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Industrial AI

Trust and scale: AI in healthcare and manufacturing

By: U.S. Corporate Communications

Editor’s note: This narrative version of a recent Optimistic Outlook podcast episode, “AI Healthcare and Manufacturing: Why Adoption is the Real Problem”—a conversation between Brittany Ng, Vice President, Siemens Digital Industries Software and Demetri Giannikopoulos, Chief Innovation Officer at Rad AI—describes the potential of AI across two crucial domains, healthcare and manufacturing, when it has the data it needs.

Artificial intelligence is moving decisively beyond experimentation and into the real world, reshaping how critical work gets done in both hospitals and industrial environments. From radiology suites to shipyards, the conversation is no longer about what AI could do, but how it is already augmenting human expertise to deliver measurable impact.

In a recent Optimistic Outlook podcast interview between Brittany Ng of Siemens Digital Industries Software and Demetri Giannikopoulos, Chief Innovation Officer at Rad AI, a common theme emerged: the future of industrial AI in healthcare and manufacturing innovation depends not on replacing people but on empowering them to make better decisions in high-stakes environments.

Delivering the right healthcare data for patients

Across industries, AI is proving most valuable when it enhances—not replaces—human judgment. Nowhere is this clearer than in healthcare, where the volume of data can overwhelm even the most experienced clinicians.

Healthcare “is an area where the amount of information that's coming out is just overwhelming,” Giannikopoulos said on the podcast. “Being able to cut through that noise can be incredibly difficult.”

By aggregating and interpreting data at scale, AI in healthcare is helping clinicians connect the dots faster—reducing missed or delayed diagnoses and guiding more precise care pathways. On the podcast, Giannikopoulos pointed to AI’s ability to bring together fragmented information across systems and specialties, offering a clearer, more complete picture of patient health.

“At its core, AI. . .[can] take the incredible amounts of data that we have. . .and ultimately help connect that patient with the best information and best pathway available for them,” he said.

Innovating with the “digital backbone” of industry

While the stakes may differ, the underlying systemic challenges in manufacturing and shipbuilding are very similar to those in healthcare: complex systems, high safety requirements, and the need for precision at every step.

The turning point, as Brittany Ng noted on the podcast, has been the emergence of a robust digital foundation—including digital-twin manufacturing environments and connected production systems.

“We finally have what I call the digital backbone,” Ng said. “AI now has this structured contextual data to work with, not just fragmented spreadsheets.”

This digital backbone is enabling AI to move beyond theoretical insights to actionable recommendations—optimizing planning, improving quality, and strengthening production capacity in industries that are critical to national defense and economic resilience.

Central to the AI transformation is access to high-quality, connected data. In healthcare, longitudinal datasets that span multiple systems can unlock earlier detection and more effective treatments. In manufacturing, linking data across design, engineering, and production enables continuous improvement.

Building trust in the technology

Despite rapid advances, both Ng and Giannikopoulos emphasized that the biggest barrier to scaling AI is not innovation—it’s trust.

In healthcare, trust is essential because patient outcomes are directly at stake. That demands rigor in how AI systems are trained, updated, and validated.

“You need to build trust with the clinicians,” Giannikopoulos said. “You need to build trust with the patients who these technologies are being used to help.”

Similarly, in industrial environments, adoption depends on ensuring that AI integrates seamlessly into existing workflows and empowers workers in such a way that they can believe in and adapt to its increasing use.

“Success depends on empowering engineers and tradespeople with AI tools that augment their expertise, not that disrupt how they work,” Ng noted.

This emphasis on trust, governance, and reliability is shaping how AI is deployed across both sectors—healthcare and industry—moving cautiously but deliberately toward scale.

Connected data is the foundation of trust

Central to this transformation is access to high-quality, connected data. In healthcare, longitudinal datasets that span multiple systems can unlock earlier detection and more effective treatments. In manufacturing, linking data across design, engineering, and production enables continuous improvement.

Ng framed the challenge succinctly: “AI is truly only as powerful as the continuity of that [data] thread.”

Breaking down data silos and enabling interoperability across ecosystems is critical—not just for performance, but for ensuring that AI delivers consistent, reliable outcomes.

AI is a workforce multiplier

Beyond productivity, AI is increasingly seen as a way to address workforce challenges in both the healthcare and manufacturing domains. In healthcare, clinician shortages and growing demand are straining systems. In manufacturing, skilled labor gaps are limiting output.

AI can help by automating repetitive tasks and elevating human roles toward higher-value work.

“By automating planning, documentation, and analysis, AI actually frees up engineers. . .to focus on craftsmanship and problem solving,” Ng said.

In radiology, similar gains are already emerging, as AI assists with analysis and reporting—allowing clinicians to focus more on patient care.

Accelerating innovation across industries

Perhaps most compelling is how AI is unlocking entirely new possibilities. In shipbuilding, generative design and dynamic-production simulations are enabling faster, more adaptive decision-making. In healthcare, AI is accelerating research timelines and helping uncover new treatment pathways.

“Now clinicians can do research at whole new levels,” Giannikopoulos said. This ultimately can result in people “surviving in situations where they wouldn’t have in the past” because of AI-powered discoveries in research.

Such breakthroughs highlight how AI is not only improving existing processes but also expanding what is possible—across both human health and industrial systems.

Scaling AI for the future

As AI adoption accelerates, the convergence of data, technology, and urgency is creating a unique period of opportunity. Both Ng and Giannikopoulos spoke of a future where AI is fully integrated into the fabric of work—supporting better decisions, stronger outcomes, and more resilient industries.

What will determine success is not just technological capability, but the ability to deploy AI responsibly, transparently, and at scale.

The path forward is clear: build trust, connect data, and focus on augmenting human expertise. With those foundations in place, AI can deliver on its promise—transforming how we care for patients, build critical infrastructure, and solve some of the most complex challenges of our time.

Published: June 16, 2026