Review finds gaps in veterinary surgical simulation training
Bottom line
Version 1 — Brief
A new systematic review in Frontiers in Veterinary Science maps the current state of physical simulation models used in veterinary surgical training and finds a field that’s growing, but still uneven. Reviewing 78 studies published from July 2016 to July 2026, the authors found that most simulators were low-fidelity, most were developed in academic settings, and the largest share focused on canine surgery or laparoscopic box trainers. Training was concentrated heavily on sterilization procedures, while other species and surgical specialties were much less represented. The authors conclude that veterinary education still lacks more diverse, higher-fidelity models, along with stronger and more standardized validation methods. (frontiersin.org)
Why it matters: For veterinary professionals and educators, the review reinforces a broader shift already underway in training: simulation is becoming a core part of preparing learners before they work with live animals, aligning with the “never the first time on a live animal” approach described by veterinary education groups. But the paper also highlights a practical gap. If most available models are basic, canine-focused, and validated inconsistently, programs may struggle to benchmark competence across species, procedures, and learner levels. That matters not just for veterinary schools, but also for internships, residencies, and continuing education that increasingly rely on simulation to build confidence, standardize exposure, and support animal welfare. (aavmc.org)
What to watch: Expect follow-on work around validation standards, broader species coverage, and whether simulation programs can show clearer links to live-animal performance and day-one readiness. (frontiersin.org)
Version 2 — Full analysis
A systematic review published August 19, 2026, in Frontiers in Veterinary Science takes stock of physical simulation models for veterinary surgical training and finds that the field has expanded, but not evenly. Across 78 included studies, the authors report that most simulators were low-fidelity, most were built in academic settings, and the most common use cases were canine models and laparoscopic box trainers, especially for sterilization procedures. Their bottom line is clear: veterinary surgical simulation is now widely used, but it still needs broader species representation and more rigorous, standardized validation. (frontiersin.org)
That conclusion lands in the middle of a longer shift in veterinary education. Traditional apprenticeship-style training has well-known strengths, but more recent reviews note recurring limits, including uneven case exposure, dependence on clinical caseload, and welfare concerns tied to invasive training on live animals. In response, veterinary schools and accrediting bodies have increasingly embraced simulation and clinical skills labs as a way to standardize practice opportunities, reduce learner anxiety, and better prepare students before live-animal procedures. The AAVMC handbook describes a rapid increase in available clinical skills models and notes growing adoption of the “never the first time on a live animal” principle. (vetsci.org)
The new review followed PRISMA 2020 methods and searched PubMed and Web of Science for studies published between July 2016 and July 2026. From 1,409 records, the authors ultimately included 78 studies focused on physical simulators, excluding cadaver-only, ex vivo, and virtual reality-only approaches. Among the included studies, 55% of simulators were low fidelity. Canine models accounted for 42% of the total, and box trainers for 21%. High-fidelity simulators were more likely to be commercial, while lower-fidelity models were typically easier and less expensive for academic groups to build. (frontiersin.org)
The concentration of training targets is one of the review’s most notable findings. The literature was dominated by models for basic laparoscopic skills and sterilization procedures, particularly spay-related training. The authors argue that these models are useful for early technical development, but their narrow scope limits how well they reflect the range of real-world veterinary surgical practice. They also point to uneven validation standards across studies, making it harder for educators to compare tools or know which models are most effective for specific competencies. (frontiersin.org)
That concern isn’t new, and outside commentary supports it. A 2022 review in Veterinary Surgery similarly found room for growth in validity evidence, as well as in models for large animal surgery, resident training, and continuing education. More recent commentary in surgical education has also argued that realism alone doesn’t guarantee educational value, and that feedback, repeated practice, and curriculum integration may matter more than fidelity by itself. In other words, a more lifelike model is not automatically a better training tool if programs can’t show what it teaches, for whom, and under what conditions. (onlinelibrary.wiley.com)
There are also signs that institutions are already trying to close some of these gaps. Cornell says its SynDaver program has been fully integrated into the DVM curriculum and is being explored for continuing education use, with faculty and alumni framing it as a way to build confidence before practice. Washington State University similarly describes simulation-based education as central to preparing graduates, with models spanning species and skill types. Those examples suggest the market and the curriculum are moving ahead, even as the evidence base remains patchy and hard to compare across programs. (vet.cornell.edu)
Why it matters: For veterinary professionals, especially educators, surgeons, and training leaders, this review is less a critique of simulation than a call to mature it. Simulation is already embedded in many programs because it supports animal welfare, deliberate practice, and more consistent learner exposure. But if the field remains dominated by low-fidelity canine sterilization models with mixed validation approaches, veterinary education may fall short in preparing graduates and trainees for the diversity of species, procedures, and settings they’ll face. The practical implication is that schools, residency programs, and CE providers may need to think not just about buying or building simulators, but about selecting models tied to specific competencies, assessment plans, and clinical outcomes. (aavmc.org)
What to watch: The next phase will likely center on standardized validation frameworks, more species-diverse and specialty-specific models, and stronger studies linking simulator performance to live-animal outcomes, learner readiness, and workforce confidence. If those data emerge, they could shape curriculum design, accreditation expectations, and investment decisions across veterinary education. (frontiersin.org)