Fireside Chat: Roy Jakobs

Session Focus

Philips spans imaging, therapeutic technology and patient monitoring, which framed a conversation about integrated platforms, the practical use of AI along a full clinical pathway, and what health systems need to do psychologically to be ready for it.

Why Platforms Rather Than Point Solutions

Jakobs began from what health systems are facing: mounting pressure from rising demand, patient volumes that are hard to keep up with, staffing challenges, and access and affordability problems.

What customers ask for, in his account, is not more products. It is standardization, consolidation and simplification of workflows that support how they actually practice.

He described the same pattern across three lines. In monitoring, a line that can begin in the home, bring a patient in and follow them through the hospital journey seamlessly and securely. In imaging, optimizing the workflow against a significantly increased load. In intervention, where the disease span has widened from cardiac and neuro into oncology while data volume, technology in the cath lab and staffing have all grown, making procedures more complex at higher volume.

His answer in each case is an open platform that connects to other systems in the hospital and makes hardware, software and AI work together across workflow steps.

Handovers as Risk

Asked about care that spans departments and settings, Jakobs made the point sharply: healthcare does not flow. A patient journey is many stops and many handover points, and every handover point is a risk.

Each transition between care settings creates a burden on the provider that has an impact on the patient. Improving patient flow therefore depends on data flow that connects seamlessly, and on technology that does not become its own burden across providers or vendors.

AI changes the economics of that, in his view, because it can reach different data sources far faster than traditional software, and because closed systems can be elevated into open ones through appropriate use.

AI Across a Single Clinical Pathway

Jakobs said there is no product in the Philips health tech portfolio where AI is not already used in some form, then made the point that it is not about AI but about what it does for the product. He walked the cardiac pathway end to end.

Detection. Ambulatory monitoring running a cardiac algorithm trained over seven years to identify arrhythmias early.

Diagnosis. Cardiovascular ultrasound where AI reduced the clicks required to diagnose a patient from 18 to three, which is immediate relief for the technologist, faster for the patient and more accurate.

Deeper diagnosis. Cardiac MR, where a complex exam traditionally ran up to 60 minutes, cumbersome and intrusive. The SmartSpeed algorithm brought that to 30 minutes, and the company is developing a 10-minute cardiac examination with institutional partners. That expands system capacity and lowers patient burden simultaneously.

Intervention. A cath lab system using AI to fuse images from different sources into a 360-degree patient view, and intravascular imaging with AI guidance enabling less intrusive stent placement positioning.

Discharge. Sending patients home on monitoring that watches cardiac recovery, which frees the cath lab for the patients waiting.

His framing of the purpose was consistent: give time back. Time back to nurses to spend with patients, to technicians to streamline the process, and to physicians to do value-added work while routine tasks run on algorithms.

Asked how integrated this is in practice, he described a clear global trend toward cardiac centers that run the whole pathway in one place, so a patient is not sent to the basement for MR, the third floor for the cath lab and the second for ultrasound. Beyond efficiency and patient experience, he argued concentration accelerates learning, because volume rises and research and innovation compound faster.

People-Led and AI-Powered

Asked what providers can do to prepare psychologically, Jakobs offered a formulation he applies to Philips itself: people-led and AI-powered.

He was careful to say he is not more enamored of AI than of hardware, citing MRI breakthroughs as equally important. Technology exists to serve people.

The practical version is that technology should absorb the data gathering clinicians never wanted, freeing them for complex cases and patient time. He noted that a nurse averages three to seven minutes with a patient, and argued that emotional connection matters to recovery alongside the treatment itself.

On autonomy, he expects autonomous imaging to push quite far into the patient journey, with a radiologist holding the final say at the last stop.

On AI’s broader capability, he pointed to mathematical problems that had resisted human solution as evidence of what the technology can do, then drew the distinction that matters in healthcare: you cannot throw AI at a hospital, you have to be in the workflow to apply it.

Key Takeaways

1. Every handover is a risk point. Improving patient flow depends on data flow that connects across settings.

2. Customers want simplification, not more products. Standardization and consolidation are the stated demand.

3. AI is already deployed across a full pathway. Detection, diagnosis, intervention and discharge each carry working examples.

4. Capacity and patient burden improve together. Cutting a cardiac MR from 60 to 30 minutes serves both.

5. Concentration of a specialty accelerates learning. Volume in one place compounds research and practice improvement.

6. Workflow presence is the precondition. AI applied outside the workflow does not produce results.