A robotic platform, an energy device, an anesthesia monitor, a video system, and an AI tool are all used in the same operation. Each one works as specified. The patient still has a serious complication, and no one can say why.
I call this the unexplainable case. It is not a hypothetical. Every device keeps its own record, on its own clock, owned by a different company or department. Video and audio capture in the operating room has made real progress, but no one yet brings together what the AI showed, which software version was running, what the team did, and what happened to the patient on a single timeline. When a case fails, each manufacturer can show that its own product worked. The question of what happened to the patient goes unanswered.
This will happen more often. Hospitals now run more than one robotic platform, software changes monthly, and AI is becoming part of decisions and actions during surgery. Product-level oversight remains essential, but it was not designed to explain how a connected system performed.
In a new paper in the Journal of Robotic Surgery, I propose System Performance Readiness. A health system and its technology partners should be able to reconstruct a connected surgical episode and learn from it across products and sites. A qualified reviewer should be able to answer five questions for any case, whatever the manufacturer. What was used? What information was available? What happened? When and where did it happen? What followed?
This does not require a new regulatory layer or a single data platform. It requires agreement on a minimum record, clear responsibility for each part of it, and a pilot to show whether it improves review and at what cost. Surgery is the right place to start, because a procedure is a defined episode with many technologies in one room.
Clinicians need to trust that AI tools work, that mistakes will be caught early, and that we will learn from them. Patients and families need to trust that they are getting the benefit of current tools, and that when a case fails, it will be understood so the next patient is spared. A learning health system everyone can trust is the goal, and these are first steps toward it.
Ashar BS. When every product works but the case fails, the need for system performance readiness in connected surgery. J Robot Surg. 2026. doi:10.1007/s11701-026-03993-3
Binita S. Ashar, MD, MBA, FACS, MAMSE, is the founder of Clarity Surgical Advisors and a former FDA Office Director for Surgical and Infection Control Devices. She consults for medical device manufacturers and is a regulatory venture partner at Puma Ventures. Views are her own.
