As artificial intelligence continues to shape the future of healthcare, the focus is shifting from innovation alone to how AI can be applied responsibly to improve patient outcomes and clinical workflows.
This was the focus of the WomenTech Network webinar with GE HealthCare leaders on March 26th, exploring how organizations can move from experimentation to meaningful, scalable impact. Hosted by Lori Lizotte, Head of Strategic Partnerships at WomenTech Network, and moderated by Sapna Rao, Executive in Organization Development at GE HealthCare, the session featured Adriana F. Jimenez Avalos, AI Solutions Leader at GE HealthCare, and Karen Tam, Executive – Strategy and Growth Initiatives at GE HealthCare.
Opening the discussion, Sapna Rao reflected on the pace of change and its impact across the industry: “AI is evolving so quickly… it’s both exciting and, at times, overwhelming — but ultimately, it’s about how we use it to improve patients’ lives.”
Aligning People, Process, and Technology in Healthcare AI
A central theme throughout the webinar was that successful AI adoption in healthcare depends on more than technology.
Karen Tam emphasized that the real challenge lies in alignment: “It’s actually not the technology problem. It’s fundamentally an alignment problem across the people, process and operating models.”
In practice, this means rethinking how care is delivered and how teams work. Without that shift, new tools risk adding complexity rather than reducing it. As Karen Tam noted, “you are basically effectively asking them to do more and not different.” For healthcare organizations, this highlights the importance of integrating AI into workflows in a way that supports clinicians and improves efficiency without increasing burden.
Building Trust Through Transparency and Human-Centered AI
For Adriana Jimenez Avalos, the success of AI in healthcare depends on trust as much as performance. “I don’t think I separate what I value from the technology,” she said, describing how transparency and responsibility shape the way AI solutions are developed.
In clinical environments, understanding how AI systems work and where their limitations lie is essential. “AI is a tool that was designed by humans to help humans. So that needs to be the center of it.”
Sapna Rao reinforced this perspective, highlighting the importance of critical thinking alongside adoption: “It’s not just about how AI works — it’s about knowing where it can help, where it can fail, and how we apply judgment.”
Moving from AI Pilots to Scalable Healthcare Solutions
While many healthcare organizations are actively exploring AI, scaling these solutions remains a common challenge.
Karen Tam pointed out that progress often slows after initial pilots: “Where many of these organizations get stuck is actually at the pilot phase.”
The barrier is rarely a lack of innovation. Instead, it comes down to infrastructure, governance, and alignment across stakeholders. Clear success metrics and systems that support faster implementation are essential to move from experimentation to enterprise-wide adoption.
From an operational perspective, Adriana highlighted the importance of readiness across teams: “Do they fully understand it? Do they know when to challenge AI?” This reinforces the growing need for AI literacy in healthcare, ensuring that teams can use these tools effectively and responsibly.
A Human-Centered Future for AI in Medicine
As the discussion concluded, one idea remained consistent: AI should enhance, not replace, human decision-making in healthcare.
By processing large volumes of data and supporting routine tasks, AI enables clinicians to focus more on patient care and clinical judgment. “Now the human can focus on that human-centered attention,”Adriana noted.
For healthcare organizations, the opportunity is clear: to use AI to improve efficiency, accelerate insights, and deliver more personalized and precise care.
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