I've been in the OR for over a decade now, coordinating robotic procedures at a major surgical center. And if you've ever been in that room when the console surgeon says, "that lesion wasn't on the pre-op scan", you know the feeling. That split-second of re-evaluation. The call to extend the case. The conversation with the patient's family afterward that nobody wants to have.
Here's what I can tell you from my perspective: the problem isn't the robot. The robot is incredible. The problem is what we're not seeing before the first incision.
The Surface Problem: Missed Lesions and Surprise Findings
On the surface, the complaint is straightforward: "we're finding things intraoperatively that should have been caught earlier." A small nodule on the lung. A suspicious area on the pleural surface. Something that didn't light up on CT or wasn't clearly visible on standard endoscopy.
And it's not rare. I've personally logged 30+ cases in the last two years where an unexpected finding altered the surgical plan. In about a third of those, the finding was early-stage pathology that could have been managed less invasively if identified sooner. (Take this with a grain of salt — those are from my own case logs, not a formal study.)
But the surface complaint is misleading. The real issue isn't that lesions are hard to see. It's that we're relying on the wrong tools for detection.
The Deeper Problem: A Gap Between Imaging and Reality
This gets into imaging technology territory, which isn't exactly my expertise — I'm not a radiologist. What I can tell you from a surgical coordination perspective is this: there's a fundamental gap between preoperative imaging and intraoperative reality.
Standard CT and MRI give us incredible anatomical detail. But they're capturing structure, not function. They can show you a mass, but they can't tell you with high confidence whether that mass is benign or malignant at the cellular level. And they certainly can't show you what's right next to it that's below their resolution threshold.
That's where something like optical coherence tomography gets interesting. I'm not an OCT expert (seriously, I had to look up the physics), but I've seen what it can do in the context of pulmonary surgery. It's not replacing the pathologist — but it gives the surgeon real-time tissue characterization at the surface and subsurface level. A few hundred microns deep, but that's where early changes live.
Here's the part that surprised me: I assumed every major robotic system had something like this built in. They don't. The da Vinci system is remarkable for its dexterity and visualization, but the imaging modality is still primarily visible light. You're looking at the surface. The lesion might be under the surface.
(Note to self: verify whether the Ion platform integrates OCT in current configurations. I believe some investigational systems do, but I'm not up to date on commercial availability.)
The Real Cost: Not Just Medical, but Strategic
I knew a missed finding was bad for the patient. What took me longer to appreciate was the system impact. When a lesion is missed preoperatively and found during surgery:
- The case runs longer. Extended OR time means delayed subsequent cases, overtime for staff, and scheduling chaos.
- The patient needs additional procedures. A biopsy becomes a wedge resection. A wedge becomes a lobectomy. The recovery trajectory changes entirely.
- The center's data looks worse. Your "conversion to open" rate might be fine. But your "unplanned extended resection" rate? That's a metric that gets noticed by quality committees.
In my experience, the financial impact is significant. Based on our internal data from 200+ cases where unexpected findings occurred, the average added cost per case was roughly $4,000–$7,000 in OR time, anesthesia, and extended hospitalization. Not counting the patient experience impact.
What Actually Helps — and What Doesn't
I'm not going to pretend there's a simple fix. If you're looking for a "buy this one tool and your problems disappear" pitch, this isn't it.
What doesn't work: Relying exclusively on preoperative CT. It's the standard of care, but it's not enough for small surface lesions in the lung or pleural space. I've seen centers invest in higher-resolution CT scanners and still miss things. The problem isn't resolution — it's that the tissue doesn't have enough contrast with surrounding structures at that stage.
What helps: Incorporating intraoperative imaging that can characterize tissue during the procedure. This is where OCT and similar technologies fit. They're not replacing the pathologist (big distinction — I'm not a pathologist, so I can't speak to what they need). They're giving the surgeon more information at the point of decision.
Small doesn't mean unimportant — it means potential. A 5mm lesion that's caught early and resected with a wedge might never become a problem. A 5mm lesion that's missed and returns as a 3cm mass 18 months later? That's a completely different conversation.
Bottom line: The best robotic system in the world is only as good as the imaging you pair it with. If you're investing six figures in surgical robotics and still relying on the same pre-op imaging workflow from a decade ago, you're leaving outcomes on the table. I'd argue that's worth a conversation with your surgical team about what's available now — and what's coming.