Mount Sinai's Dr. Girish Nadkarni Reveals the Blueprint for Healthcare AI

Dr. Girish Nadkarni in Leading health system Mount Sinai demonstrates how enterprise AI success depends on culture change, not just cutting-edge models

WASHINGTON, DC, UNITED STATES, October 3, 2025 /EINPresswire.com/ -- In the latest episode on the CAIO Connect podcast, hosted by Sanjay Puri, Dr. Girish Nadkarni focused on what it really takes to implement AI at scale in healthcare- and his insights are reshaping how enterprise leaders think about AI transformation.

As Chair of the Windreich Department of Artificial Intelligence and Human Health and Chief AI Officer at Mount Sinai Health System, Dr. Nadkarni oversees AI implementation across one of America's largest academic medical centers. But his approach challenges conventional wisdom about enterprise AI deployment.

Most enterprise AI initiatives fail not because of technology limitations, but because organizations get the process backwards. They build solutions without understanding the actual problems people face on the ground.

Dr. Nadkarni's alternative emphasizes flipping the paradigm entirely. Mount Sinai's success stems from involving end users like nurses, physicians, administrators in every phase of AI development, from ideation through deployment.

"If you sort of meet people where they are and include them in the change management process, that becomes this self-perpetuating flywheel where people who adopted it tell other people about it," Dr. Nadkarni commented. "Because other people trust them, more people adopt it, and it continues."

When pressed about demonstrating ROI to C-suite executives, Dr. Nadkarni emphasized that financial returns aren't the only metrics that matter. His framework considers four critical dimensions: how fast decisions need to be made, how reversible they are, how critical they are, and how close they are to patient care.

Take ambient scribes, for example- AI tools that document patient visits automatically. While the financial returns may be modest, they deliver value in burnout reduction, clinician satisfaction, and improved patient interactions. These benefits translate to better workforce retention, avoiding the substantial costs of recruiting and training replacements.

During the COVID-19 pandemic, when Mount Sinai became "the epicenter of the epicenter," the health system evolved into what Dr. Nadkarni calls a learning health system, one that continuously extracts insights from operational data, tests them rigorously, and scales what works.

This approach requires a culture of psychological safety where teams can take multiple shots on goal without fear of punishment for failure- as long as they learn something valuable. The model creates a virtuous cycle: operational data generates insights, which are tested and measured, then adopted and scaled to generate new data.

How does Mount Sinai attract top AI talent when competing against tech giants? Dr. Nadkarni's pitch is refreshingly honest about the tradeoffs involved.

"We can't offer Silicon Valley salaries, but health systems are the place to make an immediate, measurable, and visible impact on care because health systems treat millions of patients per year," he said.

His team isn't just looking for AI scientists and ML engineers. They need implementation specialists, clinicians who understand workflow pain points, informaticists who map processes, qualitative researchers, and storytellers who can inspire adoption across the organization. Mission-driven professionals who want to see their work directly improve patient outcomes find this environment uniquely rewarding.

Dr. Nadkarni pointed to graph neural networks as an emerging technology that moves beyond the linear understanding of transformers to create more comprehensive world models. His advice for fellow Chief AI Officers is clear: build infrastructure that spans the enterprise rather than point solutions, and design for scale from the start while involving end users at every step.

"Think about building infrastructure that spans the enterprise, not just point solutions, and build to scale, not to pilot," Dr. Nadkarni advised. "And involve end users and communities in every step of the process."

In an era of breathless AI enthusiasm, Dr. Nadkarni's measured, human-centered approach offers enterprise leaders a practical roadmap for AI transformation—one that prioritizes culture change, rigorous evaluation, and meeting people where they are over chasing the latest model or promising moonshots.

Ananya Dutta
Knowledge Networks
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Scaling AI in Health Systems: Lessons from Mount Sinai with Dr Girish Nadkarni | CAIO Connect

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