A Comprehensive Review of Artificial Intelligence and Hybrid Intelligent Systems in Veterinary Orthopedic Surgery: Current Applications, Challenges, and Future Perspectives

Abstract

Rapid advances in artificial intelligence (AI) and hybrid systems in recent years have opened new frontiers in precision medicine. In veterinary orthopedic surgery, which faces challenges such as anatomical variability across species, the complexity of joint reconstruction, and the need for high precision in implant placement, the application of these technologies can be transformative.This comprehensive review article aimed to investigate the applications of artificial intelligence (AI) and hybrid systems in veterinary orthopedic surgery between January 2020 and December 2025. A systematic search was conducted in reputable databases, including PubMed, Scopus, Web of Science, and Google Scholar, using keywords related to artificial intelligence, machine learning, neural networks, expert systems, orthopedic surgery, and veterinary medicine. From a total of 428 initial articles, after removing duplicates and screening the title and abstract, 21 full articles that met the inclusion criteria (original studies, systematic reviews, and case reports with a minimum follow-up of 6 months) were included in the final analysis. The findings showed that AI was mainly used in three main areas: 1) automatic detection and classification of fractures and dislocations from radiographs and CT scans with an accuracy of over 89%, 2) preoperative planning and simulation of implant placement in hip and knee surgeries, and 3) prediction of postoperative complications such as nonunion, infection, and implant failure based on demographic and clinical data. Hybrid systems that combined machine learning with rule-based veterinary expert systems showed the greatest success in reducing surgeon error and optimizing the bone-cutting path. However, significant challenges remain, including the lack of large, standardized veterinary datasets, explainable AI algorithms for clinical decision-making, and randomized controlled trials. It is concluded that AI and hybrid systems have great potential to transform veterinary orthopedic surgery, but widespread adoption in clinical practice requires further clinical validation and the development of user-friendly software.