before a healthcare organization adopts a new technology for widespread use, there’s likely a structure already in place involving multidisciplinary oversight to test and validate the solution.It shouldn’t be so different when implementing an artificial intelligence-powered solution. Though the technology may be different, it’s unlikely that your organization has to start completely from zero with AI governance.Having AI governance in place is particularly crucial because there is still a lot of uncertainty around industry standards and federal regulations. That may spur more scrutiny on the state level: In Texas, for example, the state attorney general and a Dallas-based healthcare technology company reached a settlement in September 2024 after a number of false and misleading claims on the accuracy of the company’s AI-powered products.

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AI governance can also be viewed as an extension of data governance. In the end, AI is composed of data and advanced analytics at its core. AI solutions need the data governance principles and practices of data stewardship, data quality, metadata management, data privacy and security just as much or more than traditional data and analytics solutions need them. It is important to not reinvent governance for AI solutions from the ground up without considering the critical foundational elements of data governance.

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