AI Bronchoscopy Navigation Is Advancing Faster Than Its Clinical Evidence

AI navigation is becoming part of bronchoscopy, but hospitals still cannot tell how much of the clinical benefit comes from the AI itself.

Systems such as Johnson & Johnson’s MONARCH QUEST combine navigation software with robotic hardware, 3D imaging, and more computing power, making it difficult to credit better results to any one component.

That distinction matters to hospitals deciding whether the technology is worth the cost and changes to clinical workflows. Recent studies show that AI can improve airway mapping and examination coverage. What they still do not show is that AI navigation by itself improves lung-nodule biopsy yield when the robot, imaging system, and care team remain the same.

What current studies actually show

Johnson & Johnson introduced MONARCH QUEST after the FDA cleared an update to the MONARCH Platform in January 2025. J&J says the system’s NVIDIA RTX hardware increased real-time computing power by 260%, allowing it to run more complex navigation algorithms. In July 2026, the company reported the first clinical use of QUEST in the Middle East at Cleveland Clinic Abu Dhabi.

Those developments show that the technology is reaching clinical settings. They do not establish that AI improves biopsy results.

A 2026 study of AIBAN tested AI airway navigation more directly. The software identifies airway landmarks from bronchoscopy video, estimates the bronchoscope’s location, and recommends the next airway along a planned route. In simulation, it correctly localized the scope in 91.1% of validation frames and followed the correct anatomical path in 96.2% of test videos.

Clinical evidence is starting to emerge as well. A May 2026 multicenter randomized trial involving 204 patients tested AI assistance during conventional bronchoscopy. AI increased the proportion of bronchial anatomy examined from 82.70% to 92.09%, although average inspection time increased from 176.8 to 221.1 seconds.

The study measured examination coverage, not whether AI helped doctors reach peripheral lung nodules or improved biopsy yield. That is an important distinction. A 2025 systematic review had found that all four studies implementing AI airway navigation were conducted in simulation.

Clinical AI also has to produce results that are reproducible across different teams and environments before hospitals can rely on performance reported in a limited study.

Hospitals still cannot isolate AI’s effect on biopsy results

A 2026 Mayo Clinic study demonstrates why individual components need to be evaluated separately. Researchers compared 331 MONARCH procedures performed with and without mobile cone-beam CT. Diagnostic yield was nearly the same, at 70.9% and 71.7%, even though the imaging-supported procedures were shorter and involved substantially more radiation exposure.

That study evaluated imaging, not QUEST’s AI. But it demonstrates the problem with judging an entire bronchoscopy platform by a single outcome. Hardware, imaging, software, and workflow can affect different parts of the procedure without producing the same clinical benefit.

The software itself also requires monitoring after deployment. The FDA currently lists an open Class II recall covering certain MONARCH systems because restarting the software can reset patient-side positioning and potentially cause unexpected robotic-arm movement. The FDA attributes the problem to software design, not AI navigation, so the recall is not evidence that QUEST’s AI is unsafe.

FDA clearance also does not answer whether one component delivers better clinical outcomes than another. Hospitals evaluating newer robotic surgery systems face a similar need to separate regulatory clearance from evidence about performance in practice. The same caution applies when healthcare AI demonstrates a narrow technical capability before evidence supports broader clinical claims.

For MONARCH QUEST, the most useful next study would compare the same bronchoscopy platform with AI navigation enabled and disabled while keeping the catheter, imaging system, operator, and clinical workflow unchanged. It should then measure outcomes hospitals actually care about, including biopsy yield and procedure performance across multiple clinical sites.

Until that evidence exists, hospitals have good reason to recognize the progress in AI-assisted bronchoscopy without assuming that every improvement from an AI-enabled platform was produced by the AI.

Also read: A recent study found ChatGPT Health missed more than half of clinician-defined medical emergencies, providing another example of why clinical AI needs controlled evaluation before it guides care.