Philips CEO: AI Reshaping Healthcare, Addressing Trust Barriers
Artificial intelligence is quietly reshaping the efficiency and potential of U.S. healthcare, even as government health policy and spending undergo significant shifts. At the forefront of this transformation is Philips, the venerable electronics manufacturer that has evolved into a leading medtech provider. Jeff DiLullo, CEO of Philips North America, offers insights into how technology is delivering tangible improvements in health outcomes today, from accelerating radiology scans to speeding up cancer diagnoses.
While much of the public discourse around AI often outpaces its practical implementation in many industries, DiLullo emphasizes that in medical technology, AI’s impact is already profound and immediate. Referencing Philips’s 2025 Future Health Index, he notes that AI in certain healthcare applications is remarkably mature, with many solutions already FDA-cleared and proven safe for clinical use. Other areas remain experimental, yet a significant barrier to broader deployment persists: trust.
The Future Health Index revealed a notable “trust gap”: approximately 60 to 65% of clinicians express confidence in AI, but only about a third of patients, particularly older individuals, share this sentiment. DiLullo believes that bridging this gap is a shared responsibility, with healthcare practitioners playing a pivotal role. Younger generations, being “digitally fluid,” naturally embrace AI models. For older patients, however, the direct interface with a trusted healthcare professional is crucial. If doctors and nurses believe in the credibility and utility of AI—using it to augment their analysis and diagnostics rather than replacing their expertise—patient trust will naturally follow. Philips’s role, DiLullo explains, is to provide validated, FDA-cleared AI diagnostic capabilities that empower clinicians, ultimately increasing their time with patients and reducing stress, which he believes will lead to a rapid, “parabolic” adoption of AI in health.
The practical applications of AI in healthcare are already transforming workflows, particularly in diagnostics like radiology. DiLullo highlights how AI embedded within imaging systems can dramatically reduce scan times. For instance, an MRI scan that once took 45 minutes can now be completed in just 20 minutes, thanks to “smart speed” technology that removes extraneous noise from the data. This not only yields a better-quality scan but also allows radiologists to process more studies daily—perhaps 20 instead of 12 or 15—leading to more patient throughput, improved reimbursement for hospitals, and ultimately, better patient care. Beyond acquisition, AI also streamlines workflow by intelligently pinpointing areas of concern in digital images, directing radiologists to specific regions that warrant closer examination.
This digital transformation extends to pathology. DiLullo describes how digital pathology, powered by AI, can transform the agonizing wait for a cancer diagnosis from days or weeks into mere hours. The ability to digitize the entire process, coupled with AI-driven analysis and on-demand “tumor board” meetings (virtual consultations among specialists), represents a monumental shift in efficiency and patient experience.
Addressing concerns about “AI hallucinations”—a phenomenon sometimes seen in generative AI where the system invents information—DiLullo clarifies that for mature, FDA-cleared applications like those in radiology workflow or digital pathology, human oversight remains central. While caution is warranted for more experimental generative AI models, DiLullo stresses that “not experimenting in them also is not an option.” Leading institutions like Massachusetts General Brigham, Stanford, and Mount Sinai are actively leveraging population health data to train AI models for specific and broad use cases, demonstrating the immense, immediate potential.
DiLullo likens the current state of AI in healthcare to learning to drive: there’s so much to achieve in the “neighborhood” before venturing onto the “Autobahn.” The immediate opportunity lies in optimizing existing systems and driving productivity at scale with already mature AI and virtual capabilities. While innovation for future breakthroughs is essential, DiLullo estimates that 80% of the transformative impact AI can have on healthcare delivery is achievable today, addressing the immense and urgent needs of the present.