Precision care needs more than algorithms – it needs partnership

We’re living through one of the most consequential moments in healthcare history. AI is already reshaping how clinicians work, how patients engage with their care and how health systems operate. According to the Philips Future Health Index 2026, 84% of US healthcare professionals are optimistic that AI can improve patient outcomes, and 72% believe the benefits already outweigh the risks. These are the numbers of a profession that’s ready.

But readiness and results are two different things. And the gap between them is where the real work begins.

When I think about precision care, I think about pieces of a picture. A single imaging scan is one piece. A diagnostic lab result is another. A patient’s genomic profile, their clinical history, their monitoring data – each is a fragment. The full picture only emerges when those fragments come together, and it’s only when you can see the whole picture that you can act with true precision.

This is why I believe so strongly that the future of precision is about better ecosystems (not just algorithms); what I call an ecosystem of supported care.

Imaging, pathology and monitoring are already enabling care teams to pair patients with labs, genomics and pharmaceutical partners to deliver life-changing outcomes. Philips sits at the center of that intersection, working to make imaging acquisition faster, clearer, and more clinically actionable. But the imaging scan alone doesn’t change a patient’s life. It’s what happens when that scan connects to the broader care network that truly matters.

I’ve seen this up close. Before joining Philips, I built the AI partner program at TeraRecon, working with third-party AI companies to bring in capabilities we weren’t developing in-house. That experience shaped my view of what partnership really means in healthcare technology. Less a commercial arrangement, it’s a recognition that each partner has domain expertise – and that when best-in-class capabilities come together, they multiply value.

The data is encouraging. According to the Philips Future Health Index 2026, 49% of US clinicians are saving at least 132 hours each year – more than three full working weeks – through AI-enabled tools. More than one-third (36%) say AI has increased their capacity to see more patients, with a median increase of five additional patients per week. AI is beginning to act as a cognitive layer, quickly consolidating patient records, imaging data and clinical evidence so clinicians can focus on making more informed decisions rather than hunting for information. That’s real and meaningful. And I don’t want to undersell it.

But here’s what concerns me: 77% of US healthcare professionals say AI training at their organization is unavailable, limited or inconsistent. Seventy-six percent are concerned about AI making errors. Sixty-seven percent flag a lack of transparency in how AI reaches its recommendations. And 69% say clear processes for monitoring AI-enabled tools aren’t yet in place.

AI innovation, in other words, is moving faster than healthcare systems can absorb it. The constraint is integration.

I’ve seen this pattern before. During my years at GE Healthcare, I worked on building global data agreements for the early development of what would eventually become AI-enabled digital products. The technology was ahead of the infrastructure then, too. What made the difference wasn’t the algorithm. It was the organizational will to build the right foundations around it.

Let me be direct about something. I’ve never believed AI is here to replace clinicians. Not once, across 17 years of practicing medicine, three healthcare startups and roles at GE Healthcare, Blue Cross Blue Shield, TeraRecon and now Philips, have I seen technology succeed by removing the human from the equation.

What AI does – at its best – is give clinicians more room to be human. More time for nuanced thinking. More bandwidth for the complex cases that demand clinical judgment. More capacity to have real conversations with patients. The Philips Future Health Index 2026 confirms this: 93% of US clinicians say it’s essential to keep a human in the loop as AI advances. And 62% say AI will help them work at the top of their license.

Working at the top of your license is what happens when you remove the administrative friction, the cognitive overload, the time spent searching for chart information that should already be in front of you. It’s the difference between a clinician who’s overwhelmed and a clinician who’s empowered. And that empowerment is what precision care is built on.

One of the reasons I’m most excited about AI in diagnostic imaging is its capacity to help the unknown become known. Lung cancer is the leading cause of cancer-related deaths globally, and AI is already supporting earlier identification of lung nodules and their characteristics– the small findings that can indicate potential disease – helping clinicians detect changes more consistently and at earlier stages. That earlier detection changes outcomes and lives.

But this is only possible when the imaging data is high-quality, the AI is well-validated and the findings seamlessly connect to the next step in the patient’s care journey. Remove any one of those elements, and the picture breaks down.

I think about this through a very personal lens. My husband has faced two life-changing diagnoses. That experience – of watching someone you love navigate a healthcare system that doesn’t always connect its dots and placing you in an advocacy role – has shaped everything around how I think about ecosystem collaboration. For me, it’s not abstract. It’s urgent. I have been the one holding the hand of the patient waiting through those agonizing moments for results that redefine life as you know it.

When I talk about ecosystem collaboration, I’m not talking about a loosely affiliated network of vendors. I’m talking about strategic, deliberate partnerships where each organization contributes its domain expertise and the patient is always at the center.

In imaging, this means connecting the precision of a CT or MRI scan with AI tools capable of surfacing clinically meaningful insights, then feeding those insights directly into the care team’s workflow. It means partnering with genomics companies so that what’s found in a scan can be contextualized against a patient’s molecular profile. It means collaborating with pharmaceutical partners so that treatment decisions are grounded in the most current clinical evidence and imaging data available.

It also means being honest about what we don’t do well – and finding the right partners to fill those gaps, all to make sure the ecosystem functions as a whole.

Right now, we’re at an inflection point. AI adoption is near-universal but uneven in depth. The tools are there. The evidence is growing. The enthusiasm is real. What’s still being built is the connective tissue – the interoperable systems, the trained workforces, the governance frameworks and the cross-sector partnerships that turn isolated AI applications into a genuinely integrated ecosystem of supported care.

Seventy-five percent of US healthcare professionals say their role is becoming more focused on higher-value clinical work. Sixty-one percent anticipate AI agents becoming part of their clinical team. The hybrid care team – clinician, patient and AI working in concert – is already forming.

The question isn’t whether this transformation will happen; it’s whether we’ll build it intentionally, with the right partners, or let it happen in fragmented, disconnected pieces.

I know which I’m choosing.

Read the full Future Health Index 2026 for the complete findings, and check out more thought leadership articles on these topics.