Creating Trustworthy AI in Healthcare is critical. It can impact life and death and quality of life. We need Strategy, Plans and Action to Address the Privacy, Impact of Algorithm Bias in Health and Healthcare, and other aspects of Trustworthy AI such as Explainability.

In this podcast I speak with Tamra Moore who is one of a handful lawyers in the US associated with legal issues on the use of AI/ML in healthcare, she also has a degree in Biomedical Ethics.

I am Pamela Gupta, CEO Trusted AI and host for the thought leadership series Trustworthy AI : De-risk business adoption of AI. Through these talks I am bridging Strategy and Actionable guidance from global Industry leaders to create practical guidance in reducing risk for managing the complex and high risk area of AI.. I believe we can achieve Trustworthy AI, but in order to do that is essential to be built in at the very DNA of an AI system or adoption, hence the Trusted AI logo – with a DNA strand for I in AI.

Tamra is an attorney with over 15 years of federal government and private sector litigation and regulatory experience.

She’s currently in-house counsel at a global financial services company, where she advises the Chief Data Officer and others on legal and regulatory compliance issues. These issues are related to the development and use of AI and ML models in consumer-facing products and internal operations.

Last year, at the invitation of the NIH and Agency for Healthcare Quality and Research, she participated on a consensus panel looking at racial bias in healthcare algorithms.

We discuss :

1. How can we “Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Healthcare.”

2. What is the US regulatory landscape with respect to the use of AI in healthcare?

3. What is the HHS rule on Algorithmic transparency. What is it, who is impacted and what does one have to do to be compliant.

Tamra talks about the Office of the National Coordinator for Health Information Technology (ONC) Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-1) rule.

4. What are some of the challenges of implementing a governance framework for healthcare algorithms used in a hospital setting?

Can Trustworthy AI help De-Risk adoption of AI? ‘Can Trustworthy AI can be instrumental in helping organizations gain a competitive edge and promote better business outcomes, including accelerated innovation with AI’.?
With extensive experience in global industry leadership in areas of Business Strategy, Technology, and Cybersecurity, Pamela helps clients in creating a strategic approach to achieving business value with AI by adopting a holistic risk based approach to AI Trust. She defined 8 essential pillars of trustworthy AI. Read more details at Trustedai.ai website.

Her insights have shaped the way we look at the impact of Cyberwarfare on Business, strategies for efficient digital transformation, and governance views on Algorithmic failures.

Join Pamela as she delves into her signature framework, AI TIPS, standing for Artificial Intelligence Trust, Integrity, Pillars and Sustainability. This podcast is all about operationalizing governance and building Trustworthy AI systems from the ground up.

For questions or comments on this podcast reach out to me.
To request an AI adoption assessment

Healthcare Trustworthy AI: De-risk adoption of AI

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