Medical-AI trial: identifying snake species with AI to aid snakebite treatment — a deep-learning system tested in emergency care (64-class classification) (NCT07658885)

In treating snakebite, quickly identifying which species of snake is crucial. Response differs greatly for a venomous versus a non-venomous snake, and for a venomous one, choosing the species-appropriate antivenom shapes the outcome. But the biting snake is often not seen, and many clinicians are unfamiliar with species identification, so accurate identification is not easy.

This study examines whether a system that identifies species from images with AI can help here.

Per the registry summary, this is a multicenter prospective observational study that develops and validates a deep-learning-based AI snake-species identification system and evaluates its clinical usefulness in real emergency snakebite care.

The main question is whether, in real clinical settings in Zhejiang Province, China, the system can accurately identify common indigenous biting species and improve the accuracy and diagnostic efficiency of species judgment for physicians at institutions of different levels.

It runs simultaneously at 10 institutions in Zhejiang, with primary outcomes of 64-class (64-species) classification and venomous/non-venomous binary discrimination. The size is 400 cases, conducted by invitation.

Globally, snakebite is counted among the neglected tropical diseases, and appropriate species identification and antivenom choice are directly tied to saving lives. Where specialists are scarce or in primary care, an AI that quickly identifies species from images and supports clinicians could help with appropriate antivenom use and transfer decisions.

Measuring across multiple centers how much it improves clinician accuracy and efficiency in real emergencies is practical. That said, this study evaluates identification performance and usefulness; it does not establish clinical use or replacement of specialist judgment.