Generative artificial intelligence provides accurate case selection in veterinary retrospective studies

Abstract

Objective: To evaluate the agreement of automation tools with expert evaluators in identifying cases meeting inclusion and exclusion criteria for retrospective veterinary studies. Methods: The review of medical records took place from December 16, 2024, through July 2, 2025. Medical records from 3 study populations (100 trauma dogs, 86 stent patients, and 100 cholecystectomy dogs) were assessed by 3 expert reviewers and were compared with automation tools, including AI applications (Gemini 2.5 Pro and NotebookLM) and a keyword search algorithm using Python, using standardized prompts for each study's criteria. Processing time and agreement with experts were compared. Results: Gemini 2.5 Pro most closely matched expert selections across all initial studies, with high case detection accuracy (99% to 100%) and fast processing times (90 to 390 seconds). NotebookLM was comparable for the stent dataset but less accurate for the others. Python tools had variable performance throughout the different studies. Conclusions: The study provides early evidence that AI is an effective tool for identifying cases using inclusion and exclusion criteria, which can accelerate the development of large retrospective studies. This approach has a multitude of other potential applications in both research and clinical practice. Clinical Relevance: Generative AI models, particularly Gemini 2.5 Pro, can enhance the speed and scalability of veterinary retrospective studies. While promising, AI-generated selections should be verified by investigators to ensure the appropriate application of inclusion criteria before final data enrollment.

Ähnliche Arbeiten