Technology · September 2026
Some of this research is genuinely promising. Very little of it has reached the exam room. Here is where things actually stand, and why the best predictor we have is still a week-long trial.
Artificial intelligence is being studied across almost every part of medicine, and pain management is no exception. Some of the research is genuinely promising. Very little of it has reached the exam room. Here is an honest picture of where things stand.
Interventional pain treatments work well for some patients and not for others, and predicting which is which remains difficult. Spinal cord stimulation is the clearest example: it can be life-changing, but reported success rates vary widely, and roughly a third of patients who receive an implant do not get the outcome they hoped for.
That is the gap researchers are aiming at. If a model could identify in advance who is likely to benefit, patients would be spared procedures unlikely to help them.
A 2026 review of this literature found around two dozen studies applying machine learning to outcome prediction in interventional pain management, most of them looking at epidural injections, radiofrequency procedures, vertebral augmentation, and spinal cord stimulation.
One line of work combines quantitative features extracted from spinal imaging — patterns in an MRI that are not obvious to the eye — with clinical information, to predict who responds to spinal cord stimulation. Other work uses physiological signals to detect pain more objectively, rather than relying only on a patient's rating.
Reviews of this field consistently identify the same limitations: models are often developed on small datasets, tested in narrow populations, and difficult to interpret — including for the clinicians expected to act on them. Bias in training data is a documented concern, and most tools have not been validated prospectively in real clinics.
None of that means the research is not worthwhile. It means a model's output is not yet a reason to have, or not have, a procedure.
Some AI-adjacent tools are already in everyday medical use and rarely described as AI at all — software that assists with imaging review, documentation tools that draft visit notes, scheduling and billing systems. These change how a clinic runs more than how a treatment decision is made.
The distinction worth holding onto: tools that help a clinician work are different from tools that decide what you need.
We follow this work closely and expect parts of it to become useful. We are not going to let an algorithm decide who gets a procedure.
There is also a certain irony in the spinal cord stimulation research: while models are being developed to predict who will respond, the treatment already includes the most reliable predictor available — a trial period where you use the device for about a week before deciding on an implant. Real-world evidence from your own body, gathered over seven days, is difficult for any model to beat.
Where AI eventually helps, we expect it to be in the same place good medicine always works: narrowing down the possibilities so the conversation with your physician starts from better information.
Two things are worth watching as this technology spreads. First, be skeptical of any clinic or product claiming an AI system can diagnose the cause of your pain from a questionnaire or a scan alone — the evidence does not support that. Second, ask where your data goes. Health information fed into consumer AI tools is not necessarily protected the way your medical record is.
This article is general information about developments in pain medicine. It is not medical advice, not a recommendation for or against any product or treatment, and it does not describe services offered at CPMC unless stated. Whether any of this applies to you depends on your diagnosis and history — that is a conversation for your appointment.