Why Hernia Surgery Still Has Room to Improve
An inguinal hernia happens when tissue — usually part of the intestine — pushes through a weak spot in the abdominal muscles in the groin area. More than 20 million hernia repairs are performed worldwide each year, making it one of the most frequently performed surgeries globally.
Despite how routine it seems, hernia surgery carries real risks: surgical site infections, recurrence of the hernia, blood clots in the legs, and — in some cases — the need to remove part of the intestine when the hernia becomes trapped. Identifying which patients are most at risk for these complications before surgery has traditionally relied on the surgeon's experience and general clinical assessment.
The Old Way of Assessing Risk
For most of surgical history, risk prediction was based on broad factors — age, weight, overall health — applied roughly the same way to every patient. Surgeons learned from experience which cases would be harder. Complications were largely expected, not predicted.
But here's the shift — machine learning models can now analyze dozens of variables at once, finding patterns that no single surgeon could hold in their head.
How AI Thinks About Surgery
Think of AI's approach to surgical risk like a supercharged checklist. Where a surgeon might check ten important boxes before an operation, a machine learning model checks hundreds — lab values, imaging features, prior health conditions, medication history — and assigns each patient a precise risk score.
Deep learning, a more advanced form of AI, goes further. It can process images and videos directly, identifying structures in surgical footage that might take a trainee years to learn to recognize reliably.
What This Review Covers
Researchers conducted a narrative review — a structured survey of the existing literature — on how machine learning and deep learning are currently being applied to inguinal hernia diagnosis and treatment. The review examined published studies on risk prediction models, imaging analysis, intraoperative navigation (guidance during surgery), and generative AI for patient consultation.
What the Evidence Shows So Far
On the risk prediction side, machine learning models have shown they can reliably predict four major complications: surgical site infections, surgical site occurrences (broader wound complications), the need for intestinal removal in trapped hernias, and blood clots in the legs after surgery. These are outcomes that currently surprise surgeons — predictive models could make them anticipated and manageable.
Deep learning has shown particular promise in the operating room itself. By analyzing laparoscopic video (footage from minimally invasive surgery using a tiny camera), AI systems can identify key anatomical landmarks in real time — the specific tissue layers and structures that surgeons must navigate carefully to avoid injury. This capability could guide surgeons during the operation and provide an objective training tool for residents learning the procedure.
That said, the field is still maturing.
Where Things Stand in Practice
Generative AI — the type of AI that can hold a conversation or answer questions — has also been explored for patient consultations before hernia surgery. Early findings suggest it can provide accurate general information, but its reliability and consistency are not yet good enough to be trusted without physician oversight.
AI tools for hernia surgery are not yet standard practice in most hospitals.
What This Means for Patients
If you are scheduled for hernia repair, AI-assisted tools are unlikely to be part of your immediate care unless you are at a specialized academic center. But this is changing. Asking your surgeon whether they use any AI-assisted risk assessment or imaging tools is a reasonable question. More broadly, this research signals that the surgery patients receive for even "routine" procedures will likely look different in the next decade.
Honest Limitations of This Review
Narrative reviews summarize existing literature but are not as rigorous as systematic reviews with formal statistical pooling. The studies reviewed used different patient populations, different AI models, and different outcome definitions — making direct comparisons difficult. Most AI tools described are still at the research or pilot stage and have not been validated in large, diverse patient populations across multiple hospitals.
The authors point to three priorities for the near future: integrating data from multiple sources (imaging, lab work, surgical video) into unified AI systems; developing real-time feedback tools that work reliably in live operating rooms; and building interdisciplinary teams where surgeons, data scientists, and engineers collaborate from the start. As the evidence base grows and regulatory pathways become clearer, AI-assisted hernia care is expected to move from research settings into routine surgical practice — improving outcomes for a procedure that already touches tens of millions of lives every year.
