Scale AI leader on why clinical AI needs a ‘trust layer’

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For this week’s Making Rounds, we talked with MaryAnne Rizk, general manager of healthcare and life sciences at Scale AI.

The software company focuses on providing data and other tools to help develop and oversee AI systems. Since its 2016 founding, it has worked with big names including Cisco, Mayo Clinic, the US federal government, and even the government of Qatar. Meta announced plans to invest $14.3 billion in the company in June 2025 after Scale generated close to $1 billion in revenue the year before, per CNBC.

Rizk talked with us about the company’s efforts to increase responsible, evidence-based AI in healthcare.

This interview has been lightly edited for length and clarity.

How would you describe your job to someone who doesn’t work in healthcare?

I focus on scaling reliable, clinical-grade AI across the patient journey, from the point of care to innovative medicine. My role is to build agentic AI systems that are both clinical grade and regulatory grade, transforming fragmented data into real-time, evidence-based decisions.

What healthcare trend are you most optimistic about?

Agentic clinical AI is becoming a core healthcare infrastructure, aligned with a national push to expand capacity, improve outcomes, and modernize care delivery. Healthcare venture funding jumped to nearly $60 billion across 2,000 or so deals—the strongest rebound since 2022, with a growing share flowing directly into clinical AI platforms.

Backed by national health standards like TEFCA and HTI-1 and 2, we now have the infrastructure for nationwide data exchange as well and the technology to make this data actionable in a way it never has been before.

What healthcare trend are you least optimistic about?

I’m concerned that some leaders are overlooking the trust layer that healthcare systems need to move from pilot to production. While investment is rapidly increasing, we need leaders to measure impact across pillars that actually matter, including precision, accuracy, efficiency, and ethics. Healthcare doesn’t need more AI for the sake of it. It needs clinical-grade, regulatory-grade systems that deliver measurable outcomes.