On May 7, 2025, a team of Mayo Clinic researchers has developed an artificial intelligence (AI) system that can detect surgical site infections (SSIs) with high accuracy from patient-submitted postoperative wound photos, potentially transforming how postoperative care is delivered.

Published in the Annals of Surgery, the study introduces an AI-based pipeline the researchers created that can automatically identify surgical incisions, assess image quality and flag signs of infection in photos submitted by patients through online portals. The system was trained on over 20,000 images from more than 6,000 patients across nine Mayo Clinic hospitals.

The AI system uses a two-stage model. First, it detects whether an image contains a surgical incision and then evaluates whether that incision shows signs of infection. The model, Vision Transformer, achieved a 94% accuracy in detecting incisions and an 81% area under the curve (AUC) in identifying infections.

The researchers are hopeful that this technology could help patients receive faster responses, reduce delays in diagnosing infections and support better care for those recovering from surgery at home. With further validation, it could function as a frontline screening tool that alerts clinicians to concerning incisions. This AI tool also paves the way for developing algorithms capable of detecting subtle signs of infection, potentially before they become visually apparent to the care team. This would allow for earlier treatment, decreased morbidity and reduced costs.

Importantly, the model demonstrated consistent performance across diverse groups, addressing concerns about algorithmic bias. While the results are promising, the team says that further validation is needed.